/**
 * Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
 * SPDX-License-Identifier: Apache-2.0.
 */

#pragma once
#include <aws/alexaforbusiness/AlexaForBusiness_EXPORTS.h>
#include <aws/core/utils/memory/stl/AWSString.h>
#include <utility>

namespace Aws
{
namespace Utils
{
namespace Json
{
  class JsonValue;
  class JsonView;
} // namespace Json
} // namespace Utils
namespace AlexaForBusiness
{
namespace Model
{

  /**
   *
The summary of a gateway group.

See Also:
   AWS
   * API Reference

   */
  class AWS_ALEXAFORBUSINESS_API GatewayGroupSummary
  {
  public:
    GatewayGroupSummary();
    GatewayGroupSummary(Aws::Utils::Json::JsonView jsonValue);
    GatewayGroupSummary& operator=(Aws::Utils::Json::JsonView jsonValue);
    Aws::Utils::Json::JsonValue Jsonize() const;

    /**
     *
The ARN of the gateway group.

     */
    inline const Aws::String& GetArn() const{ return m_arn; }

    /**
     *
The ARN of the gateway group.

     */
    inline bool ArnHasBeenSet() const { return m_arnHasBeenSet; }

    /**
     *
The ARN of the gateway group.

     */
    inline void SetArn(const Aws::String& value) { m_arnHasBeenSet = true; m_arn = value; }

    /**
     *
The ARN of the gateway group.

     */
    inline void SetArn(Aws::String&& value) { m_arnHasBeenSet = true; m_arn = std::move(value); }

    /**
     *
The ARN of the gateway group.

     */
    inline void SetArn(const char* value) { m_arnHasBeenSet = true; m_arn.assign(value); }

    /**
     *
The ARN of the gateway group.

     */
    inline GatewayGroupSummary& WithArn(const Aws::String& value) { SetArn(value); return *this;}

    /**
     *
The ARN of the gateway group.

     */
    inline GatewayGroupSummary& WithArn(Aws::String&& value) { SetArn(std::move(value)); return *this;}

    /**
     *
The ARN of the gateway group.

     */
    inline GatewayGroupSummary& WithArn(const char* value) { SetArn(value); return *this;}

    /**
     *
The name of the gateway group.

     */
    inline const Aws::String& GetName() const{ return m_name; }

    /**
     *
The name of the gateway group.

     */
    inline bool NameHasBeenSet() const { return m_nameHasBeenSet; }

    /**
     *
The name of the gateway group.

     */
    inline void SetName(const Aws::String& value) { m_nameHasBeenSet = true; m_name = value; }

    /**
     *
The name of the gateway group.

     */
    inline void SetName(Aws::String&& value) { m_nameHasBeenSet = true; m_name = std::move(value); }

    /**
     *
The name of the gateway group.

     */
    inline void SetName(const char* value) { m_nameHasBeenSet = true; m_name.assign(value); }

    /**
     *
The name of the gateway group.

     */
    inline GatewayGroupSummary& WithName(const Aws::String& value) { SetName(value); return *this;}

    /**
     *
The name of the gateway group.

     */
    inline GatewayGroupSummary& WithName(Aws::String&& value) { SetName(std::move(value)); return *this;}

    /**
     *
The name of the gateway group.

     */
    inline GatewayGroupSummary& WithName(const char* value) { SetName(value); return *this;}

    /**
     *
The description of the gateway group.

     */
    inline const Aws::String& GetDescription() const{ return m_description; }

    /**
     *
The description of the gateway group.

     */
    inline bool DescriptionHasBeenSet() const { return m_descriptionHasBeenSet; }

    /**
     *
The description of the gateway group.

     */
    inline void SetDescription(const Aws::String& value) { m_descriptionHasBeenSet = true; m_description = value; }

    /**
     *
The description of the gateway group.

     */
    inline void SetDescription(Aws::String&& value) { m_descriptionHasBeenSet = true; m_description = std::move(value); }

    /**
     *
The description of the gateway group.

     */
    inline void SetDescription(const char* value) { m_descriptionHasBeenSet = true; m_description.assign(value); }

    /**
     *
The description of the gateway group.

     */
    inline GatewayGroupSummary& WithDescription(const Aws::String& value) { SetDescription(value); return *this;}

    /**
     *
The description of the gateway group.

     */
    inline GatewayGroupSummary& WithDescription(Aws::String&& value) { SetDescription(std::move(value)); return *this;}

    /**
     *
The description of the gateway group.

     */
    inline GatewayGroupSummary& WithDescription(const char* value) { SetDescription(value); return *this;}

  private:

    Aws::String m_arn;
    bool m_arnHasBeenSet;

    Aws::String m_name;
    bool m_nameHasBeenSet;

    Aws::String m_description;
    bool m_descriptionHasBeenSet;
  };

} // namespace Model
} // namespace AlexaForBusiness
} // namespace Aws


Enough's enough: pass sensible gun control legislation now.

The shooting rampage in a movie theater in Aurora, Colorado that resulted in loss of life, injuries and trauma is the latest in a series of mass shootings committed by people who have one thing in common: much-too-easy access to automatic and semiautomatic weapons.

Gun control laws in the USA are ludicrously lax, and yet almost the day after the shooting, some members of Congress were announcing that there would be no changes to gun control laws. It's a slap in the face to the victims of this crime and others (Rep. Gabrielle Giffords, Virginia Tech students, Columbine students, the list goes on and on and on and on. Sadly.). Why? Because lawmakers are petrified of the NRA and repercussions that might come from even attempting to suggest that this country could maybe, just maybe, stand to ban weapons like AK-47s and other guns that let someone shoot hundreds of bullets per minute.

It's time to say enough's enough. Someone in the nation's highest legislative body needs to step up and be a leader. How many more people have to die before this country has sensible gun control laws? Could your Representative or Senators be the catalyst for change?

Share this petition

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Protester Nadine Wolf demonstrates against the proposed Stop Online Piracy Act (SOPA) and Protect IP Act (PIPA) outside the offices of US Senators Charles Schumer and Kirsten Gillibrand in New York City. Credit:Getty Images The bill, 18 months in the making, will establish international protocols on the issues surrounding cybercrime, allow the US government to name and shame lax governments, and establish a framework for economic sanctions on uncooperative governments. "Right now, there is no protocol," Gillibrand said. "There is no way to call the Russian government and say 'You've got five guys sitting in a room over there who keep attacking our stock exchange – what are you going to do about that?' "We want to give incentives for countries that need our help, for countries that don't have a way to prosecute cyber crime, or to investigate these cases." Speaking before an audience of law enforcement officials, industry insiders and academics in New York City recently, Gillibrand said a personal fear was for a talented hacker to sell his skills to terrorist groups.

US Senator Kirsten Gillibrand wants cybercrime hotspots sanctioned. Credit:Abdul Smith "If countries that are players in this issue don't participate or don't actually try to enforce laws against cyber criminals we could have sanctions in terms of multilateral banking finance or we could have sanctions that would limit or suspend preferential trade programs. We could also suspend, restrict, or withdraw foreign assistance." Unsurprisingly, law enforcement officials have welcomed the bill. FBI Special Agent Mary Galagin, head of the agency's Cyber/Special Operations Division, believes technology is in danger of outrunning the legal framework applying to her office. "If we continue to increase the technology in the United States but legislation does not increase, then law enforcement will get to a point where we will go dark," Galagin said. "We cannot get the information we need to stop a terrorist attack, espionage, criminal attacks. "You can do a lot more harm to our country right now with a computer than you can with a lot of other weapons. It is kind of like the Pirates of the Caribbean out there. There are pirate ships. There is lots of gold. People are going for it."

While terrorism and espionage is one focus, companies including Facebook and MasterCard support the bill in the face of an added twist – their interest in protecting private customer data from compromise. Chris Sonderby, Facebook's Associate General Counsel who oversees the company's global law enforcement relations, believes it's in the interest of private companies to partner with law enforcement agencies to protect customers as much as themselves. "People demand a level of security," said Sonderby, whose company holds data on over 800 million active users. "Those companies that don't protect information are those that people are going to be uncomfortable sharing with or they're not going to use. "There are powerful market incentives to make sure that companies you entrust information to have taken adequate steps to protect that data."


+++
title = "Talk:Trabb Pardo–Knuth algorithm"
description = ""
date = 2016-11-15T23:47:20Z
aliases = []
[extra]
id = 11756
[taxonomies]
categories = []
tags = []
+++

==Notes==
I don't have access to the WP reference works and just went from the WP article and some of the examples on its external references. I hope the numbered parts to the requirements will ensure that the task can be completed by most. --[[User:Paddy3118|Paddy3118]] 20:21, 22 May 2012 (UTC)
: Hmm, it's an awkward one as there isn't a copy in the academic library here (the library only has partial coverage of US-published-only works). –[[User:Dkf|Donal Fellows]] 08:29, 23 May 2012 (UTC)

== Python code ==

I'm not entirely convinced by the Python implementations, especially given that the WP samples seem to be very focused on accepting exactly 11 numbers. While yes, the greater generality of the Python code is what I'd go for as well ''normally'', this task is much more exact than that I believe. –[[User:Dkf|Donal Fellows]] 08:23, 23 May 2012 (UTC)

==Goto D==
OK Bearophile, I'll bite. ''Why use the goto instead of some kind of do..until got_eleven_numbers structured programming approach in the D language example?''
:-) --[[User:Paddy3118|Paddy3118]] 05:01, 28 May 2012 (UTC)

==ALGOL 60 and  the prompt==
The ALGOL 60 example does not do the following part of the task:
* '''Print a prompt''' before accepting eleven, textual, numeric inputs.
If this is to make it more like the original case rather than what I could glean from wikipedia then please remove the banner and add a small explanation ; if it is not then please update the example. --[[User:Paddy3118|Paddy3118]] 05:34, 4 June 2012 (UTC)

: Yeah, even the ALGOL example was only there to introduce the algorithm to the audiences of the mid-'70s, before delving 25 years further back in time. The systems that are the subject of the report mostly predated notions of keyboard interaction or textual I/O. --[[User:Saccade|Saccade]] 05:59, 5 June 2012 (UTC)

:: Thanks Saccade for the update to the Algol entry. (Now I wonder what the original Algol looked like though :-)
--[[User:Paddy3118|Paddy3118]] 06:32, 5 June 2012 (UTC)

==Function made invisible by under-tested cosmetic edits at 18:57, 15 April 2016==

Under-tested cosmetic edits made to the task page at 18:57, 15 April 2016, including the injection of spaces around expressions in <math> tags, have left the function to be used completely invisible to all browsers which display the graphic file version of formulae rather than processing the MathML (this is, in fact, the majority of browsers). The MediaWiki processor does not currently expect such spaces, and generates syntactically ill-formed HTML if they are introduced. Other aspects of these cosmetic edits may further compound the problem. [[User:Hout|Hout]] ([[User talk:Hout|talk]]) 21:54, 22 September 2016 (UTC)

: Visibility now restored [[User:Hout|Hout]] ([[User talk:Hout|talk]]) 23:47, 15 November 2016 (UTC)


Arun Jaitley Says Cryptocurrency Is Illegal

India has had an interesting journey with bitcoin and other cryptocurrencies in the last year or two. As one of the world’s most populous countries and most influential economies, India is in a position to drastically alter the future of cryptocurrency. We’ve seen, for instance, that when some Far East countries made it easier for people to trade in cryptocurrency, bitcoin prices surged. One article put it about as clearly as it could be said: Japan’s legalization of bitcoin led to a rapid surge in mainstream adoption (and by extension, increased demand and higher prices).

