mainly for safety reasons , many patients and their families are looking for a seizure detection system that is efficient , comfortable , and easy to use .
this implies that such a system should have a high detection sensitivity ( detect as many seizures as possible ) and low false alarm rate ; it should be unobtrusive , wireless , and in case of daytime use practically unnoticeable ; and it should allow long - term use in a home situation without the presence of professionals . alarming for safety reasons or reassurance
is already used in fall detectors for the elderly , babyphones ( reacting to sound or movement ) , and glucose monitors for persons with diabetes , .
in addition to being used for alarming , a detection system could allow offline storage or online streaming of selected data , allowing follow - up on treatment efficacy or summoning emergency support , respectively .
our team is interested in the long - term observation of patients with epilepsy and detection of their seizures , .
the focus lies on those convulsive seizures that can be dangerous because of their intensity ( possible injuries ) , duration , or serious consequences such as brain damage , autonomic dysregulation , or suffocation .
clonic and clonic seizures with patients remaining in one place , their bed , mainly because of the need for supervision at night and because seizures often occur during sleep . as sensors need to be as unobtrusive as possible but still effective , small wireless accelerometers ( measuring the acceleration of movement ) are used and attached to the wrists and ankles of the patient .
the goal of this study was to test the efficacy of the varia ( video , accelerometry , and radar - induced activity recording ) system and algorithms for tonic  clonic and clonic seizures compared with that of semicontinuous monitoring by professional caregivers ( keeping an eye on multiple patients ) , as well as the independence , robustness , comfort , and user - friendliness of the system .
we therefore measure long term in a home environment without uncomfortable and stigmatizing electroencephalography ( eeg ) electrodes rather than in a video / eeg monitoring unit , and the system therefore needs to be able to store the data and to allow visual verification of detected events .
two patients were measured for one month in their rooms in an epilepsy center in flanders , belgium .
as they returned home for the weekends and as the caregivers needed to get acquainted with the recording system , they were measured for a mean of 12 nights per patient .
patient 1 is an 8-year - old girl with epilepsy e.c.i . and different types of seizures that typically last no more than 3  min : tonic  clonic seizures with or without faltering respiration , tonic seizures , and clonic seizures .
she also exhibits myoclonic jerks manifesting as ( head ) nods or facial contractions with eye deviation .
seizures are noticed based on semicontinuous video monitoring and when a scream or breathing problem is heard .
patient 2 is a 9-year - old boy with lesional epilepsy due to herpes encephalitis , who is kept in bed with a restraining belt around the waist .
clonic seizures of various duration , tonic , clonic , and myoclonic seizures and ( series of ) spasms .
the caregivers use a babyphone and semicontinuous video monitoring to keep an eye on him .
approval by the medical ethical commission of the antwerp university hospital , belgium and signed informed consent forms from all parents were obtained prior to inclusion in the study , which was performed in accordance with the 1964 declaration of helsinki .
the system uses video , accelerometry ( acm ) , and radar - induced activity recording and is therefore named varia .
it consists of an axis m1011 camera ( axis communications ab , sweden ) and a hygrosens rad - mod motion sensor based on the radar principle ( b  +  b thermo - technik gmbh , germany ) .
the camera and radar are attached to a tripod that is placed close to the patient 's bed .
the radar is added for detection of movements in the direction of the sensor and for measurement through sheets .
radar technology is also used for detection of falling , wandering , sitting , or getting up , so we expect it to detect large and slow epileptic and nonepileptic movements , also those caused by other persons in the room .
four shimmer sensors with integrated three - axis acm ( shimmer , ireland ) , streaming wireless ( bluetooth ) communication standards , are adjusted to allow recording of more than 10  h before batteries need recharging and are put in elastic bracelets worn around wrists and ankles . a laptop with a software application developed in labview ( national instruments corporation , us ) stores all movement data recorded by either camera , radar , or acm ( fig .
the technical aspects of the recording system will be published in more detail by bonroy et al . and are briefly described in the supplementary material .
clonic and clonic seizures based on a large database of video / electroencephalography ( eeg ) data were used .
results of a patient - specific ( algorithm trained only on data of the patient itself ) , a nonpatient - specific ( algorithm trained on data not including those of the patient itself ) , and a semipatient - specific ( algorithm trained on data also including those of the patient itself ) approach were compared with the notes of professional caregivers . for a full description on how to implement these methodologies in practice
professional caregivers were asked to write down all seizures the patients had , with time and description in a form of a clinical report .
they recognize epileptic seizures ( one month training and yearly tuition ) , are awake during the whole night , know the patients , and are semicontinuously watching on average four patients with active epilepsy in front of a video screen , only leaving if one of them or one of the other ( maximum ) twelve patients need care . the events detected with the algorithms
were visually inspected by a pediatric video / eeg specialist and a pediatric neurologist using the screening tool which is a graphical user interface developed in matlab ( mathworks inc . , us ) ( fig .
