# MHDeep: Mental Health Disorder Detection System based on Body-Area and Deep
Neural Networks

Shayan Hassantabar, Joe Zhang, Hongxu Yin, and Niraj K. Jha  This work was
supported by NSF under Grant No. CNS-1907381. Shayan Hassantabar, Joe Zhang,
Hongxu Yin, and Niraj K. Jha are with the Department of Electrical
Engineering, Princeton University, Princeton, NJ, 08544 USA,
<EMAIL_ADDRESS>
###### Abstract

Mental health problems impact quality of life of millions of people around the
world. However, diagnosis of mental health disorders is a challenging problem
that often relies on self-reporting by patients about their behavioral
patterns and social interactions. Therefore, there is a need for new
strategies for diagnosis and daily monitoring of mental health conditions. The
recent introduction of body-area networks consisting of a plethora of accurate
sensors embedded in smartwatches and smartphones and edge-compatible deep
neural networks (DNNs) points towards a possible solution. Such wearable
medical sensors (WMSs) enable continuous monitoring of physiological signals
in a passive and non-invasive manner. However, disease diagnosis based on WMSs
and DNNs, and their deployment on edge devices, such as smartphones, remains a
challenging problem. To this end, we propose a framework called MHDeep that
utilizes commercially available WMSs and efficient DNN models to diagnose
three important mental health disorders: schizoaffective, major depressive,
and bipolar. MHDeep uses eight different categories of data obtained from
sensors integrated in a smartwatch and smartphone. These categories include
various physiological signals and additional information on motion patterns
and environmental variables related to the wearer. MHDeep eliminates the need
for manual feature engineering by directly operating on the data streams
obtained from participants. Since the amount of the data is limited, MHDeep
uses a synthetic data generation module to augment real data with synthetic
data drawn from the same probability distribution. We use the synthetic
dataset to pre-train the weights of the DNN models, thus imposing a prior on
the weights. We use a grow-and-prune DNN synthesis approach to learn both the
architecture and weights during the training process. We use three different
data partitions to evaluate the MHDeep models trained with data collected from
74 individuals. We conduct two types of evaluations: at the data instance
level and at the patient level. MHDeep achieves an average test accuracy,
across the three data partitions, of 90.4%, 87.3%, and 82.4%, respectively,
for classifications between healthy and schizoaffective disorder instances,
healthy and major depressive disorder instances, and healthy and bipolar
disorder instances. At the patient level, MHDeep DNNs achieve an accuracy of
100%, 100%, and 90.0% for the three mental health disorders, respectively,
based on inference that uses 40, 16, and 22 minutes of data from each patient.

###### Index Terms:

Body-area network; deep neural network; disease diagnosis; health monitoring;
mental health disorders; wearable medical sensors; synthetic data generation.

##  1 Introduction

Mental health problems impact around $20\%$ of the world population [1]. They
may negatively affect a person’s mind, emotions, behavior, and even physical
health. Mental health issues may include various disorders like bipolar,
depression, schizophrenia, and attention-deficit hyperactivity, to name but a
few. These disorders do not only affect adults, but children and adolescents
may suffer from them as well [2]. Moreover, patients with serious mental
health issues have a higher risk of morbidity due to physical health problems.
Depression, for example, can lead to disability and increases the risk of
suicidal thoughts and attempts.

In order to understand the mental health condition of the patient and provide
suitable patient care, early detection is essential. However, this remains a
public health challenge. While many other diseases can be diagnosed based on
specific medical tests and laboratory measurements, detection of mental health
problems mainly relies on self-reports and responses to specific
questionnaires designed for identifying certain patterns of behavior and
social interactions. Hence, to address this challenge, novel detection
strategies are needed.

There has been recent interest in employing machine learning (ML) to detect
mental health conditions [3]. Neural networks are popular machine learning
models that use nonlinear computations to make inferences from large datasets.
Thus, they have started being deployed in the smart healthcare domain [4, 5,
6, 7, 8, 9].

In previous studies, two main data sources for deep learning based analysis of
mental health have been clinical data and social media usage data. The former
includes studies that use neuro-image data for detecting various mental health
disorders [10], electroencephalogram (EEG) data to study brain disorders [11],
and analysis of electronic health records (EHR) to study mental health
problems [12]. Moreover, social media usage patterns have been used to predict
personal traits of the user. As a result, several recent works focus on
exploiting such patterns to detect psychiatric illness [13].

Although the above works have demonstrated the promise of using machine
learning in identifying mental health disorders, daily mental health
monitoring is still a challenge. Since mental health condition treatment
delays may lead to negative outcomes, potentially even loss of life, it is
desirable to have immediate and pervasive mental health detection. This is the
motivation behind our mental health detection system, MHDeep. As shown in Fig.
1, MHDeep relies on physiological data collected using various WMSs. WMSs can
be used to continuously monitor the physiological signals of the wearer
throughout the day. This enables constant tracking of the health conditions of
the user. MHDeep uses various sensors embedded in smartwatches and
smartphones. For training purposes, the collected physiological data are
processed to obtain a comprehensive dataset. MHDeep combines data from WMSs
with the inference capabilities of deep neural networks (DNNs) to directly
extract mental health condition from the physiological signals. These
inferences can be communicated to a health server that is accessible to the
physician. This has the potential to enhance to ability of the physician to
intervene quickly when mental health conditions deteriorate.