India could have a similar effect if the government or financial institutions were to offer a full-throated endorsement of cryptocurrency. As of now, however, it appears they’re doing just the opposite. The latest word comes from Finance Minister Arun Jaitley, who has said that bitcoin and other cryptocurrencies are not legal tender, and thus that trading in them is actually considered illegal. While Jaitley suggested that people trading cryptocurrencies are doing so “at their own risk,” his overall statement might not be as dramatic as pieces of it sound. Asked about the potential for regulation, Jaitley essentially said that the government is continuing to examine the matter and will form a more complete position in time.

Nevertheless, these are significant words from Jaitley, with potential to suppress the bitcoin market in India and perhaps impact global prices. India’s population is believed to account for just over 10 percent of cryptocurrency trading in the entire world, and if even a portion of that market is dissuaded, it could make for a significant blow to prices.

As to how Indian citizens with an interest in cryptocurrency should react to the statements from Jaitley, it’s difficult to say. Bitcoin regulation in general is a somewhat hazy concept, as there is no such thing as universal legislation surrounding cryptocurrencies. Reading about the subject you can basically learn that individuals must still comply with local financial law where bitcoin trading is concerned. Here, however, Jaitley has been somewhat unclear. While he made it clear that he doesn’t consider cryptocurrency to be legal tender, he did not expressly say that trading is a punishable offense – he merely made the vague remark about people doing it at their own risk.

If anything, this recent news appears to signal that a firmer Indian position on cryptocurrency could be on the horizon. It’s clear that the financial powers in the government are staunchly opposed to the continued spread of cryptocurrency. It is less clear, however, what if anything they plan to do about it.


Motor

(İngilizce - Türkçe)

motor

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Quality assurance in radiation therapy: European experience--present and future clinical efforts.
A high standard of radiotherapeutic practice must be sought in all phases of management of a patient with malignant disease. Radiation therapy must be appropriately chosen and integrated with surgery, cytotoxic chemotherapy and all other modes of treatment. The most suitable technique with a dose, fractionation and time regime must be devised and executed with technical and personal care. Follow-up to truly assess tumor control and morbidity is essential so as to guide the management of future patients. To achieve this in Europe great reliance is placed upon the training and qualification of the therapist and staff. In England a Fellowship of the Royal College of Radiologists in radiation therapy is regarded as essential for appointment as a permanent consultant within the Health Service. A high examination standard is set with pass rates ranging from 10 to 30%. High standards are applied to the professional qualifications for radiation physicists, nurses and technical staff. Most radiotherapy centers and departments have been visited by the Royal College in order to determine if the standard of staffing and equipment justifies inclusion in the list of training departments. There is, unfortunately, no control of continuing education of the consultant once appointed. In the countries belonging to the European economic union, a new Diploma in Radiation Therapy has been established to be a standard for consultant practice through all the countries included. In a few multi-center trials in the United Kingdom members of the central organization visit centers to look at the case notes and compare them with the material submitted from the trial center. This effort at monitoring clinical trials is only just beginning in the United Kingdom. The European Organization for Research and Treatment of Cancer has recently initiated a quality control study in some of the centers included in the Radiotherapy Group. A preliminary report has just appeared on the results of the clinical and dosimetric studies in 8 centers placed in 5 European countries. This first European effort appears to have been successful and further details will become available. These initiatives through research may well lead to wider studies of quality assurance in radiation therapy within Europe.


Review
======

Introduction
------------

Hepatitis C virus (HCV) chronically infects an estimated 170 million people worldwide \[[@B1]\]. HCV infection is one of the major causes of end-stage liver disease and hepatocellular carcinoma (HCC) worldwide \[[@B2]-[@B4]\]. Approximately 30% of patients who develop acute hepatitis C recover spontaneously, signaled by improved symptoms, normalized liver-related chemistries, loss of HCV RNA from serum, and the development of HCV antibody \[[@B5]-[@B7]\]. In chronic hepatitis C, the progression of liver fibrosis is slow, but steady. It has been reported that the progression rate of liver fibrosis is 0.10-0.13 U/year in untreated patients \[[@B8]\]. Progression of chronic HCV infection is not linear in time, probably because many cofactors are involved in changing the rate of development of fibrosis, cirrhosis, and HCC \[[@B6]\]. Cirrhosis rates become significant after 20 years of HCV infection. About 20-30% of patients could develop a progressive liver disease leading to cirrhosis and HCC \[[@B5],[@B7]\]. HCC develops at about 1-7% per year \[[@B5],[@B7]\]. It has been demonstrated that subjects who achieve sustained virological response (SVR) have a clear advantage at histological and clinical levels compared to those who do not achieve SVR \[[@B8]-[@B12]\]. The present standard for the judgment of SVR is undetectability of serum HCV RNA at 24 weeks post-treatment.

Preventive measures against HCV, including vaccine development, are now in progress \[[@B13]\]. But the standard of care (SOC), peginterferon and ribavirin therapy, and new standard of care (NSOC), combination protease inhibitors such as telaprevir or boceprevir with peginterferon plus ribavirin therapy, have been approved for the eradication of HCV in US, Europe, and Japan \[[@B14]-[@B18]\]. Even with these advances in antiviral therapies against HCV, SVR rates were \~70% in HCV genotype-1 treated with NSOC and \~80% in HCV genotype-2/3 treated with SOC. Rash also occurs in 56% of patients treated with NSOC, compared to 34% of patients treated with SOC alone. Other adverse events were still present \[[@B19]\], although even interferon is also associated with severe adverse events \[[@B20]\]. When we treat patients infected with HCV in daily clinical practice, it seems important to be aware of the potential treatments of HCV in the near future, as the development of new drugs is always ongoing.

HCV belongs to the flaviviridae family, and HCV genome is a positive-strand \~9.6-kb RNA. HCV has a 5^′^ untranslated region (5^′^UTR), a long open reading frame, and a 3^′^UTR. An internal ribosomal entry site (IRES), containing the 5^′^UTR and part of the core coding region, forms a stem-loop structure and supports translation initiation of HCV genome in a cap-independent manner \[[@B21],[@B22]\]. HCV genome encodes a single precursor polyprotein that is processed by host signal peptidases and HCV proteases, resulting in structural (core, envelopes E1 and E2, and p7) and nonstructural (NS2, NS3, NS4A, NS4B, NS5A and NS5B) proteins. Direct-acting antivirals (DAAs) against HCV are classified into several categories: 1) HCV NS3/4A protease inhibitors, 2) HCV NS5B polymerase inhibitors, 3) HCV NS5A inhibitors, and others. In the near future, interferon-sparing regimens and treatment with all-oral DAAs will play major roles in treating HCV-infected patients (Figure [1](#F1){ref-type="fig"}).

![**Treatments for chronic hepatitis C in the present and future.** PegIFN, peginterferon; RBV, ribavirin.](2001-1326-2-9-1){#F1}

Standard of care (SOC) treatment for HCV infection
--------------------------------------------------

Interferon, combination interferon plus ribavirin, and peginterferon plus ribavirin increased SVR rate from \~5% to \~40-80%, depending on the HCV genotypes \[[@B18],[@B23]\]. Peginterferon plus ribavirin treatment for 48 weeks, the SOC treatment for HCV genotype 1-infected patients, leads to only \~50% SVR in those patients with high viral loads, who were mostly null-responders or relapsers \[[@B23]-[@B27]\]. On the other hand, peginterferon plus ribavirin treatment for 24 weeks, the SOC treatment for HCV genotype 2-infected patients, leads to \~80% SVR in those patients (Table [1](#T1){ref-type="table"}). Race has been shown to be a factor in the response to therapy for HCV infection \[[@B25],[@B27]\]. A higher homocysteine level is also one of factors predicting a nonresponse to treatment \[[@B28]\]. Because the favorable interleukin 28B (IL28B) associated-single nucleotide polymorphisms (SNPs), leading to better response, exist at substantially greater frequency in European than African populations, they also explain approximately half of the difference in response rates between African-Americans and patients of European ancestry \[[@B29]\]. IL28B SNPs help to improve the treatment outcomes in HCV-patients treated with SOC \[[@B29]-[@B34]\]. In future interferon-included regimens, IL28B SNPs may also provide useful information about the treatment response even before treatment.