2 ) . the screening tool displays information on the number of movement events and on their duration .
the acm signals , a distribution graph , and the video images are depicted for each event . also , radar and video signals are shown , as well as the fraction ( percentage ) of movement within the event .
the graph at the lower left corner sets out amplitude of all events based on acm signals and length of all events based on the three detection modalities . in other words
, it shows the intensity and duration of the movement . after the application of each of the three developed seizure detection algorithms and during postprocessing , the following logical reasoning was used : rule 1 : a margin of error of 5  min has been taken into account for the seizure reporting . as the caregiver might write down the seizure after having attended to the patient , we assume the seizure may have been noticed 5  min before or after the actual time in the notes.rule 2 : only seizures longer than 10  s are considered as possibly dangerous and therefore candidates for the alarm system , hence shorter seizures are not counted when calculating detection performance.rule 3 : an alarm is set after 7  s of continuous positive detection .
clonic seizures is not typically detected by acm ; the tonic phase involves muscle contraction rather than limb movement.rule 4 : an alarm within 1  min after a previous one is ignored .
a future real - time alarm system should allow the system to be turned off by the caregiver when checking on an alarm , and 1  min is considered too short for the caregiver to have checked on and left the patient after the first alarm.rule 5 : alarms caused by the caregiver waking or handling the patient ( medication administration ) and installing ( evening ) or removing ( morning ) the system and acm are ignored .
again , at these times , the future real - time alarm system should be turned off by the caregiver .
rule 1 : a margin of error of 5  min has been taken into account for the seizure reporting . as the caregiver might write down the seizure after having attended to the patient , we assume the seizure may have been noticed 5  min before or after the actual time in the notes .
rule 2 : only seizures longer than 10  s are considered as possibly dangerous and therefore candidates for the alarm system , hence shorter seizures are not counted when calculating detection performance .
rule 3 : an alarm is set after 7  s of continuous positive detection .
clonic seizures is not typically detected by acm ; the tonic phase involves muscle contraction rather than limb movement .
rule 4 : an alarm within 1  min after a previous one is ignored .
a future real - time alarm system should allow the system to be turned off by the caregiver when checking on an alarm , and 1  min is considered too short for the caregiver to have checked on and left the patient after the first alarm .
rule 5 : alarms caused by the caregiver waking or handling the patient ( medication administration ) and installing ( evening ) or removing ( morning ) the system and acm are ignored .
again , at these times , the future real - time alarm system should be turned off by the caregiver .
true positives ( tp ) are detected seizures with the extra seizures detected by the algorithm but not the caregiver mentioned in between brackets .
false negatives ( fn ) are seizures reported by the caregiver but not detected by the algorithm . to calculate sensitivity , the formula tp  /  ( tp  +  fn ) was used .
false positives ( fp ) are detections that were not seizures , which in some occasions were multiple alarms in one movement event . false detection rate ( fdr ) is calculated as the number of fp per night .
two patients were measured for one month in their rooms in an epilepsy center in flanders , belgium .
as they returned home for the weekends and as the caregivers needed to get acquainted with the recording system , they were measured for a mean of 12 nights per patient .
patient 1 is an 8-year - old girl with epilepsy e.c.i . and different types of seizures that typically last no more than 3  min : tonic  clonic seizures with or without faltering respiration , tonic seizures , and clonic seizures .
she also exhibits myoclonic jerks manifesting as ( head ) nods or facial contractions with eye deviation .
seizures are noticed based on semicontinuous video monitoring and when a scream or breathing problem is heard .
patient 2 is a 9-year - old boy with lesional epilepsy due to herpes encephalitis , who is kept in bed with a restraining belt around the waist .
clonic seizures of various duration , tonic , clonic , and myoclonic seizures and ( series of ) spasms .
the caregivers use a babyphone and semicontinuous video monitoring to keep an eye on him .
approval by the medical ethical commission of the antwerp university hospital , belgium and signed informed consent forms from all parents were obtained prior to inclusion in the study , which was performed in accordance with the 1964 declaration of helsinki .
the system uses video , accelerometry ( acm ) , and radar - induced activity recording and is therefore named varia .
it consists of an axis m1011 camera ( axis communications ab , sweden ) and a hygrosens rad - mod motion sensor based on the radar principle ( b  +  b thermo - technik gmbh , germany ) .