Difficulty of data collection and labeling limits the amount of available
data. Hence, MHDeep uses synthetic data drawn from the same probability
distribution as real data to augment the dataset. It also leverages a grow-
and-prune DNN synthesis approach [14, 15] to train accurate and
computationally efficient neural network models to detect the mental health
condition of the user.

The major contributions of this article are summarized next.

  * •

We demonstrate an easy-to-use, accurate, and pervasive mental health disorder
detection system, called MHDeep. MHDeep combines physiological signals
collected from WMSs with the prediction power of DNNs to detect three main
mental health disorders: major depressive, schizoaffective, and bipolar.
Unlike many other approaches for detecting mental health problems, MHDeep does
not rely on any self-reports from the user.

  * •

We do an extensive search to extract the most appropriate set of data
categories for detecting each of the three mental health disorders.

  * •

MHdeep relies on a synthetic data generation module to alleviate the concerns
arising from unavailability of large datasets. It uses a grow-and-prune DNN
synthesis approach to improve the accuracy of the DNNs while reducing their
computational costs.

  * •

We demonstrate the performance, accuracy, and feasibility of MHDeep through
extensive evaluations.

Figure 1: MHDeep mental health disorder detection system

The rest of the article is organized as follows. Section 2 presents background
information on various works related to MHDeep. Section 3 explains the MHDeep
framework thoroughly. Section 4 provides implementation details. Section 5
presents experimental evaluations. Section 6 provides a short discussion
related to this work. Finally, Section 7 concludes the article.

##  2 Background

In this section, we first provide background information on various mental
health disorders and how they affect patient lives. Next, we discuss various
methods for identifying mental health conditions based on machine learning. We
also discuss some of the related work on synthesizing efficient neural network
models. Finally, we discuss WMSs and their applications to various disease
diagnosis frameworks.

###  2.1 Mental health disorders and their impact

Mental health conditions can affect a patient’s thinking, feeling, and
behavior. They may have a deep impact on the daily life of the person and
affect their ability to adequately perform in society. There are hundreds of
different mental illnesses [16]. We discuss the three mental health disorders
that we target in this work: bipolar, major depressive, and schizoaffective.

Bipolar disorder can cause a dramatic shift in a person’s mood, energy, and
behavior. It is characterized by experiences of alternating episodes of manic
and depressed states. Major depressive disorder may present different symptoms
like loss of interest, sleep disturbance, change in appetite, and feeling of
fatigue. Schizoaffective disorder is characterized by various symptoms of
schizophrenia such as episodes of hallucinations and delusions. It may also
present other symptoms such as disorganized thinking, depressed mood, and
manic behavior.

Apart from various conditions that these mental health disorders may cause,
stereotypes related to mental health seem to still be widely prevalent in
society, not just among uninformed people but even among well-trained
professionals [17]. These stereotypes often lead to social and employment
discrimination [18] and poor treatment of physical health problems [19].

###  2.2 Deep learning for mental health

Deep learning has been recently used to better understand and detect mental
health problems. Deep learning approaches have been applied to various types
of data: mainly clinical data and social media usage data [20]. The three
types of clinical data used in these works are neuroimage data, EEG data, and
EHR data.

Several studies demonstrate the effectiveness of neuroimages in detecting
neuropsychiatric disorders [10]. Two types of neuroimage data used in such
works are functional magnetic resonance imaging (fMRI) and structural magnetic
resonance imaging (sMRI). fMRI measures brain activity by monitoring blood
oxygenation and flow in response to neural activity. sMRI examines the anatomy
and pathology of the brain. Deep belief networks have been used to detect the
presence of attention-deficit hyperactivity disorder (ADHD) using fMRI and
sMRI data [21, 22]. These data types have also been used to detect
schizophrenia [23, 24]. Depression has been detected using time-series fMRI
data using convolutional neural networks (CNNs) and autoencoders [25]. EEG is
another source of data for studying brain disorders. For example, CNN based
feature extraction from EEG data has been used to detect depression [11]. EHR
is a collection of patient-centered records and includes both structural data
such as laboratory reports and unstructured data such as clinical and
discharge notes. Since EHR data is a collection of longitudinal records,
recurrent neural networks (RNNs) have been used to distill information from
them. Pham et al. [26] use RNN architectures to predict future outcomes of
depressive episodes. Unstructured clinical notes have been analyzed with deep
learning-based models to detect depression [12].

Social media usage data have also proved their usefulness in identifying
psychiatric illness. Birnbaum et al. [13] investigate Facebook messages and
patterns of sharing images on social media to distinguish among healthy
individuals, individuals with a schizophrenia spectrum disorder, and
individuals with mood disorders. Other works have used DNNs with textual data
and image data shared on social media platforms to detect stress [27],
depression [28], and risk of suicide [29].