######

Standard of care treatment and sustained virological response rates for chronic hepatitis C patients

  **References**   **G**      **Number of patients**                 **Naïve or re-treatment**   **Formula of therapy**                 **Duration of treatment (weeks)**   **SVR rates (%)**   **Notes**
  ---------------- ---------- -------------------------------------- --------------------------- -------------------------------------- ----------------------------------- ------------------- -----------
  \[[@B24]\]       G1         298                                                                Peginterferon alfa-2a plus ribavirin   48                                  46
  285                         Interferon alfa-2b plus ribavirin      48                          36
  145                         Peginterferon alfa-2a plus placebo     48                          21
  \[[@B24]\]       G2/G3      140                                                                Peginterferon alfa-2a plus ribavirin   48                                  76
  145                         Interferon alfa-2b plus ribavirin      48                          61
  69                          Peginterferon alfa-2a plus placebo     48                          45
  \[[@B24]\]       G4         13                                                                 Peginterferon alfa-2a plus ribavirin   48                                  77
  11                          Interferon alfa-2b plus ribavirin      48                          36
  9                           Peginterferon alfa-2a plus placebo     48                          22
  \[[@B25]\]       G1 (98%)   100                                                                Peginterferon alfa-2b plus placebo     48                                  19                  Blacks
  100                         Peginterferon alfa-2a plus ribavirin   48                          52                                     Non-Hispanic Whites
  \[[@B26]\]       G2/3       732                                                                Peginterferon alfa-2a plus ribavirin   16                                  65
  731                         Peginterferon alfa-2a plus ribavirin   24                          76
  \[[@B27]\]       G1         269                                    naive                       Peginterferon alfa-2a plus ribavirin   48                                  34                  Latino
  300              naive      Peginterferon alfa-2a plus ribavirin   48                          49                                     Non-Latino

G, genotype; SVR, sustained virological response.

New standard of care (NSOC) treatment for HCV infection
-------------------------------------------------------

In 2011, telaprevir and boceprevir were the first approved DAAs against HCV. The triple combination therapy of telaprevir or boceprevir plus ribavirin and peginterferon-alfa is the NSOC treatment for chronic HCV genotype 1-infected patients \[[@B35]-[@B42]\]. Telaprevir (VX-950) is a potent, selective inhibitor of NS3/4A protease, which is essential for HCV replication \[[@B43]\]. SVR rates among treatment-naïve patients were \~70% in telaprevir-included regimens \[[@B35],[@B36],[@B38],[@B40]\]. The SVR rates among patients with no previous response were 30\~40% and those among patients with a previous relapse were 70\~75%, both in telaprevir-included regimens \[[@B37],[@B39]\]. SVR rates were significantly higher in telaprevir-included regimens than in SOC among patients who had a previous relapse (83-88% vs. 24%), a partial response (54-59% vs. 15%) and non- response (29-33% vs. 5%) \[[@B39],[@B44]\]. Thus, SVR rates were higher among patients who had previously had relapses than among nonresponders. Response-guided therapy is also useful in the telaprevir-included regimen (Table [2](#T2){ref-type="table"}) \[[@B40]\]. Boceprevir (SCH 503034) is a potent ketoamide inhibitor of HCV NS3 serine protease \[[@B45]\]. The addition of boceprevir to SOC results in higher SVR rates in both treatment-naïve and re-treated patients infected with HCV genotype 1 (Table [3](#T3){ref-type="table"}) \[[@B41],[@B42]\]. SVR rates were significantly higher in boceprevir-included regimens than in SOC among patients who had a prior relapse (69-75% vs. 29%) or a prior nonresponse (40-52% vs. 7%) \[[@B42]\]. Viewed from the clinical data of first-generation protease inhibitors, telaprevir and boceprevir, these drugs showed potent inhibition of HCV, although they occasionally led to severe adverse events \[[@B46],[@B47]\].

######

New standard of care treatment with telaprevir for chronic hepatitis C patients

  **References**   **G**          **Number of patients**   **Naïve or re-treatment**   **Formula of therapy, and duration of treatment (weeks)**   **SVR rates (%)**   **Notes**
  ---------------- -------------- ------------------------ --------------------------- ----------------------------------------------------------- ------------------- ------------------
  \[[@B35]\]       G1             79                       naive                       T12PR24                                                     61                  PROVE1 Study
  79               naive          T12PR48                  67
  17               naive          T12PR12                  35
  75               naive          PR48                     41
  \[[@B36]\]       G1             81                       naive                       T12PR24                                                     69                  PROVE2 Study
  82               naive          T12PR12                  60
  78               naive          T12P12                   36
  42               naive          PR48                     46
  \[[@B37]\]       G1             115                      re-treatment                T12PR24                                                     51                  PROVE3 Study
  113              re-treatment   T12PR48                  53
  111              re-treatment   T24P24                   24
  114              re-treatment   PR48                     14
  \[[@B38]\]       G1             363                      naive                       T12PR                                                       73                  ADVANCE Study
                                  364                      naive                       T8PR                                                        67
                                  49                       naïve                       PR48                                                        44
  \[[@B39]\]       G1             145                      re-treatment                T12PR48                                                     93                  REALIZE Study
                                  141                      re-treatment                Lead-in T12 PR48                                            89
                                  68                       re-treatment                PR48                                                        24
  \[[@B40]\]       G1             162                      naïve                       T12PR24 (RGT)                                               92                  ILLUMINATE Study
                                  160                      naïve                       T12PR48 (RGT)                                               88
                                  118                      naïve                       T12PR48 (non-RGT)                                           64
                                  100                      naïve                       Discontinued treatment before wk 20                         23

######

New standard of care treatment with boceprevir for chronic hepatitis C patients

  **References**   **G**          **Number of patients**   **Naïve or re-treatment**   **Formula of therapy, and duration of treatment (weeks)**   **SVR rates (%)**   **Notes**
  ---------------- -------------- ------------------------ --------------------------- ----------------------------------------------------------- ------------------- ---------------
  \[[@B41]\]       G1             363                      naive                       PR48                                                        38                  SPRINT-2
  368              naive          PR4+BocPR24+PR22         63
  366              naive          PR4+BocPR44              66
  \[[@B42]\]       G1             80                       re-treatment                PR48                                                        21                  HCV RESPOND-2
  162              re-treatment   PR4+BocPR24+PR22         59
  161              re-treatment   PR4+BocPR44              66

PR48 group received peginterferon alfa-2a and ribavirin for 48 weeks; T12PR12, T12PR24 or T12 PR48 group received telaprevir for 12 weeks and peginterferon alfa-2a and ribavirin for 12, 24 or 48 weeks, respectively; T12P12 group received telaprevir and ribavirin for 12 weeks; T24P24 group received telaprevir and peginterferon alfa-2a for 24 weeks; RGT, response-guided therapy; Boc, boceprevir.

Second-generation HCV NS3/4A inhibitors
---------------------------------------

Simeprevir (TMC435) is an investigational HCV NS3/4A protease inhibitor administered orally once daily, and it is currently in phase III clinical development \[[@B48]\]. It differs from the first generation protease inhibitors in terms of its once-daily administration. Superior efficacies of simeprevir and peginterferon plus ribavirin were observed compared to those of peginterferon plus ribavirin alone in treatment-naive \[[@B49]\] and previously treated patients (Table [4](#T4){ref-type="table"}) \[[@B50]\]. Although anemia and rash, respectively, were notable adverse events in boceprevir and telaprevir, those of simeprevir and peginterferon plus ribavirin did not differ from those of peginterferon plus ribavirin alone in phase II studies \[[@B48]-[@B50]\].

######

New standard of care treatment with other drugs (second-generation DAAs) chronic hepatitis C patients

  **References**      **G**                                     **Number of patients**   **Naïve or re-treatment**   **Formula of therapy, and duration of treatment (weeks)**   **SVR rates (%)**   **Notes**
  ------------------- ----------------------------------------- ------------------------ --------------------------- ----------------------------------------------------------- ------------------- -----------------------------------------------------------------------------------------
  \[[@B48],[@B49]\]   G1                                        78                       Naïve                       TMC435 12 W peginterferon/ribavirin RGT                     82.1                TMC435 (simeprevir) 75 mg daily
  75                  TMC435 24 W peginterferon/ribavirin RGT   74.7
  77                  TMC435 12 W peginterferon/ribavirin RGT   80.5                     TMC435 150 mg daily
  79                  TMC435 24 W peginterferon/ribavirin RGT   86.1
  77                  Placebo                                   64.9
  \[[@B48],[@B50]\]   G1                                        27                       Relapsers                   PR PR48                                                     37
  79                  TMC435 100 mg PR48                        85
  79                  TMC435 150 mg PR48                        85
  23                  Partial responders                        PR PR48                  9
  68                  TMC435 100 mg PR48                        57
  69                  TMC435 150 mg PR48                        75
  16                  Null responders                           PR PR48                  19
  50                  TMC435 100 mg PR48                        46
  51                  TMC435 150 mg PR48                        51
  \[[@B55]\]          G1                                        12                                                   3 mg daclatasvir PR48                                       42                  Treatment-naïve or less than 4 wks of exposure to ribavirin or interferon-based therapy
  12                  10 mg daclatasvir PR48                    83
  12                  60 mg daclatasvir PR48                    83
                                                                12                                                   Placebo PR48                                                25

MK-5172, a novel P2-P4 quinoxaline macrocyclic peptide, maintained potency across a genetically diverse panel of genotype 1a and 1b sequences from plasma of HCV-infected patients. This drug is to be used in combination with peginterferon plus ribavirin or with other DAAs \[[@B51]\]. Faldaprevir (BI 201335) is an inhibitor of HCV NS3/4A protease and is undergoing phase III clinical trials \[[@B52],[@B53]\].

HCV NS5A and NS5B inhibitors
----------------------------

HCV NS5A inhibitor daclatasvir (BMS-790052) with potent clinical effects has been found in the HCV replicon system \[[@B54]\]. Daclatasvir is a potent NS5A replication complex inhibitor and increases the antiviral potency of peginterferon and ribavirin \[[@B55]\] (Tables [4](#T4){ref-type="table"}).