the camera and radar are attached to a tripod that is placed close to the patient 's bed .
the radar is added for detection of movements in the direction of the sensor and for measurement through sheets .
radar technology is also used for detection of falling , wandering , sitting , or getting up , so we expect it to detect large and slow epileptic and nonepileptic movements , also those caused by other persons in the room .
four shimmer sensors with integrated three - axis acm ( shimmer , ireland ) , streaming wireless ( bluetooth ) communication standards , are adjusted to allow recording of more than 10  h before batteries need recharging and are put in elastic bracelets worn around wrists and ankles . a laptop with a software application developed in labview ( national instruments corporation , us ) stores all movement data recorded by either camera , radar , or acm ( fig .
the technical aspects of the recording system will be published in more detail by bonroy et al . and are briefly described in the supplementary material .
the algorithms developed by milosevic et al .   for tonic  clonic and clonic seizures based on a large database of video / electroencephalography ( eeg ) data were used .
results of a patient - specific ( algorithm trained only on data of the patient itself ) , a nonpatient - specific ( algorithm trained on data not including those of the patient itself ) , and a semipatient - specific ( algorithm trained on data also including those of the patient itself ) approach were compared with the notes of professional caregivers . for a full description on how to implement these methodologies in practice
professional caregivers were asked to write down all seizures the patients had , with time and description in a form of a clinical report .
they recognize epileptic seizures ( one month training and yearly tuition ) , are awake during the whole night , know the patients , and are semicontinuously watching on average four patients with active epilepsy in front of a video screen , only leaving if one of them or one of the other ( maximum ) twelve patients need care .
the events detected with the algorithms were visually inspected by a pediatric video / eeg specialist and a pediatric neurologist using the screening tool which is a graphical user interface developed in matlab ( mathworks inc . , us ) ( fig .
2 ) . the screening tool displays information on the number of movement events and on their duration .
the acm signals , a distribution graph , and the video images are depicted for each event . also , radar and video signals are shown , as well as the fraction ( percentage ) of movement within the event .
the graph at the lower left corner sets out amplitude of all events based on acm signals and length of all events based on the three detection modalities . in other words , it shows the intensity and duration of the movement . after the application of each of the three developed seizure detection algorithms and during postprocessing , the following logical reasoning was used : rule 1 : a margin of error of 5  min has been taken into account for the seizure reporting . as the caregiver might write down the seizure after having attended to the patient
, we assume the seizure may have been noticed 5  min before or after the actual time in the notes.rule 2 : only seizures longer than 10  s are considered as possibly dangerous and therefore candidates for the alarm system , hence shorter seizures are not counted when calculating detection performance.rule 3 : an alarm is set after 7  s of continuous positive detection .
clonic seizures is not typically detected by acm ; the tonic phase involves muscle contraction rather than limb movement.rule 4 : an alarm within 1  min after a previous one is ignored .
a future real - time alarm system should allow the system to be turned off by the caregiver when checking on an alarm , and 1  min is considered too short for the caregiver to have checked on and left the patient after the first alarm.rule 5 : alarms caused by the caregiver waking or handling the patient ( medication administration ) and installing ( evening ) or removing ( morning ) the system and acm are ignored .
again , at these times , the future real - time alarm system should be turned off by the caregiver .
rule 1 : a margin of error of 5  min has been taken into account for the seizure reporting . as the caregiver might write down the seizure after having attended to the patient , we assume the seizure may have been noticed 5  min before or after the actual time in the notes .
rule 2 : only seizures longer than 10  s are considered as possibly dangerous and therefore candidates for the alarm system , hence shorter seizures are not counted when calculating detection performance .
rule 3 : an alarm is set after 7  s of continuous positive detection .
clonic seizures is not typically detected by acm ; the tonic phase involves muscle contraction rather than limb movement .
rule 4 : an alarm within 1  min after a previous one is ignored .
a future real - time alarm system should allow the system to be turned off by the caregiver when checking on an alarm , and 1  min is considered too short for the caregiver to have checked on and left the patient after the first alarm .
rule 5 : alarms caused by the caregiver waking or handling the patient ( medication administration ) and installing ( evening ) or removing ( morning ) the system and acm are ignored .
again , at these times , the future real - time alarm system should be turned off by the caregiver .
true positives ( tp ) are detected seizures with the extra seizures detected by the algorithm but not the caregiver mentioned in between brackets .
false negatives ( fn ) are seizures reported by the caregiver but not detected by the algorithm . to calculate sensitivity , the formula tp  /  ( tp  +  fn ) was used . false positives ( fp ) are detections that were not seizures , which in some occasions were multiple alarms in one movement event
. false detection rate ( fdr ) is calculated as the number of fp per night .