###  2.3 Efficient neural network synthesis

Next, we summarize the main approaches to synthesis of compact DNN models.
Conventional synthesis methods are based on the use of efficient building
blocks. For example, MobileNetV2 [30] leverages inverted residual blocks to
reduce model size and computations significantly. Wu et al. [31] use shift-
based operations rather than convolution layers to significantly reduce
computational costs of the model. The main drawback of such approaches is the
need for considerable design insight and trial-and-error process to design
such efficient building blocks. Network compression is another approach to
design of efficient models. It removes the need for design insights. Network
pruning is a widely used method that eliminates weights or filters that do not
enhance model performance. Han et al. [32] have shown the effectiveness of
pruning in removing redundancy in CNNs and multilayer-perceptron
architectures. Grow-and-prune DNN synthesis uses network growth followed by
network pruning in an iterative process to improve model performance while
ensuring its compactness [15, 14].

Another recent approach relies on the use of reinforcement learning (RL) to
search for DNN architectures in an automated flow. It is known as neural
architecture search (NAS) [33]. NAS generally uses a controller, e.g., an RNN,
to iteratively generate candidate architectures in the search process. The RL
controller is improved based on candidate performance. As an example, MnasNet
[34] uses an RL-based approach to develop efficient DNNs for mobile platforms.
However, the downside of the RL-based NAS approach is that it is
computationally intensive. FBNet [35] uses the Gumbel softmax function to
optimize weights and connections using a single objective function. NEAT [36]
uses evolutionary algorithms to generate optimized and increasingly complex
architectures over multiple generations. Combining efficient evolutionary
search algorithms with various performance predictors, e.g., for accuracy,
energy, and latency, is another approach for synthesizing accurate yet compact
CNNs/DNNs [37, 38].

###  2.4 Wearable medical sensors

Due to recent developments in low-power sensor design and efficient wireless
communications, battery-powered WMSs are becoming ubiquitous. More than 123
million WMSs were sold worldwide in 2018\. This number is projected to grow to
1 billion by the end of 2022 [39]. WMSs can track different aspects of human
health including heart rate, body/skin temperature, respiration rate, blood
pressure, EEG, electrocardiogram (ECG), and Galvanic skin response (GSR) [40].
Furthermore, the number of physiological signals that can be measured using
WMSs keeps growing every year.

WMSs have begun to be used in many smart healthcare applications. CodeBlue
[41] is a sensor network that collects vital health signs and transmits them
to the healthcare provider. MobiHealth [42] is a WMS based body-area network
(BAN) that realizes an end-to-end mobile health monitoring platform. Yin et
al. [7] use WMSs for pervasive diagnosis of Type-I and Type-II diabetes.
CovidDeep [4] is a WMS-based framework for quick detection of SARS-
CoV-2/COVID-19.

For data collection in MHDeep, we use an Empatica E4 smartwatch [43] to record
a subset of patient’s physiological signals. It is a wearable wireless device
designed for comfortable, continuous, and real-time data acquisition. We also
use a smartphone to simultaneously record signals related to motion
information and environmental variables. Since the DNNs developed for
diagnosing various mental health conditions can reside on the smartphone, use
of a smartwatch/smartphone based BAN can enable accurate, yet convenient,
disease diagnosis and continuous healthcare monitoring.

##  3 Methodology

In this section we describe various parts of the MHDeep framework. First, we
give an overview of our approach. Then, we discuss the data collection and
preparation process, synthetic data generation, and grow-and-prune DNN
synthesis.

###  3.1 The MHDeep framework

We illustrate the MHDeep framework in Fig. 2. The input data are derived from
the physiological signals collected using various WMSs in the smartwatch and
smartphone in a non-invasive, passive, and efficient manner. The list of
collected data streams include GSR, skin temperature (ST), inter-beat interval
(IBI), and 3-way acceleration from the smartwatch. In addition, some
information related to motion patterns of the user and ambient information are
collected using smartphone sensors. This includes ambient temperature,
gravity, acceleration, and angular velocity. After sensor data collection, the
collected signals are synchronized, aggregated, and merged into a
comprehensive data input for subsequent analysis. To enhance the accuracy of
subsequent analysis and improve noise tolerance, we normalize the data. The
process of data collection and preparation is discussed in more detail in
Section 3.2. When the size of the training dataset is small, it can be useful
to generate a synthetic dataset from the same probability distribution as the
real training dataset. MHDeep leverages Gaussian mixture model (GMM) based
density estimation to generate the synthetic data. Then, it uses grow-and-
prune DNN synthesis to generate inference models that are both accurate and
computationally efficient. Section 3.3 discusses the MHDeep DNN synthesis
process in detail. MHDeep generates DNN architectures that are efficient
enough to be deployed on the edge devices such as smartphones or smartwatches.
Section 3.4 discusses the inference process of the MHDeep DNNs for diagnosis
and daily monitoring of mental health issues.