######

New drugs (DAA combinations) for the treatment of hepatitis C in phase II study

  **Drug name/category**                        **Drug name/category**                        **Company**
  --------------------------------------------- --------------------------------------------- ------------------------------
  ABT-450r/Protease inhibitor                   ABT-072/Polymerase inhibitor                  Abbott/Enanta
  ABT-450r/Protease inhibitor                   ABT-267/NS5A inhibitor                        Abbott/Enanta
  ABT-450r/Protease inhibitor                   ABT-333/Polymerase inhibitor                  Abbott/Enanta
  Daclatasvir (BMS-79002)/ NS5A inhibitor       Sofosbuvir (GS-7977)/ Polymerase inhibitor    Bristol-Myers Squibb/Gilead
  Daclatasvir (BMS-79002)/ NS5A inhibitor       Simeprevir (TMC435)/ Protease inhibitor       Bristol-Myers Squibb/Janssen
  Danoprevir (RG7227)/ Protease inhibitor       Setrobuvir (ANA 598)/ Polymerase inhibitor    Genentech
  Danoprevir (RG7227)/ Protease inhibitor       Mericitabine (RG7128)/ Polymerase inhibitor   Genentech
  GS-9256                                       Tegobuvir (GS-9190)/ Polymerase inhibitor     Gilead
  Incivek (Telaprevir)/ Protease inhibitor      VX-222/Polymerase inhibitor                   Vertex
  Mericitabine (RG7128)/ Polymerase inhibitor   Incivek (Telaprevir)/ Protease inhibitor      Genentech/Vertex
  Simeprevir (TMC435)/ Protease inhibitor       Daclatasvir (BMS-79002)/ NS5A inhibitor       Janssen/Bristol-Myers Squibb
  Simeprevir (TMC435)/ Protease inhibitor       Sofosbuvir (GS-7977)/ Polymerase inhibitor    Janssen/ Gilead
  Simeprevir (TMC435)/ Protease inhibitor       TMC647055/NNI inhibitor/Ritonavir             Janssen
  Sofosbuvir (GS-7977)/ Polymerase inhibitor    GS-5885/ NS5A inhibitor                       Gilead
  VX-135/Polymerase inhibitor                   GSK2336805/ NS5A inhibitor                    Vertex/GSK
  VX-135/Polymerase inhibitor                   Simeprevir (TMC435)/ Protease inhibitor       Vertex/Janssen

HCV advocate: <http://hcvadvocate.blogspot.ca/p/quick-reference-guide_21.html> accessed on 2013/2/24.

######

New drugs for the treatment of hepatitis C in phase III study (combinations with peginterferon plus ribavirin, or DAA combinations)

  ***Combination with peginterferon plus ribavirin***
  ----------------------------------------------------- ------------------------------------------------------------ ----------------------
  Daclatasvir (BMS-79002)                               NS5A inhibitor                                               Bristol-Myers Squibb
  Simeprevir (TMC435)                                   Protease inhibitor                                           Janssen
  Sofosbuvir (GS-7977)                                  Polymerase inhibitor                                         Gilead
  ***DAA combinations***
  **Drug name/Category**                                **Drug name/Category**                                       **Company**
  ABT-450r/Protease inhibitor                           ABT-267/NS5A inhibitor and/or ABT-333/Polymerase inhibitor   Abbott/Enanta
  Daclatasvir (BMS-79002)/ NS5A inhibitor               Asunaprevir (BMS-650032)/Protease inhibitor                  Bristol-Myers Squibb
  Faldaprevir (BI201335)/ Protease inhibitor            GS-5885/NS5A inhibitor                                       Boehringer Ingelheim
  Sofosbuvir (GS-7977)/ Polymerase inhibitor            BI207127/Polymerase inhibitor                                Gilead
  Sofosbuvir (GS-7977)/ Polymerase inhibitor            GS-5885/NS5A inhibitor                                       Gilead

HCV advocate: <http://hcvadvocate.blogspot.ca/p/quick-reference-guide_21.html> accessed on 2013/2/24.

Sofosbuvir (GS-7977/PSI-7977) is a nucleotide inhibitor of HCV NS5B polymerase. Triple therapy including peginterferon plus ribavirin and sofosbuvir cures \>90% of patients treated for 12 or 24 weeks regardless of HCV genotype \[[@B56]\]. However, SVR rates of sofosbuvir plus ribavirin were \~76% for interferon-naive patients and \~11% for prior null responders in HCV genotype 1 patients \[[@B56],[@B57]\]. Sofosbuvir plus peginterferon/ribavirin in treatment-naive patients with HCV genotype 1 leads to higher RVR rates and SVR rates than SOC (88-94% vs. 21%; and 56-83% vs. 43%, respectively) \[[@B58]\]. It was also reported that sofosbuvir in combination with low- or full- dose ribavirin for 24 weeks leads to higher efficacy in difficult-to-treat HCV infected genotype 1 patients \[[@B59]\].

HCV drugs in the future
-----------------------

New drugs for the treatment of hepatitis C are now being investigated in phase II and phase III clinical trials (Tables [5](#T5){ref-type="table"} and [6](#T6){ref-type="table"}). These include interferon-sparing regimens, which are needed for the treatment of those intolerant to, or medically ineligible for peginterferon plus ribavirin therapy \[[@B60],[@B61]\]. Among treatment-experienced patients with advanced fibrosis or cirrhosis, previous relapsers are likely to respond very well to telaprevir- or boceprevir-based treatment, although advanced liver disease had a greater influence on SVR rates in previous nonresponders \[[@B39],[@B42],[@B62],[@B63]\]. Of importance is interferon-sparing combinations that might potentially be used in all patients who cannot use interferon such as subjects with decompensated cirrhosis or low platelet count. Some DAAs are potent inhibitors independently of HCV genotypes \[[@B56],[@B64]-[@B66]\]. The all-oral combination of daclatasvir plus sofosbuvir, with or without ribavirin, leads to higher SVR rates in treatment-naive patients chronically infected with HCV genotypes 1, 2 and 3 \[[@B67]\]. We may select the combination of several drugs according to personal features and/or HCV genotypes. In the near future, all-oral DAAs will treat HCV-infected patients (Figure [1](#F1){ref-type="fig"}).

Conclusions
===========

We expect that all-oral DAAs and interferon-free regimens will be applied in the treatment of HCV-infected patients and that they will have more potent efficacy and less adverse events. Further studies are now ongoing.

Competing interests
===================

Dr. Tatsuo Kanda reports receiving lecture fees from Chugai Pharmaceutical, MSD, Ajinomoto, GlaxoSmithKlein and Mitsubishi Tanabe Pharma, and Prof. Osamu Yokosuka received grant support from Chugai Pharmaceutical, Bayer, MSD, Daiichi-Sankyo, and Mitsubishi Tanabe Pharma.

Authors' contributions
======================

TK drafted the manuscript and all authors read through and made corrections to the manuscript. All authors read and approved the final manuscript.


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1. Introduction {#sec1}
===============

EEG signal is widely used for exploring the human brain activity and is preferred over other physiological signals because they can be used to detect directly brain electrical activity changes over spans of millisecond time, whereas functional magnetic resonance imaging (fMRI) has time resolutions in seconds or minutes. Usually, EEG signal suffers from various motion artifacts generated at the capturing time. There are two main sources of artifact in neural signals other than the machine and environment. These are the muscular and ocular activities of the individual which generate low-amplitude, low-frequency electrical pulses that fall in filter range of sensors and recording equipment.

The EEG signal is contaminated by various artifacts as electrocardiogram (ECG), electrooculogram (EOG), and electromyogram (EMG). The EMG artifact is of more interest as it is having higher amplitude, broad spectrum, and variable topographical distribution than EEG signal \[[@B1]\]. The EMG artifacts have lower autocorrelation than EEG signal due to its wide frequency spectrum. Moreover, these artifacts resemble as temporal white noise. Therefore, artifact rejection is a fundamental research topic and is well researched in \[[@B2]\]. The EEMD is a data-driven and noise-assisted approach which is applied to remove motion artifacts from single-channel EEG signal \[[@B3]\].

A component-based automated separator of artifacts is required to linearly decompose the signals into source components. The components give the individual nature of information, where artifact information combines into separate sources and reconstruction of signals without these sources are claimed as artifact-free information. The performance of blind source separation (BSS) methods as independent component analysis (ICA) and CCA for EEG signal eye blink artifact removal is compared in \[[@B4]\] and concluded that CCA is more accurate and faster than ICA. The CCA algorithm performs better than ICA for muscle artifact removal because these artifacts are generated due to movement of body muscle group. Moreover, these artifacts do not represent stereotyped topography \[[@B5]\]. Anastasiadou et al. \[[@B6]\] applied CCA algorithm to remove muscle artifact from EEG signal. Moreover, artifact removal approach is improved by applying WT after CCA algorithm for automatic detection and removal of muscle artifact from EEG signal \[[@B7]\]. The cascaded combination of EEMD and CCA techniques is applied for single-channel EEG artifact removal in \[[@B8], [@B9]\]. The single-channel EEG signal is converted into the multidimensional signal by EEMD technique. In CCA, a second-order statistics is applied to segregate the artifact components from the input signal and its performance is compared and presented better in comparison of existing wavelet denoising and EEMD-ICA cascaded algorithms. Chen et al. \[[@B10]\] have improved the filtering approach by applying EEMD and MCCA (multiset CCA) to remove EMG with more computational time. To remove ocular artifacts automatically, the cascaded combination of CCA and WT algorithms is proposed in \[[@B11]\] and demonstrated that the proposed method removes artifacts significantly with preserving neural activity of the original signal. Safieddine et al. \[[@B12]\] suggested that EEMD algorithm outperforms over other stochastic and deterministic artifact removal approaches and WT algorithm performs well in the case of less noisy data.

An efficient cascaded approach EEMD-CCA-SWT is found successful for EEG motion artifact removal \[[@B13]\]. The combination of EEMD, CCA, and SWT approaches has been applied for effective suppression of the motion artifact from EEG signal. This three-stage cascaded approach removes the artifacts effectively with increased computational cost. This computational complexity is reduced in this research paper by developing an existing correlation-based algorithm with Gaussian elimination (GE) and inserted at the cascade of EEMD-SWT, leading to EEMD-GECCA-SWT which is the combination of EEMD and an improved approach GECCA (Gaussian elimination canonical correlation analysis) with SWT. This increased computational cost is effectively reduced due to applying GECCA approach in place of CCA. The left matrix division applied in GECCA allows better estimates for the matrix inversion; therefore, it improves the SWT filtering efficiency and thus improves the overall efficiency of the EEG motion artifact removal.