monitoring for 15 nights for patient 1 and nine nights for patient 2 resulted in 9586 movement events . in this first validation , 322 events ( 3.4% or 15  min ) were discarded before algorithm application because the data of one or more accelerometers lasted less than 1  s or was completely missing . the results of the nonpatient - specific and semipatient - specific approaches can be found in table 1 .
the nonpatient - specific approach resulted in a sensitivity of 90% and fdr of one per night for patient 1 and a sensitivity of 33% and fdr of almost two per night for patient 2 .
the semipatient - specific approach gave somewhat better results with a sensitivity of 93% and fdr of a little over one per night for patient 1 and a sensitivity of 40% and fdr of one per night for patient 2 .
results of the patient - specific approach are not depicted as it was immediately clear that many seizures were missed , many false alarms occurred , and the approach was not stable in between trials ( the results depended on which data were used for algorithm training ) .
in this work , we validated algorithms for the detection of tonic  clonic and clonic epileptic seizures with a new ( wireless ) system using video , wireless accelerometry ( acm ) , and radar - induced activity recording , which is placed in a home environment for a prolonged period of time .
three methodologies were tested : patient - specific , nonpatient - specific , and semipatient - specific algorithms .
it seems that combining a large amount of data of many ( other ) patients as in the last two methodologies , instead of using a limited amount of data of one specific patient as in the first approach , yields better classification results .
the semipatient - specific algorithm gave the best results , including 13 extra seizures detected ( 31% ) and eight seizures missed ( 19% ) compared with professional caregivers ' observations .
of these eight , four were not even recorded in any of the movement data .
possible explanations include a temporary defect in the system , mistakes in reporting by the caregiver ( one seizure was reported at 5:20  a.m. but found at 6:20  a.m. ) , or short seizures involving only the head ( not attached to an acm ) .
the limitations of our setup are discomfort ( four accelerometers ) , noncontinuously recorded data ( only movement events ) , and lack of gold standard ( no electroencephalography or eeg ) .
they are not able to detect the tonic phase of tonic  clonic seizures ( which involves muscle contraction rather than limb movement ) , so if the clonic phase is too short , the seizure will be missed : the alarm is set after 7  s of positive detection , so if the clonic phase lasts 6  s and this whole part is detected , the alarm will not be set off . finally , asymmetric tonic 
clonic or clonic seizures during which the movements of the limbs are not synchronized and seizures with a low frequency of clonic jerks are often missed , since the spatial and frequency content of these seizures differ from the majority of seizures in the training set ( a large database of wired acm data synchronized with video / eeg ) . fig .
we compared the results of the semipatient - specific algorithm with those of the screening tool of bonroy et al .  
( submitted ) where the most intense and longest movement events were visually inspected for seizures using the same datasets and the graphical user interface of fig .
2 . the screening tool can be used before a diagnosis is made to quickly and retrospectively look at a night and report abnormal nocturnal behavior , including seizures . once the patient is diagnosed ( with epilepsy )
this implies that we have to reduce the false detection rate ( fdr ) and increase the sensitivity .
the comparison results , presented in table 2 , show an increase of sensitivity from 58% to 66% ( five seizures were missed , but 14 more were detected ) and a decrease of fdr per night from 15 to 0.7 . because the method of bonroy et al
. selected a percentage of  most abnormal events irrespective of the number of movements or seizures that night , this automatically resulted in more false positives , so comparison of fdr is not relevant .
moreover , 11 of the 16 false positives in our approach were due to a system failure ( broken channel or high - frequency noise ) , hence clustered alarms can be used to indicate this kind of events and reduce the number of false alarms .
other publications reported on body - attached accelerometry for tonic  clonic and clonic seizure detection .
lockman et al .   reached a sensitivity of 87% testing 6 patients with ( only ) 8 seizures ; however , patterson et al .   only obtained a sensitivity of 31%
it is difficult to compare with this study and obtained results , however , mainly because the gold standard video / eeg was not used to annotate the data .
furthermore , we did not review all video data ( only where the algorithm or caregiver indicated a seizure ) , and some of the seizures reported by the caregivers were not even found in the ( movement ) data .
although setup without eeg is less optimal for seizure detection performance , it is optimal for testing the independence and robustness of the system and the patient - friendliness of the system in a long - term home environment . also , the second part of the gold standard , namely video , is present in our system .
furthermore , a major advantage of our system is that it allows video storage for retrospective analysis . of the commercially available seizure detection systems reported by van de vel et al .