Figure 2: Schematic diagram of the MHDeep framework.

###  3.2 Data collection and preparation

We collected WMS data from a total of 74 adult participants at the Hackensack
Meridian Health Carrier Clinic, Belle Mead, New Jersey. The participants were
diagnosed by medical professionals at the clinic. The 74 participants
comprised the following four categories: 25 healthy participants (no mental
health disorder), 23 participants with bipolar disorder, 10 participants with
major depressive disorder, and 16 participants with schizoaffective disorder.
The experimental procedure for data collection and analysis was approved by
the Institutional Review Board of Princeton University. The physiological
signals of the participants were captured by a commercially-available Empatica
E4 smartwatch [43] and a Samsung Galaxy S4 smartphone, as shown in Fig. 3. We
summarize all the data types collected in this study in Table I. The
physiological signals are derived from WMSs embedded in the smartwatch. They
include GSR that measures sympathetic nervous system arousal, IBI that
indicates the heart rate, ST that provides skin temperature readings, and
$3$-axis accelerometer (Acc-W) that measures acceleration in the $X$, $Y$, and
$Z$ directions. Ambient and motion information is captured using sensors in
the smartphone that include ambient temperature (Temp), gravity (Grav),
acceleration (Acc-P), and angular velocity (Vel). It is worth mentioning that
the acceleration sensors in the smartphone and smartwatch have different
sampling rates, and capture different motion information.

Before data collection, all participants are informed about the experiment and
are asked to sign a consent form. The data collection setup consists of
placing the Empatica E4 smartwatch on the wrist of the participant’s non-
dominant hand and placing the Samsung Galaxy S4 smartphone in the opposite
front pocket. Data collection lasts around 1.5 hours, during which time the
participant is allowed to freely move around in the room with their on-body
devices. During this time, the smartwatch and smartphone continuously record
and store the physiological signals and ambient/motion information. At the end
of the data collection period, we remove the smartwatch from the patient’s
wrist and the smartphone from their pocket. We use the Empatica E4 Connect
portal for smartwatch data retrieval. We use a private Android application to
download the smartphone data streams. All of the recorded data are timestamped
at the time of sampling.

Next, we preprocess the dataset for use in DNN training. We first synchronize
the smartwatch and smartphone data streams for each participant. This is
necessary since the WMS data streams may vary in their start times and
frequencies. Then, we divide the data for each participant into 15-second
windows. Each 15-second window of the combined smartwatch/smartphone data
constitutes one data instance. There is no time overlap between data
instances. To obtain each data instance, we flatten and concatenate the data
within the same time window from both the smartwatch and smartphone. This
results in a feature space of dimension 2325. The smartwatch (smartphone)
contributes 1575 (750) features. All the smartphone sensors have a sampling
rate of 5Hz. In addition, the smartwatch sensors include one data stream at
32Hz, two data streams at 4Hz, and one data stream at 1Hz.

Figure 3: An Empatica E4 smartwatch (left) and Samsung Galaxy S4 smartphone (right) used in the data collection process. TABLE I: Data types collected in the MHDeep framework Data type | Sampling rate (Hz) | Data source
---|---|---
Galvanic skin response ($\mu$S) | 4 | Smartwatch
Skin temperature (${}^{\circ}C$) | 4 | Smartwatch
Inter-beat interval ($ms$) | 1 | Smartwatch
Acceleration ($x,y,z$) | 32 | Smartwatch
Ambient temperature (%) | 5 | Smartphone
Gravity ($x,y,z$) | 5 | Smartphone
Acceleration ($x,y,z$) | 5 | Smartphone
Angular velocity ($x,y,z$) | 5 | Smartphone

For each classification task, since the number of individuals in each of the
four categories is small, we created three different data partitions for
evaluation. The data instances extracted from the individuals in each of the
four groups (healthy, schizoaffective, depressive, and bipolar) were divided
into three sets: training, validation, and test. To evaluate the models on
different unseen patients, data instances included in the training,
validation, and test sets came from different individuals, i.e., no individual
contributed data to more than one of these sets. Among the healthy
participants, for each of the three data partitions, data instances from 15
individuals (60% of the healthy participants) are selected for the training
set, from 5 individuals (20% of the healthy participants) for the validation
set, and from the remaining 5 individuals (20% of the healthy participants)
for the test set. For individuals with bipolar disorder, the training,
validation, and test sets contain data instances from 13, 5, and 5
participants, respectively. Among the participants who had major depressive
disorder, data instances from 6 participants are selected for the training set
and from 2 participants each for the validation and test sets. For individuals
with schizoaffective disorder, the training, validation, and test sets include
data instances from 10, 3, and 3 participants, respectively.

We create the final dataset for each binary classification task (healthy vs.
the mental health disroder) by combining the training, validation, and test
sets of the two classes involved in that task. We use SMOTE [44] to up-sample
data instances from the minority class. Table II shows the number of instances
for each of the classification tasks for all three data partitions.