The paper organization is as the following. The artifact removal methods are recalled in [Section 2](#sec2){ref-type="sec"}, and then the proposed algorithm is discussed in [Section 3](#sec3){ref-type="sec"}. Details of applied EEG dataset is given in [Section 4](#sec4){ref-type="sec"}; results obtained by methods are presented, compared in tabular form, and detailed discussed in [Section 5](#sec5){ref-type="sec"}. Finally, conclusion is in [Section 6](#sec6){ref-type="sec"}.

2. Artifact Removal Methods {#sec2}
===========================

2.1. Ensemble Empirical Mode Decomposition {#sec2.1}
------------------------------------------

The EEMD algorithm decomposes a signal into a number of intrinsic mode functions (IMFs) through an iterative method termed as sifting \[[@B12]\]. At first level, the IMF1 is the mean of upper and lower envelop of original EEG signal *X*(*t*). Then residual signal is obtained by subtracting IMF1 from *X*(*t*). This process is iterated till stopping criterion is fulfilled (residual signal energy content is close to zero). The remaining residual signal is $$\begin{matrix}
{P_{n}\left( t \right) = P_{n - 1}\left( t \right) - {IMF}_{n}\left( t \right),} \\
\end{matrix}$$where *P*~*n*~(*t*) = *X*(*t*).

Finally, the signal is reconstructed by adding all IMFs and residual signal as $$\begin{matrix}
{X\left( t \right) = P_{n}\left( t \right) + \sum\limits_{i = 1}^{N}{IMF}_{i}\left( t \right).} \\
\end{matrix}$$

2.2. Gaussian Elimination Canonical Correlation Analysis (GECCA) {#sec2.2}
----------------------------------------------------------------

The standard existing CCA algorithm starts with an assumption that *X*\[*n*\] and *Y*\[*n*\] are two sets of random variables \[[@B4]\]. *X*\[*n*\] is the input vector of matrix and *Y*\[*n*\] is defined as temporally correlated by 2-D valid convolution operator from *X*\[*n*\] vectors using the linear convolution mask \[1 0 1\] as $$\begin{matrix}
{Y\left\lbrack n \right\rbrack = conv2\left( {X,\left\lbrack {1\, 0\, 1} \right\rbrack} \right).} \\
\end{matrix}$$

Merging both input vectors as $$\begin{matrix}
{Z = \left\lbrack {X;Y} \right\rbrack.} \\
\end{matrix}$$

If *ρ* is the maximum canonical correlation, *Cxx*, *Cyy* are auto covariances of vectors *X* and *Y*, respectively, and *Cxy* and *Cyx* are the cross covariance between vectors *X* and *Y*. The various possible correlation matrixes are $$\begin{matrix}
{Cxx = C\left( {1:sx,1:sx} \right) + \beta \ast eye\left( {sx} \right),} \\
\end{matrix}$$where *sx* is the size of the *X*, *sy* is the size of the *Y*, *β* is the predefined small residual constant which is set to 10^−8^ in this paper which is used by standard CCA method \[[@B2]\], and eye is the identity matrix with diagonal terms as 1 and all other terms are zero. The term *β*∗eye(*sx*) is used in order to set and maintain the initial nonzero value of the covariance matrix *Cxx*. The small value of *β* does not affect the covariance matrix values much. $$\begin{matrix}
{Cxy = C\left( {1:sx,sx + 1:sx + sy} \right),} \\
{Cyx = Cxy^{\prime},} \\
{Cyy = C\left( {sx + 1:sx + sy,sx + 1:sx + sy} \right) + ç \ast eye\left( {sy} \right),} \\
{{Cyy}^{- 1} = inv\left( {Cyy} \right).} \\
\end{matrix}$$

Two different canonical solutions are obtained from *Z* by calculating the covariance as $$\begin{matrix}
{C = \text{cov}\left( {Z.^{\prime}} \right).} \\
\end{matrix}$$

This is equivalent to two linear equations in *X* and *Y* vector directions, respectively, as $$\begin{matrix}
{p\left\lbrack n \right\rbrack = a \ast X\left\lbrack n \right\rbrack,} \\
{q\left\lbrack n \right\rbrack = b \ast Y\left\lbrack n \right\rbrack,} \\
\end{matrix}$$where *a* and *b* are weight vectors and *p*\[*n*\] and *q*\[*n*\] are canonical variates correspondent to *X*\[*n*\] and *Y*\[*n*\], respectively.

The maximum correlation between variables *a* and *b* are calculated as \[[@B8]\] $$\begin{matrix}
{\text{max}\left( \rho \right) = \frac{a^{T}C_{xy}b}{\sqrt{a^{T}C_{xx}a}\sqrt{b^{T}C_{yy}b}}.} \\
\end{matrix}$$

Now, the demixing matrix *W* is calculated by simplifying ([9](#EEq2){ref-type="disp-formula"}) as $$\begin{matrix}
{\rho^{2}a = inv\left( {Cxx} \right) \ast Cxy \ast inv\left( {Cyy} \right) \ast Cyx \ast a,} \\
\end{matrix}$$$$\begin{matrix}
{\rho^{2}b = inv\left( {Cxx} \right) \ast \left( {Cxy} \right) \ast inv\left( {Cyy} \right) \ast Cyx \ast b,} \\
\end{matrix}$$where *ρ*^2^ is calculated by obtaining Eigen values (*k*) of ([10](#EEq3){ref-type="disp-formula"}) and ([11](#EEq4){ref-type="disp-formula"}) and further, *a* and *b* variables are equal to Eigen vectors for the greatest value of *k* (Eigen value).

These weight vectors *a* and *b* are replaced in ([8](#EEq1){ref-type="disp-formula"}) to calculate canonical variates *p*\[*n*\] and *q*\[*n*\]. Moreover, the demixing matrix *W* can be calculated by inverse of estimated weight vector (*W* = *a*^−1^). Finally, the source *S*\[*n*\] is estimated by applying *W* and *p*\[*n*\] or *q*\[*n*\] as $$\begin{matrix}
{S\left\lbrack n \right\rbrack = W^{\prime}\, p\left\lbrack n \right\rbrack.} \\
\end{matrix}$$

Thus, it is observed that standard CCA algorithm uses the matrix inverse operation to solve the linear equations by obtaining the maximum value of *ρ*^2^ for determining the demixing matrix *W*.

In this research paper, a fast and efficient modified CCA algorithm is proposed which uses the backslash or left matrix division operator for solving the linear ([10](#EEq3){ref-type="disp-formula"}) and ([11](#EEq4){ref-type="disp-formula"}) to calculate Eigen values. This backslash operator is fast and efficient than the inverse operator. The remaining algorithm is similar to the CCA approach. Let us define the linear ([10](#EEq3){ref-type="disp-formula"}) as $$\begin{matrix}
{G = \rho^{2} = inv\left( C_{xx} \right) \ast C_{xy} \ast inv\left( C_{yy} \right) \ast C_{yx},} \\
\end{matrix}$$$$\begin{matrix}
{G = \rho^{2} = A \ast B,} \\
\end{matrix}$$where $$\begin{matrix}
{\, A = inv\left( C_{xx} \right) \ast C_{xy} = {C_{xx}}^{- 1} \ast C_{xy},} \\
\end{matrix}$$$$\begin{matrix}
{B = inv\left( C_{yy} \right) \ast C_{yx} = {C_{yy}}^{- 1} \ast C_{yx}.} \\
\end{matrix}$$

The parameters *A* and *B* from linear ([15](#EEq7){ref-type="disp-formula"}) and ([16](#EEq8){ref-type="disp-formula"}) are defined as $$\begin{matrix}
\begin{array}{l}
{A \ast Cxx = Cxy,} \\
{B \ast Cyy = Cyx.} \\
\end{array} \\
\end{matrix}$$

Standard CCA method uses matrix inverse to obtain the value of *A* and *B*. In this paper, the left matrix division is proposed to solve these linear ([15](#EEq7){ref-type="disp-formula"}) and ([16](#EEq8){ref-type="disp-formula"}).

The left division operator as *Cxx*\\*Cxy* can be defined as a matrix division of *Cxx* into *Cxy*, which is equal to the solution of inv(*Cxx*)∗*Cxy*. If *Cxx* is an *N*-by-*N* matrix and *Cxy* is a column vector with *N* components or a matrix containing several such columns, then *A*∗*Cxx* = *Cxy* equation can be defined in terms of left division operator as *A* = *Cxx*\\*Cxy*.

The similar solution can be defined for the matrix *B*. The solution of ([15](#EEq7){ref-type="disp-formula"}) and ([16](#EEq8){ref-type="disp-formula"}) can be written as $$\begin{matrix}
{A = Cxx\backslash Cxy,} \\
\end{matrix}$$$$\begin{matrix}
{B = Cyy\backslash Cyx.} \\
\end{matrix}$$

Equations ([18](#EEq9){ref-type="disp-formula"}) and ([19](#EEq10){ref-type="disp-formula"}) suggest that the role of inverse operation is avoided to obtain the value of parameters *A* and *B*. This will simplify the equation and thus computation time too.

### 2.2.1. The Justification of Left Division Operator Employed in GECCA {#sec2.2.1}

In order to prove left division operator, the numerator and denominator are multiplied by numerator transposed as $$\begin{matrix}
{A = {Cxx}^{T} \ast Cxx\backslash{Cxx}^{T} \ast Cxy,} \\
{B = {Cyy}^{T} \ast Cyy\backslash{Cyy}^{T} \ast Cyx.} \\
\end{matrix}$$

By shuffling the order of operator by using Gaussian elimination concept, $$\begin{matrix}
{A = {Cxx}^{T} \ast \left( {Cxx \ast {Cxx}^{T}} \right)\backslash Cxy,} \\
\end{matrix}$$$$\begin{matrix}
{B = {Cyy}^{T} \ast \left( {Cyy \ast {Cyy}^{T}} \right)\backslash Cyx,} \\
\end{matrix}$$since *Cxx* and *Cyy* are *N*-by-*N* symmetric matrix and *Cxy* and *Cyx* are column vectors with *N* components or a matrix with several such columns.