, some keep a log of the detected seizures : epiwatcher   stores the time and duration of up to 20 detections , smartwatch , ,   keeps the time , duration , and movement patterns , epicare ,   saves the time and duration of an unspecified number of detections , and the mobile phone application epdetect   logs the raw accelerometer data .
some studies report on video - based seizure detection ( so without acm ) , but a disadvantage is the difficulty to measure through bed linen , , . in order to improve the accuracy of the device
, we are convinced that a multimodal approach is needed , and literature research as well as user questioning has persuaded us that the most important signals to be monitored are movement ( often an early manifestation of seizures ) and heart rate changes ( indicating clinically relevant seizures and part of the pathophysiological mechanism leading to sudden unexpected death in epilepsy or sudep ) .
it is clear that the number of sensors should be reduced to one or two at the most .
attachment of a seizure detection device to the upper arm is less comfortable than to the lower arm   and can easily move when for example changing clothes or not tightly attached ; so for the moment , we will continue our research with wrist - attached devices .
detection of respiration changes is certainly useful , but these changes occur probably almost simultaneously with heart rhythm changes , respiration is a very slow signal , and heart rhythm changes are very easy to detect , which is why more studies focus on cardiac- than respiration - based detection , .
oxygen saturation measurement and electrodermal activity ( eda ) seem promising , but changes are detected more slowly than heart rhythm changes , , and eda is measured using electrodes ( as are ecg and emg ) , which cause potential skin irritation , and there is a risk of losing the signal due to electrodes falling off or a bad contact with the skin , .
furthermore , emg and respiration can not be measured at the wrist , and oxygen saturation can not be measured yet without adding a finger cuff , even though reflectance oximetry might be an option in the future .
all of these arguments lead us to focus our future research on movement detection by acm in combination with heart rate detection by ppg ( photoplethysmography ) .
the algorithm should be robust and independent of the number and functionality of used sensors , so that there is no rejection of the data , and to be able to use our future device patient - independently , we will be concentrating on adaptive ( learning ) algorithms .
not only efficacy of the device will be important but also comfort ( including the reduction of sensors ) , user - friendliness ( less setup steps ) , and therapeutic ( e.g. , medication changes ) and social ( e.g. , quality of life including the impact of false alarms ) impact .
therapeutic impact can be examined by using the logging function of a seizure detection device , but in order to look into social impact , the device obviously needs to allow online use ( with real - time alarming ) .
the latter brings along additional challenges such as the need for fast computation time , low energy consumption , large storage capacity , and miniaturization of the device , which we need to address .
finally , it would be interesting to compare the performance with that of commercially available seizure detection bracelets using accelerometry , such as smartwatch or epicare free , also tested in a home environment .
this study demonstrates the first clinical results of an algorithm for the detection of tonic  clonic and clonic seizures tested on two patients with epilepsy monitored in a long - term home environment with the varia system ( video , accelerometry , and radar - induced activity recording ) developed by our team .
compared with the professional caregivers ' observations , a mean sensitivity of 61.97% and false detection rate of 1.44 per night were obtained with the nonpatient - specific approach and a mean sensitivity of 66.87% and false detection rate of 1.16 per night with the semipatient - specific approach .
we believe that , while privacy protection needs to be taken into account , the addition of video to accelerometry allows a seizure detection system not only to alarm the family or ( informal ) caregiver but also allows retrospective ( offline ) or emergency ( online ) verification of detected events .
much work remains to develop a multimodal , learning , comfortable , and user - friendly device .
 purposethe aim of our study was to test the efficacy of the varia system ( video , accelerometry , and radar - induced activity recording ) and validation of accelerometry - based detection algorithms for nocturnal tonic  clonic and clonic seizures developed by our team.methodswe present the results of two patients with tonic  clonic and clonic seizures , measured for about one month in a home environment with four wireless accelerometers ( acm ) attached to wrists and ankles . 
 the algorithms were developed using wired acm data synchronized with the gold standard video-/electroencephalography ( eeg ) and then run offline on the wireless acm signals . 
 detection of seizures was compared with semicontinuous monitoring by professional caregivers ( keeping an eye on multiple patients).resultsthe best result for the two patients was obtained with the semipatient - specific algorithm which was developed using all patients with tonic  clonic and clonic seizures in our database with wired acm . 
 it gave a mean sensitivity of 66.87% and false detection rate of 1.16 per night . 
 this included 13 extra seizures detected ( 31% ) compared with professional caregivers ' observations.conclusionwhile the algorithms were previously validated in a controlled video / eeg monitoring unit with wired sensors , we now show the first results of long - term , wireless testing in a home environment . 
Introduction
Methods
Patients
Measurement
Analysis
Results
Discussion
Conclusion
Supplementary data
Conflict of interest