TABLE II: Details of various datasets (MDD: major depressive disorder). Classification task | Data partition | Training instances (#individuals) | Validation instances (#individuals) | Test set (#individuals)
---|---|---|---|---
Healthy vs. Bipolar | 1 | 14828 (28) | 3582 (10) | 3754 (10)
Healthy vs. MDD | 1 | 14828 (21) | 2414 (7) | 2515 (7)
Healthy vs. Schizo. | 1 | 14828 (25) | 2789 (8) | 3047 (8)
Healthy vs. Bipolar | 2 | 13330 (28) | 3922 (10) | 3582 (10)
Healthy vs. MDD | 2 | 13330 (21) | 3266 (7) | 2414 (7)
Healthy vs. Schizo. | 2 | 13330 (25) | 3773 (8) | 2789 (8)
Healthy vs. Bipolar | 3 | 12054 (28) | 4102 (10) | 3922 (10)
Healthy vs. MDD | 3 | 12054 (21) | 3088 (7) | 3266 (7)
Healthy vs. Schizo. | 3 | 12054 (25) | 3522 (8) | 3773 (8)

###  3.3 MHDeep DNN synthesis

Fig. 4 shows the DNN architectures used in the MHDeep framework. The
architectures receive the input data at the bottom and make their diagnostic
decisions at the top. For the healthy vs. major depressive disorder and
healthy vs. schizoaffective disorder binary classification tasks, the DNN
architecture has four layers with a width of 256, 128, 128, and 2,
respectively. For the healthy vs. bipolar disorder binary classification task,
we use a DNN architecture with five layers with a width of 256, 128, 64, 32,
and 2, respectively. We selected these DNN architectures by verifying the
performance of various DNNs (with different numbers of layers and number of
neurons per layer) on the validation set and picking the best performing one.
These architectures are initially fully-connected.

Figure 4: Architecture of MHDeep DNNs for: (a) healthy vs. major depressive
disorder and healthy vs. schizoaffective disorder, and (b) healthy vs. bipolar
disorder.

We then subject the fully-connected DNNs to three sequential steps: (i)
synthetic data generation to mimic the distribution of the real training data,
(ii) pre-training of the DNN architectures with the synthetic data, and (iii)
grow-and-prune DNN synthesis to reduce the redundancy of the DNN model while
improving its performance. Next, we discuss each step in more detail.

  1. 1.

Synthetic data generation: In this step, we generate a synthetic dataset that
mimics the probability distribution of the real training dataset. Fig. 5
illustrates the synthetic data generation process. This approach was first
presented in [45] to alleviate the need for large datasets to train DNN
architectures. We first use GMM to estimate the density of the training
dataset. We optimize the number of mixtures in the GMM by monitoring the
likelihood of validation data. We choose the number of components that
maximizes the following criterion:

$N^{*}=\underset{N}{\arg\max}\left(\texttt{GMM}_{N}(x).\texttt{score}(X_{validation})\right)$

Finally, we train the optimal GMM model with $N^{*}$ mixtures on the
combination of the training and validation datasets. By sampling this model,
we are able to generate the synthetic data:

$X^{*}=GMM_{N^{*}}(X_{total}).sample()$

For our experiments, we generate 100,000 samples as synthetic data. The final
step is labeling of the synthetic dataset. We use a traditional machine
learning model for this purpose. We evaluate various models, e.g., the support
vector machine and random forest models based on different splitting criteria
(such as Gini index and entropy), and different depth limits on the decision
trees, on the validation set. The model with the highest accuracy is used to
label the synthetic data.

  2. 2.

DNN pre-training: In this step, we use the labeled synthetic data to obtain a
prior on the weights of the DNN architecture by pre-training them. The
intuition behind this step is that pre-training the DNN provides a suitable
inductive bias to the DNN. As a result, we can commence on the final training
stage with a better weight initialization. Therefore, it alleviates the need
for large training datasets.

  3. 3.

Grow-and-prune DNN synthesis: MHDeep uses a grow-and-prune DNN synthesis
paradigm to train the models. Algorithm 1 summarizes this process. It uses a
mask-based approach. For each weight matrix, there is an associated binary
mask of the same size that is used to disregard dormant connections in the
architecture. It applies magnitude-based pruning and full growth to fully-
connected DNNs iteratively. For magnitude-based pruning, a hyperparameter
$\alpha$ is used to depict the pruning ratio. We prune a connection if and
only if its weight is in the lowest $\alpha*100$ percent of the weights in its
associated layer. Finally, for the pruned connections, we set the weight and
its binary mask both to $0$. Since connection pruning is an iterative process,
we retrain the network to recover its performance after each pruning
iteration. In our experiments, after each architecture changing operation, we
train the DNN for 20 epochs. In addition, we set the number of iterations to
$5$.