Since *Cxx*∗*Cxx*^T^ = *Cyy*∗*Cyy*^T^ = 1, therefore, ([21](#EEq11){ref-type="disp-formula"}) and ([22](#EEq12){ref-type="disp-formula"}) can be written as $$\begin{matrix}
{A = {Cxx}^{T} \ast \left( 1 \right)\backslash Cxy,} \\
\end{matrix}$$$$\begin{matrix}
{B = {Cyy}^{T} \ast \left( 1 \right)\backslash Cyx.} \\
\end{matrix}$$

For symmetric and square matrix, *Cxx*^*T*^ = *Cxx* and *Cyy*^*T*^ = *Cyy*.

Then ([23](#EEq13){ref-type="disp-formula"}) and ([24](#EEq14){ref-type="disp-formula"}) are simplified as $$\begin{matrix}
{A = Cxx\backslash Cxy,} \\
\end{matrix}$$$$\begin{matrix}
{B = Cyy\backslash Cyx.} \\
\end{matrix}$$

Replacing the values of *A* and *B* from ([25](#EEq15){ref-type="disp-formula"}) and ([26](#EEq16){ref-type="disp-formula"}) to ([14](#EEq6){ref-type="disp-formula"}), the solution of *G* is obtained as $$\begin{matrix}
{G = \left( {Cxx\backslash Cxy} \right) \ast \left( {Cyy\backslash Cyx} \right).} \\
\end{matrix}$$

The value of *G* is obtained without inverse operation by just applying left division operator. This solution not only saves the computation time but also increases the efficiency of the CCA method with minimizing the error. The obtained G = *ρ*^2^ is the Eigen value with *k* in descending order, that is, *k*~1~ \> *k*~2~ \> *k*~*n*~; *a* and *b* are the Eigen vector for the greatest value of *k*.

Then the CCA component *p*\[*n*\] = \[*p*~1~, *p*~2~,..., *p*~*n*~\] are obtained by placing the vector *X*\[*n*\] in ([8](#EEq8){ref-type="disp-formula"}), and sources are expected by using the weighted demixing matrix from *X* by using ([12](#EEq12){ref-type="disp-formula"}).

If *A* is a *M* × *N* matrix with *M* ≤ *N* and *B* is a column vector with *M* components, or a matrix with several such columns, then *X* = *A*\\*B* is the solution in the least squares sense to the under or over determined system of equations *A*∗*X* = *B*. The effective rank *K* of *A* is determined from the QR decomposition with pivoting. The solution *X* is computed which has at most *K* nonzero components per column.

### 2.2.2. Benefits of GECCA over CCA Algorithm {#sec2.2.2}

The advantage of GECCA Algorithm over CCA are as follows: The operation of CCA algorithm depends on the inverse operation to find the demixing matrix. If the input matrix is not a square matrix, then this CCA algorithm fails. However, left division operator-based GECCA algorithm provides a solution if input matrix is not square.The CCA algorithm involves the inverse of the matrix for their operation. The inverse operation is computationally complex and time-consuming. However, proposed GECCA algorithm is faster than CCA for obtaining the correlation-based source separation by employing left division operator in place of inverse operation.The proposed GECCA algorithm employs left division operator. This operator allows better estimates of the matrix inversion. These efficiently estimated coefficients once applied to SWT filter improve the SWT filtering efficiency thus improving the overall efficiency of the motion artifact removal algorithm.The CCA algorithm operation is not consistent in each situation. This issue can be overcome by the proposed GECCA algorithm.

The above discussed efficient and fast correlation-based algorithm applied with WT algorithm is discussed in the subsequent section.

2.3. Wavelet Transform (WT) {#sec2.3}
---------------------------

The artifacts available in the EEG signal are suppressed with cascaded approach of EEMD and GECCA. Although, some brain actions may be disturbed due to high-frequency sensor noise and low-pass and high-pass filter components. As these noise signal frequencies will get overlap with the brain signals, then conventional filtering technique cannot be utilized and thus this WT is applied to take away unwanted noises from EEG signal \[[@B12]\].

The most frequently applied wavelet transform is discrete wavelet transform (DWT). In the case of EEG artifact removal, preserving neural information of the signal is determinant. Thus, some latest research shows that stationary wavelet transform (SWT) is a powerful tool to remove artifacts of the signal with preserving the neural information of original EEG signal \[[@B14]\].

SWT algorithm is translation invariant, so no downsampling of the data is involved. Translation-invariance is achieved by removing the down- and upsamplers as in the DWT and upsampling the filter coefficients by a factor of 2^(*j*\ −\ 1)^ at the *j*th level of the algorithm. SWT is preferred as it removes unpredictable behavior and noise randomness in the EEG signal due to the motion artifacts remaining after two-stage filtering of EEG signals. Therefore, EEG signal gets smoothened over the length with containing all their fundamental properties. Finally, proposed algorithm based on discussed algorithm is discussed in the next section.

3. Proposed Algorithm {#sec3}
=====================

The proposed artifact removal algorithm is as follows: Define the reference EEG data from the multichannel data set for correlation match.The input artifact EEG channel is passed through EEMD to decompose with 3 ensembles to convert single-channel EEG into multichannel EEG data.Generated IMFs are passed to GECCA algorithm for source separation.The GECCA output contains traces of artifacts in the form of randomness and noise. Thus, the output of GECCA is applied further to SWT algorithm for effective artifact removal. The decomposition through SWT is performed on the frequency domain of the signal with Rigrsure thresholding.Pearson\'s correlation coefficient is used for artifact recognition and their suppression and finally reconstructed to find the artifact-free signal.Parametric evolution is carried out based on SNR and correlation improvement. The correlation is carried out with respect to reference original EEG data initially defined.The parametric evaluation of the proposed method is done with existing motion artifact removal methods.

4. EEG Signal Data Set {#sec4}
======================

The EEG data is a multichannel data considered from standard MIT scalp data set with 24 channels recorded with different electrodes. This dataset is available on PhysioNet. The data is taken for 10 seconds and with 2560 samples/signals. Sampling frequency is taken as 256 Hz, and sampling interval is of 0.00390625 sec. These data samples are reduced to 16 channels to satisfy the international standard of 10--20 channels. [Figure 1()](#fig1){ref-type="fig"} shows the first 16 channels of EEG data set available online with chb01_01_edfm.mat available at PhysioNet provided by Shoeb \[[@B15]\]. It is observed from [Figure 1()](#fig1){ref-type="fig"} that each channel is recorded with different electrode having different motion characteristics.

In [Figure 1(a)](#fig1){ref-type="fig"}, the channels 1, 5, 9, and 13 correspond to the eye blinks as shown by negative high peaks (scale of 1000 and 2000). This was displayed and referenced by Shoeb \[[@B15]\]. These channels (1, 5, 9, and 13) have maximum motion or artifacts since they have higher standard deviation 172.0907, 175.417, 136.129, and 170.467, respectively. In this paper, channel numbers 4, 7, and 11 have the least artifact effects (only with scale of 200) out of the 16 channel EEG signal, this can be quantitatively justified by their lowest mean value and standard deviation (on division by 10 scale) as shown in [Figure 1(b)](#fig1){ref-type="fig"}. The channel 7 in [Figure 1(a)](#fig1){ref-type="fig"} has the smallest standard deviation (=4,364,903) and the smallest absolute mean value (=0.228125) as shown in [Figure 1(b)](#fig1){ref-type="fig"} which justifies the minimum motion artifacts. Thus, the channel number 7 is used as a reference signal for correlation-based artifact removal. This reference signal is termed as original signal in the paper.

[Figure 2](#fig2){ref-type="fig"} compares the original or reference EEG data (channel number 7) with blue color and artifactual signals (channel number 5) with red color. The channel 5 is shown here for the comparison because it has maximum standard deviation of 175.417 with maximum peak variations with eye blink. It is observed from [Figure 2](#fig2){ref-type="fig"} that raw EEG data may suffer from either muscular motion artifact (shown under green rectangle) or eye blink artifact (shown under yellow rectangle) or both of them. The eye blink artifact may cause amplitude increase about 10 times greater than the original EEG data. The EOG artifact overlaps with EEG signal in impulsive form, while muscular motions cause random broad spectrum amplitude variations. Nature of the reference EEG data with and without artifacts is clearly shown in Figures [2(b)](#fig2){ref-type="fig"} and [2(c)](#fig2){ref-type="fig"}.

The eye blink artifact may cause amplitude increase about 10 times greater than the original EEG data. The EOG artifact overlaps with EEG signal in impulsive form, while muscular motions cause random broad spectrum amplitude variations. In this paper, the muscular motion artifact signals (as in channel 5) are naturally generated at the time of EEG data capturing with electrodes. Therefore, for the sake of clarity, the enhanced version of reference and artifact signal is shown in Figures [2(b)](#fig2){ref-type="fig"} and [2(c)](#fig2){ref-type="fig"}.

5. Result and Discussion {#sec5}
========================

The novel GECCA artifact removal approach is evaluated on artifactual EEG channel number 5 and compared with CCA algorithm. Comparison of the EEG artifact removal methods for channel number 5 using CCA and GECCA along with DWT is shown in [Figure 3](#fig3){ref-type="fig"}. The EEG signal channel number 7 is considered as pure EEG signal. The comparison of the green and yellow boxes in Figures [3(a)](#fig3){ref-type="fig"} and [3(b)](#fig3){ref-type="fig"} suggests that blind source separation- (BSS-) based proposed GECCA method removes the motion artifacts significantly better than the existing CCA-based method. Green rectangles are correspondent to CCA, and yellow rectangles are correspondent to GECCA. It is observed that the eye blink peaks with GECCA-DWT are minimized to half that of the eye blink peaks with CCA-DWT (of the order of ±200 is reduced to around ±100) as mentioned in fourth row of [Figure 3](#fig3){ref-type="fig"}. Moreover, the pattern of the second blink is also much similar to the reference EEG signal after GECCA-DWT.

The SWT algorithm is more proficient for artifact removal of neural EEG signal in comparison to DWT algorithm; consequently, the efficacy of proposed BSS approach (GECCA) is also evaluated with SWT and compared with CCA-based approaches. [Figure 4](#fig4){ref-type="fig"} provides the comparison of the EEG artifact removal methods using CCA and GECCA along with SWT filtering. The artifact removal approaches are applied on channel number 5, and performance is compared with reference channel 7 EEG signal. The comparison of the respective green and yellow boxes in Figures [4(a)](#fig4){ref-type="fig"} and [4(b)](#fig4){ref-type="fig"} suggests that the proposed GECCA method removes the motion artifacts significantly better than the existing CCA-based method. In addition, the vigilant observation and comparison of the respective green rectangles in [Figure 4(a)](#fig4){ref-type="fig"} and yellow rectangles in [Figure 4(b)](#fig4){ref-type="fig"} suggests that GECCA with SWT removes the eye blink effect better than CCA-SWT. It is observed that the eye blink peaks with GECCA-SWT are minimized to half that of the eye blink peaks with CCA-SWT (of the order of ±200 is reduced to around ±100) as mentioned in the fourth row of [Figure 4](#fig4){ref-type="fig"}. This could be quantitatively even better clear in the next sections with evaluation parametric comparison encapsulated in [Table 1](#tab1){ref-type="table"}.

The 8 reconstructed signals after SWT-smoothened approach are compared for CCA and GECCA as shown in [Figure 5](#fig5){ref-type="fig"}. It is observed that GECCA method not only preserves the information but also provides better and fast estimates. This is concluded by comparing the 2nd and 4th frequency bands as shown in Figures [5(c)](#fig5){ref-type="fig"} and [5(d)](#fig5){ref-type="fig"}.

Moreover, the proposed artifact removal method (EEMD-GECCA-SWT) outputs are compared for three distinct EEG channel data, namely, Ch 9, Ch 4, and Ch 14, as presented in [Figure 6](#fig6){ref-type="fig"}. In [Figure 6](#fig6){ref-type="fig"}, blue color presents the original artifactual signal, whereas red color signal represents the filtered output by the proposed approach. It is demonstrated that proposed method of GECCA removes the eye blink artifacts and motion artifacts significantly.

In order to evaluate the performance of the proposed method, parametric evaluation is carried out as shown in [Table 1](#tab1){ref-type="table"}. The performance of artifact removal methods is carried out for CCA and GECCA methods along with DWT and SWT filtering methods. It is observed that proposed method EEMD-GECCA-SWT reduces the mean square error (MSE) by around 15.81% and improves the DSNR performance significantly. Moreover, the higher lambda (*λ*) value indicates the improved filtering performance \[[@B8]\]. [Table 1](#tab1){ref-type="table"} shows that GECCA-based artifact removal methods attain improved lambda (*λ*) value. The vigilant observation of results on [Table 1](#tab1){ref-type="table"} also presents better correlation improvement of GECCA when compared to CCA, both for DWT and SWT. The backslash operator provides better estimation efficiency. Thus, Gaussian elimination-based approach provides improved correlation by using backslash operation which is more efficient than the matrix inverse method.

The proposed method evaluation is also carried out with the receiver operation characteristic (ROC) curve parameters as shown in [Table 2](#tab2){ref-type="table"}. The positive predictive value (PPV), negative predictive value (NPV), sensitivity (Sen), and specificity (Spe) are calculated by true positive (TP), true negative (TN), false positive (FP), and false negative (FN) \[[@B16]\] as follows: $$\begin{matrix}
{Sen = \frac{TP}{TP + FN},} \\
\end{matrix}$$$$\begin{matrix}
{Spe = \frac{TN}{TN + FP},} \\
\end{matrix}$$$$\begin{matrix}
{PPV = \frac{TP}{TP + FP},\mspace{9mu}} \\
\end{matrix}$$$$\begin{matrix}
{NPV = \frac{TN}{TN + FN}.} \\
\end{matrix}$$

The true positive (TP) indicates that the sample is identified as an artifact when it was actually an artifact. False positive (FP) presents that the sample is identified as an artifact but actually it was not. The false negative (FN) is an indicator for the absence of artifact; however, the artifact was present in the sample. Moreover, true negative (TN) indicates the absence of the artifact when the artifact was actually not present.

This ROC curve is a comparison plot between true positive rate or sensitivity and false positive rate (1 − specificity) \[[@B16]\] for accurate detection of artifact from EEG signal as shown in [Figure 7()](#fig7){ref-type="fig"}.

[Figure 7(a)](#fig7){ref-type="fig"} shows comparison of CCA and GECCA approaches based on DWT. Blue and red color horizontal line indicates the sensitivity of EEMD-CCA-DWT and EEMD-GECCA-DWT artifact removal methods, respectively. The sensitivity or true artifact detection rate for CCA-based approach is 0.39961, whereas GECCA-based approach presents improved and fast response with increased value of 0.45156. However, [Figure 7(b)](#fig7){ref-type="fig"} shows SWT-based approach comparison for CCA and GECCA algorithm with the same blue and red color code. Both approaches present almost similar sensitivity 0.43438 and 0.43789, respectively, as shown in [Table 2](#tab2){ref-type="table"}. Although, GECCA-based approach performs better than CCA artifact removal algorithm. Moreover, the sensitivity is associated with accuracy of the algorithm too. All the ROC parameters as sensitivity, specificity, PPV, and NPV as mentioned above in ([28](#EEq17){ref-type="disp-formula"}), ([29](#EEq18){ref-type="disp-formula"}), ([30](#EEq19){ref-type="disp-formula"}), and ([31](#EEq20){ref-type="disp-formula"}) are calculated for EEG signal channel number 5 and tabulated for effective comparison of CCA and GECCA-based approaches as shown in [Table 2](#tab2){ref-type="table"}. It is observed that GECCA-based approach shows improved sensitivity and thus better accuracy than CCA-based approaches with both DWT and SWT filtering. Moreover, specificity, PPV, and NPV also improve by GECCA-based methods.

In addition, the efficiency of GECCA-based artifact removal approach is also evaluated by considering ROC parameters for most artifact contaminated channel numbers (1, 3, 5, 6, 9, and 14) from [Figure 1](#fig1){ref-type="fig"}. [Table 3](#tab3){ref-type="table"} encapsulates all the ROC parameters for filtering approach based on CCA and GECCA. It is observed from [Table 3](#tab3){ref-type="table"} that GECCA-based methods show improved accuracy in comparison to CCA-based method except channel 1. Similarly, specificity, PPV, and NPV parameters are also improved for most of the channel which presents the success of GECCA-based artifact removal approach.

The use of the left matrix division operator in the GECCA algorithms executes faster than the inverse matrix method used in CCA algorithm. Therefore, the proposed GECCA method is faster than the CCA. Moreover, left matrix division allows better estimates of the matrix inversion which improves the SWT filtering efficiency thus improves the overall efficiency of the Pearson\'s correlation-based artifact removal.

This execution or computation speed is evaluated by elapsed time. The elapsed time comparison of DWT and SWT is shown in [Figure 8(a)](#fig8){ref-type="fig"} suggesting that SWT is faster than DWT. [Figure 8(b)](#fig8){ref-type="fig"} compares the elapsed time for GECCA and CCA with SWT. This presents that GECCA with SWT algorithm computation time is faster than CCA with SWT approach.

[Figure 8(a)](#fig8){ref-type="fig"} suggests that SWT is faster than DWT approach for artifact removal. Thus, SWT elapsed time is evaluated with CCA and GECCA approaches and concluded that GECCA approach with SWT is faster and efficient for EEG artifact removal.

6. Conclusion {#sec6}
=============

The measurement and processing of EEG signal result in the probability of signal contamination prominently through motion artifacts which can obstruct the important features and information quality existing in the original EEG signal. To diagnose the human neurological diseases like epilepsy, tumors, and problems associated with trauma \[[@B15]\], these artifacts must be properly pruned assuring that there is no loss of the main attributes of EEG signals. Thus, a novel algorithm GECCA is introduced in cascade with EEMD and SWT for fast and effective suppression of motion artifacts from single-channel EEG signal. The proposed GECCA method uses backslash operation to solve the linear equations. This improves the computation efficiency of the methods. The application of SWT instead of DWT improves the SNR performance of the method and faster than the DWT method too. However, the proposed algorithm may give over smoothening if not properly designed. The proposed method based on GECCA is 18% faster than the conventional CCA. The various evaluation parameters as del signal to noise ratio (DSNR), lambda (*λ*), root mean square error (RMSE), and ROC parameters are employed to compare the performance of the proposed artifact removal algorithm. The ROC parameter comparison suggested that the improved accuracy is attained with GECCA algorithm except for Ch 1. The optimum evaluation result shows the success of proposed motion artifact removal method.

Conflicts of Interest
=====================

The authors declare that they have no conflicts of interest.

![(a) Multichannel EEG data set containing the muscular and eye blink motions. (b) Statistical properties of EEG channels.](JHE2017-9674712.001){#fig1}

![(a) Comparison of referenced channel 7 and artifact channel 5 for first 10 seconds. (b) Enhanced reference data channel 7. (c) Artifact channel 5 for 3-4 seconds respective to yellow rectangle.](JHE2017-9674712.002){#fig2}

![Comparisons of EEG artifact removal methods (a) with CCA and DWT and (b) with GECCA and DWT.](JHE2017-9674712.003){#fig3}

![Comparisons of EEG artifact removal methods (a) with CCA and SWT and (b) with GECCA and SWT.](JHE2017-9674712.004){#fig4}

![Reconstructed IMFs after (a) CCA-SWT, (b) GECCA-SWT, and (c) comparison of CCA and GECCA for the 2nd and 4th reconstructed EEMD (IMFs).](JHE2017-9674712.005){#fig5}

![Artifact removal consequences with the proposed (EEMD-GECCA-SWT) method.](JHE2017-9674712.006){#fig6}

![ROC plot comparison for CCA and GECCA (a) based on DWT and (b) based on SWT.](JHE2017-9674712.007){#fig7}

![(a) Elapsed time comparison for DWT and SWT approach and (b) elapsed time comparison for proposed GECCA method with CCA method.](JHE2017-9674712.008){#fig8}

######

Parametric comparison of the artifact removal methods with CCA and GECCA.