Algorithm 1 Grow-and-prune synthesis

0: Pre-trained DNN architecture; iteration number $numIterations$; weight
matrix $W\in R^{M\times N}$; mask matrix $Mask$ of the same dimension as the
weight matrix; $\alpha$: pruning ratio

best-validation-acc = 0

for all layers in the DNN do

$t=(\alpha\times MN)^{th}$ largest element in $\left|W\right|$

for all $w_{ij}$ do

if $\left|w_{ij}\right|<t$ then

$Mask_{ij}=0$

end if

end for

$W$ = $W\otimes Mask$

end for

Train DNN for given #epochs

validation-acc = evaluate DNN on validation set

if validation-acc >best-validation-acc then

best-validation-acc = validation-acc

Save the DNN

end if

for $numIterations$ do

for all layers in the DNN do

$Mask_{[1:M,1:N]}=1$

end for

for all layers in the DNN do

$t=(\alpha\times MN)^{th}$ largest element in $\left|W\right|$

for all $w_{ij}$ do

if $\left|w_{ij}\right|<t$ then

$Mask_{ij}=0$

end if

end for

$W$ = $W\otimes Mask$

end for

Train DNN for given #epochs

validation-acc = evaluate DNN on validation set

if validation-acc >best-validation-acc then

best-validation-acc = validation-acc

Save the DNN

end if

end for

Best architecture with the weight and mask matrices

Figure 5: Schematic diagram of the MHDeep synthetic data generation process.

###  3.4 MHDeep inference process

The trained DNN models can be used for diagnosis or daily monitoring of the
mental state of the user based on collection of physiological signals and
ambient information during the day. The collected data streams are processed
based on the step explained in Section 3.2. We feed the processed data to the
MHDeep DNN that predicts the mental health condition of the user. When the
model predicts that the user is experiencing an episode of mental health
disorder, this information can be sent to a physician for early treatment.

##  4 Implementation Details

We have implemented the data processing and preparation parts of the MHDeep
framework in Python and the MHDeep DNN synthesis framework in PyTorch. We use
the Nvidia Tesla P$100$ data center accelerator for DNN training. We use the
cuDNN library to accelerate GPU processing. For training, we use a stochastic
gradient descent (SGD) optimizer, with a learning rate of $5$e-$4$ and a batch
size of $256$. We use 100,000 synthetic data instances to pre-train the
network architecture. In the grow-and-prune synthesis phase, we train the
network for $20$ epochs each time the architecture changes. We use an SGD
optimizer, with an initialized learning rate of $1$e-$4$ that we halve in each
succeeding iteration. We apply network-changing operations over five
iterations.

##  5 Evaluation

In this section, we analyze the performance of MHDeep DNN models for
diagnosing three mental health disorders. This entails three binary
classifications: (i) schizoaffective disorder vs. healthy individuals, (ii)
major depressive disorder vs. healthy individuals, and (iii) bipolar disorder
vs. healthy individuals. For each classification task, we use three different
data partitions, each partition with data instances obtained from different
individuals in the training, validation, and test sets.

The MHDeep DNN models are evaluated with four different metrics: test
accuracy, false positive rate (FPR), false negative rate (FNR), and F1 score.
Accuracy measures overall classification performance. It is simply the ratio
of all the correct predictions on the test data instances and the total number
of such instances. FPR and FNR measure how often healthy individuals are
declared to have the corresponding mental health condition and vice versa,
respectively.

First, we report the performance of the MHDeep DNN models in detecting each of
the three mental health disorders at the data instance level. Next, we
evaluate the accuracy of the models in detecting mental health disorders at
the patient level.

###  5.1 MHDeep performance evaluation at the data instance level

We first analyze the performance of the three binary classifiers. We begin by
training DNN models on features obtained from subsets of the eight data
categories presented in Table I. We analyze all the subsets of the eight data
categories and report results for the top models. Since there are eight data
categories, there are 256 subsets, with one being the null subset. We
evaluated the remaining 255 subsets. This helps distinguish the impact of each
data category and to find the most effective combination of categories for
each classification task.

Table III shows the results for classification between healthy and
schizoaffective data instances. The best data category subset, in this case,
achieves an average test accuracy of 90.4%. We also report test accuracy, FPR,
FNR, and F1 score for each of the three data partitions. The model reaches the
highest test accuracy of 93.3% on the second data partition. Furthermore, for
the healthy instances, the top model achieves a low average FPR of 6.5%,
demonstrating its effectiveness in avoiding false alarms. For the
schizoaffective instances, the model achieves an average FNR of 16.9%,
indicating reasonable effectiveness in raising alarms when schizoaffective
disorder does occur. We also report the number of parameters (#params) and
floating-point operations (FLOPs) required for each model. We also compare
#params and FLOPs of the models with those of the fully-connected baselines.
As can be seen, using the grow-and-prune DNN synthesis approach enables us to
reduce both #params and FLOPs, leading to a reduction in memory and
computational requirements.

We present the results for classification between healthy and major depressive
disorder instances in Table IV. The data category subset with the best
performance achieves an average test accuracy of 87.3%. This model achieves
the highest accuracy of 91.2% on the second data partition. It achieves an
average FPR (FNR) of 6.8% (29.3%).

Table V presents the results for classification between healthy and bipolar
disorder instances. In this case, the model trained on the best data category
subset achieves an average test accuracy of 82.4%, with an FPR (FNR) of 16.7%
(20.7%).

TABLE III: Test accuracy, FPR, FNRs, and F1 score (all in %) for top data categories for classification between healthy and schizoaffective disorder data instances Data category | Data partition | #Params (compression) | FLOPs (compression) | Acc. | FPR | FNR | F1 Score
---|---|---|---|---|---|---|---
(Acc-P, Temp, Vel, Acc-W, GSR, IBI) | 1 | 275.0k (2.1$\times$) | 549.5k (2.0$\times$) | 91.2 | 11.4 | 5.8 | 89.5
| 2 | 300.0k (1.9$\times$) | 599.5k (1.9$\times$) | 81.4 | 6.6 | 37.8 | 72.0
| 3 | 275.0k (2.1$\times$) | 549.5k (2.0$\times$) | 87.3 | 2.4 | 34.1 | 84.2
Average |  |  |  | 86.6 | 6.8 | 25.9 | 81.9
(Acc-P, Temp, Vel, Acc-W, GSR) | 1 | 200.0k (2.8$\times$) | 399.5k (2.8$\times$) | 90.5 | 13.0 | 4.4 | 89.3
| 2 | 300.0k (1.9$\times$) | 599.5k (1.8$\times$) | 93.3 | 4.0 | 13.3 | 89.8
| 3 | 250.0k (2.3$\times$) | 499.5k (2.2$\times$) | 87.5 | 2.5 | 32.9 | 77.9
Average |  |  |  | 90.4 | 6.5 | 16.9 | 85.7
(Acc-P, Temp, Grav, Vel, Acc-W, GSR, IBI) | 1 | 300.0k (2.1$\times$) | 599.5k (2.0$\times$) | 88.3 | 14.4 | 7.7 | 86.8
| 2 | 350.0k (1.8$\times$) | 699.5k (1.8$\times$) | 82.6 | 5.9 | 45.7 | 66.3
| 3 | 300.0k (2.1$\times$) | 599.5k (2.0$\times$) | 88.7 | 3.9 | 26.4 | 81.1
Average |  |  |  | 86.5 | 8.1 | 26.6 | 78.1
TABLE IV: Test accuracy, FPR, FNRs, and F1 score (all in %) for top data categories for classification between healthy and major depressive disorder data instances Data category | Data partition | #Params (compression) | FLOPs (compression) | Acc. | FPR | FNR | F1 Score
---|---|---|---|---|---|---|---
(Temp, Grav, Vel, GSR) | 1 | 75.0k (2.7$\times$) | 149.5k (2.4$\times$) | 89.0 | 4.6 | 26.7 | 79.4
| 2 | 120.0k (1.7$\times$) | 239.5k (1.5$\times$) | 90.5 | 4.5 | 21.7 | 82.7
| 3 | 150.0k (1.3$\times$) | 299.5k (1.2$\times$) | 81.7 | 12.7 | 37.9 | 60.2
Average |  |  |  | 87.1 | 7.3 | 28.8 | 74.1
(Acc-P, Grav, Vel, GSR) | 1 | 120.0k (2.0$\times$) | 239.5k (1.8$\times$) | 88.2 | 6.5 | 24.8 | 78.7
| 2 | 145.0k (1.6$\times$) | 289.5k (1.5$\times$) | 91.2 | 1.9 | 25.7 | 83.0
| 3 | 185.0k (1.3$\times$) | 369.5k (1.2$\times$) | 82.4 | 11.9 | 37.5 | 61.3
Average |  |  |  | 87.3 | 6.8 | 29.3 | 74.3
TABLE V: Test accuracy, FPR, FNRs, and F1 score (all in %) for top data categories for classification between healthy and bipolar disorder data instances Data category | Data partition | #Params (compression) | FLOPs (compression) | Acc. | FPR | FNR | F1 Score
---|---|---|---|---|---|---|---
(Acc-P, Temp, Grav, Vel, Acc-W, IBI, ST) | 1 | 500.0k (1.2$\times$) | 999.5k (1.2$\times$) | 76.1 | 47.1 | 2.8 | 81.0
| 2 | 480.0k (1.3$\times$) | 959.5k (1.3$\times$) | 81.4 | 1.0 | 34.3 | 78.9
| 3 | 400.0k (1.6$\times$) | 799.5k (1.5$\times$) | 89.8 | 2.1 | 25.1 | 83.8
Average |  |  |  | 82.4 | 16.7 | 20.7 | 81.2
(Temp, Grav, Vel, Acc-W, GSR, IBI) | 1 | 490.0k (1.2$\times$) | 979.5k (1.1$\times$) | 75.6 | 47.1 | 3.8 | 80.5
| 2 | 450.0k (1.3$\times$) | 899.5k (1.2$\times$) | 81.6 | 0.9 | 34.2 | 79.0
| 3 | 380.0k (1.5$\times$) | 759.5k (1.5$\times$) | 87.0 | 2.1 | 33.0 | 78.4
Average |  |  |  | $81.4$ | $16.7$ | $23.7$ | $79.3$