  Algorithm                 With DWT filtering   With SWT filtering
  ------------------------- -------------------- -------------------- --------- ---------
  MSE                       69.3283              51.2272              70.2484   50.1366
  Correlation improvement   0.0019               0.0666               0.0103    0.0654
  Lambda                    66.8838              86.0016              66.2544   87.2759
  DSNR                      17.7248              29.0387              17.2621   30.2080

######

Comparison of the ROC parameters with CCA and GECCA for channel 5.

  Algorithm     DWT filtering with   SWT filtering with
  ------------- -------------------- -------------------- ---------- ----------
  Sensitivity   0.39961              0.45156              0.43438    0.43789
  Specificity   0.74062              0.78477              0.65469    0.82656
  Accuracy      57.0117%             61.8164%             54.4531%   63.2227%
  PPV           60.6402%             67.7211%             55.7114%   71.6294%
  NPV           55.2287%             58.8632%             53.6492%   59.5218%

######

Comparison of ROC parameters for 6 different EEG channels for proposed method.

  Method ↓     Parameters in %   Ch 1      Ch 3      Ch 5      Ch 6      Ch 9      Ch 14
  ------------ ----------------- --------- --------- --------- --------- --------- ---------
  With GECCA   Accuracy          57.3047   59.4141   63.2227   65.3125   63.0859   63.5352
  PPV          57.18             57.0674   71.6294   75.0319   71.9241   59.802
  NPV          55.3368           64.0936   59.5218   61.0298   59.3263   71.8612
  With CCA     Accuracy          57.6953   58.8672   54.4531   60.2734   62.2461   61.2109
  PPV          56.0578           62.6251   55.7114   64.7422   69.3399   58.2566
  NPV          60.546            56.8332   53.6492   57.8837   58.9597   67.4574

[^1]: Academic Editor: João Manuel R. S. Tavares


1. Field of the Invention The present invention is directed to a method for the presentation of an examination subject by means of tomograms of the type wherein a number of tomograms are produced which differ in position with respect to one dimension such as, for example, each tomogram being allocated to a different time center of gravity or each tomogram being allocated to a different position of a location axis.
2. Description of the Prior Art
Such methods are known, for example, from computed tomography (CT) and serve the purpose of illustrating modifications within the examination subject as a function of time and/or location. A disadvantage of such known methods is that a number of tomograms must be viewed in order to be able to acquire the information with respect to the time or position change.
There is the possibility of presenting the modifications by producing three-dimensional images or with dynamic, cinematic-like presentation of the tomograms; however, the evaluation of such presentations is not always simple since, in particular, the users of CT apparatuses are accustomed to interpreting static, two-dimensional tomograms.
U.S. Pat. No. 5,216,602 discloses showing the blood flow in an examination subject in CT images with the use of an x-ray contrast agent, with the individual pixels of a diagnostically relevant image being compared to the pixels of a reference image, and those pixels of the diagnostically relevant image that deviate in terms of their CT number from the corresponding pixels of the reference image are presented in color, the color corresponding to the extent of the respective deviation.
An object of the present invention is to provide a method of the type initially described that allows information with respect to a third dimension to be presented in a static tomogram.
This object is inventively achieved by a method for the presentation of an examination subject upon employment of tomograms including the steps of generating a number (series) of tomograms that differ from one another in position with respect to one dimension, coloring some of tomograms in the series of tomograms, with respective colors, whose respective positions on a color scale correspond to the positions of the respective tomograms with respect to the dimension, being allocated to the respective tomograms, and superimposing a number of the color tomograms to form a resultant image.
When three tomograms are colored with the colors red, green and blue, a black-and-white image would arise by superimposition of the colored tomograms if there are no differences whatsoever with respect to the dimension among the tomograms. When, by contrast, differences are present in the three tomograms with respect to the dimension, then an image arises as resultant image that is colored in those regions wherein differences are present with respect to the dimensions, but is a black-and-white image in those regions wherein there are no differences with respect to the dimension.
Thus information with respect to an additional dimension can be presented and recognized in the inventive method in a static tomogram.
The procedure of coloring a tomogram is to be understood as meaning that this operation relates to the tomogram overall, for example by converting all gray-scale values of a black/white image into corresponding red values. In the case of a tomogram that is composed of pixels arranged matrix-like in a manner known from computed tomography, this means that all pixels of the tomogram are affected by the operation of coloring by, for example, the original gray scale value of the respective pixel being replaced by an analogous red value.
In a further version of the invention, the tomograms respectively have substantially the same section plane, and the dimension with respect to which the tomograms differ is the position of the time center of gravity on a time axis, and each tomogram is allocated to a different time center of gravity, and a color is allocated to the respective tomogram to be colored whose position on a color scale corresponds to the position of the time center of gravity of the respective tomogram on the time axis. In this case, all time changes are shown on the basis of color changes.
The term xe2x80x9ctime center of gravityxe2x80x9d means that point in time within the time interval from which the data underlying the tomogram are derived at which half of the data underlying the tomogram are acquired.
Continuous, chronologically successive tomograms, can be produced with the most recently produced tomogram being added at successive points in time to the series of tomograms and inserted, with a tomogram produced earlier from the series of tomograms being removed from the series, and with a number of colored tomograms being superimposed to form a resultant image at each of the following points in time. In this case, the resultant image represents a time window of defined length that is continuously updated. To make the resultant image easily interpretable, the colored tomograms that are superimposed to form a resultant image at each of the successive points include the most recently generated tomogram. Therefore, the point in time (among the aforementioned successive points in time) at which the superimposition takes place occurs after the point in time at which the most recently generated tomogram in the superimposed tomograms was generated.
The resultant images have particular diagnostic effect when the time centers of gravity of successive tomograms superimposed to form a resultant image have identical spacings from one another on the time axis.
The interpretation of a resultant image has a simple form when tomograms are produced that substantially correspond to parallel planes of section.
In a further, preferred embodiment of the invention, the tomograms are of different section planes, with the dimension with respect to which the tomograms differ being the position of the section plane on a location axis, and a color is allocated to the respective tomogram to be colored whose position on a color scale corresponds to the position of the section plane of the respective tomogram on the location axis. In this case, the position of the section planes on the location axis is presented as an additional dimension. This means that those regions that do not coincide in the superimposed tomograms are displayed colored in the resultant image. In contrast thereto, those regions that coincide in the superimposed tomograms are presented in the manner of a black-and-white image.
The term xe2x80x9csection planexe2x80x9d means the plane that represents the center plane of the slice of the examination subject scanned in the acquisition of the data underlying the tomogram.
As described above tomograms can be continuously produced, namely for positions following one another along the location axis, with the most recently produced tomogram at successive points in time being added to the series of tomograms and inserted, and the tomogram produced earliest is removed from the series of tomograms, and with the colored tomograms of the series being superimposed to form a resultant image at each of the successive points in time. In this case, the resultant image represents a section on the location axis having a defined length that is continuously updated. As above, for good interpretation of the resultant image a tomogram is used in the superimposition to form a resultant image at each of the successive points in time that is generated for a position along the location axis that lies following the position for which the most recently produced tomogram of the tomograms superimposed to form a resultant image was generated at the preceding point in time.
When the dimension for which the tomograms differ is the position of the section planes on the location axis, in a further embodiment of the invention sets of tomograms are produced in continuous succession for a number of different section planes that respectively have substantially the same time center of gravity as the respective tomograms of a set be colored and superimposed to form a resultant image, whereby a new, resultant image is respectively generated as soon as a new set of tomograms is present. This method is particularly suited for implementation on a CT apparatus having a multi-line detector, whereby each line of the detector can simultaneously generate one tomogram. The slice or slices in which a medical instrument, for example a punction needle, is located in the implementation of an intervention under CT monitoring can then be recognized on the basis of the color in the resultant image.
In order to be able to optimally utilize the information available in the form of tomograms, all tomograms in accordance with the invention are colored in the number of tomograms and all colored tomograms are superimposed.
The tomograms need not be directly produced but, in an embodiment of the invention, can be produced by scanning a volume of the examination subject, and the tomograms are determined from measured data acquired in the scanning, being determined with known methods of multi-planar reconstruction (MPR). This offers the advantage that arbitrary tomograms within the scope of the scanned volume can be utilized for the superimposition.
In a preferred embodiment of the invention, the tomograms are acquired with a CT apparatus, for example a spiral CT apparatus known from German OS 198 00 946, with the location axis corresponding to the system axis of the CT apparatus in the case where the additional dimension is the position of the section planes with respect to a location axis.
The CT apparatus as disclosed by U.S. Pat. No. 5,377,250, can have a detector system with a number of lines of detector elements. In this case in another, preferred embodiment of the invention that the tomograms are acquired with different lines of detector elements, with the positions of the section planes respectively corresponding to the tomograms on the location axis corresponding to the position of the respective line of detector elements with respect to the system axis of the CT apparatus. In this case, the tomograms to be superimposed can be simultaneously generated, i.e. with the same time center of gravity, even though they exhibit differently positioned section planes with respect to the system axis of the CT apparatus.


iPLEDGE Weaknesses: Is It Time to Address the Flaws?
The observance during acne follow-ups that information stored within iPLEDGE was discordant with medical charts prompted this study. To evaluate the information acquired and stored within iPLEDGE as it compares to medical charts with a goal of assessing the efficacy of iPLEDGE as a database. This is a multicenter retrospective chart review analyzing congruence and discrepancies between medical chart documentation and iPLEDGE data for all patients who received at least a single dose of isotretinoin from the primary investigators between January 2006 and November 2010. A total of 357 charts were analyzed. Overall congruence between medical chart documentation and iPLEDGE data was observed in only 73.1% of cases. The discrepancy (N=96) was due to a missed dose (prescription recorded in chart but not in iPLEDGE) in 81.4% of cases, or an addition (medication dispensed per iPLEDGE without corresponding chart documentation) in the remainder of cases. Of note, several charts had multiple discrepancies (N=249 total discrepancies). Retrospective chart review study. Given the large percentage of discordant data, our findings question the efficacy of the iPLEDGE system, which is designed to monitor every dispensed isotretinoin dose.