###  5.2 MHDeep performance evaluation at the patient level

Next, we show patient-level diagnostic test accuracy. We use the most accurate
model from among the models discussed above for each classification task. Fig.
6 shows the results. In these graphs, we plot patient-level test accuracy vs.
the duration of data needed for inference. Prediction is performed for each
patient by simply taking the majority of the predicted labels for each 15s
data instance in the given data duration. We step up the data duration size by
2 minutes each time. Thus, we add eight data instances in each 2-minute
window. As we can see, the models reach 100% test accuracy after a certain
point for distinguishing healthy individuals from those with schizoaffective
and major depressive disorders. In addition, the best model for classification
between healthy and bipolar disorder individuals reaches 90.0% patient-level
accuracy. Table VI shows the minimum data duration needed to reach the
saturation accuracy. The durations are 40, 16, and 22 minutes for healthy vs.
schizoaffective disorder, healthy vs. major depressive disorder, and healthy
vs. bipolar disorder classifications, respectively.

Figure 6: Patient-level test accuracy vs. duration of data needed for classification between (a) healthy and schizoaffective disorder individuals, (b) healthy and major depressive disorder individuals, and (c) healthy and bipolar disorder individuals. TABLE VI: Minimum inference data duration (in minutes) needed to reach saturation patient-level accuracy (in %) for each classification task Classification | Time | Saturation accuracy
---|---|---
Healthy vs. Schizoaffective disorder | 40 | 100
Healthy vs. Major depressive disorder | 16 | 100
Healthy vs. Bipolar disorder | 22 | 90.0

##  6 Discussion

MHDeep combines efficient neural networks with commercially available WMSs to
diagnose various mental health disorders. Although several works address
mental health problem detection using machine learning, to our knowledge,
MHDeep is the only solution that focuses on an easy-to-use system that can
monitor the daily mental health state of the user. The diagnostic decisions
can be sent to a health server from where medical professionals can access the
information. This can enable them to quickly intervene during severe episodes
of the disorder.

We demonstrated the diagnostic effectiveness of MHDeep for three different
mental health disorders. However, this work can be generalized to other types
of mental health disorders as well. We hope it will encourage researchers to
start collecting WMS data from individuals across a diverse set of challenging
diagnostic tasks. Bypassing the manual feature engineering step through the
use of efficient DNNs enables easy scalability of this approach to many other
disease domains. It can also be used to predict the progress of a disease
based on longitudinal WMS data collected in the training stage.

Diagnosis of mental health disorders is often based on patient’s self-report
and answers to a questionnaire designed to detect each disorder. In the
future, we can improve the performance of MHDeep by adding a specifically
designed questionnaire to the data categories. In addition, adding features
based on other WMSs such as blood pressure may also help enhance model
performance.

##  7 Conclusion

In this article, we proposed a framework called MHDeep that combines data
obtained from commercially available WMSs with the knowledge distillation
power of DNNs for continuous and pervasive diagnosis of three main mental
health disorders: schizoaffective, major depressive, and bipolar. MHDeep uses
a synthetic data generation module to address the lack of large datasets. We
trained the DNN models by using iterative network growth and pruning to learn
both the weights and architecture during the training process. We evaluated
MHDeep based on data collected from 74 individuals. It achieves patient-level
accuracy of 100%, 100%, and 90.0%, using 40, 16, and 22 minutes of data
collected in the inference stage, for classification between healthy and
schizoaffective disorder individuals, healthy and major depressive disorder
individuals, and healthy and bipolar disorder individuals, respectively. The
MHDeep models were also shown to be computationally efficient. Thus, MHDeep
can be employed for pervasive diagnosis and daily monitoring while offering
high computational efficiency and accuracy.

Acknowledgments: We would like to thanks the nurses and physicians in the
Acute Care Unit East and West at the Hackensack Meridian Health Carrier Clinic
for smartwatch/smartphone based data collection from various patient cohorts
and providing patient labels. Special thanks to Donald J. Parker, CEO of
Carrier Clinic, for recognizing the promise of such a study and facilitating
data collection.

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|  Shayan Hassantabar received his B.S. degree in Electrical Engineering,
Digital Systems focus, from Sharif University of Technology, Iran. He also
received his M.Math. degree in Computer Science from University of Waterloo,
Canada, and his M.A. degree in Electrical and Computer Engineering from
Princeton University. He is pursuing the Ph.D. degree in Electrical and
Computer Engineering at Princeton University. His research interests include
automated neural network architecture synthesis, neural network compression,
and smart healthcare.
---|---
|  Joe Zhang received his B.S.E. from Princeton University in 2020. He is now
pursuing a Ph.D. in Electrical Engineering at Stanford University, supported
by a Stanford Graduate Fellowship. At Princeton, he was the recipient of the
Shapiro Prize for Academic Excellence and the Hisashi Kobayashi Prize. He has
worked on deep learning for healthcare applications and his current research
at Stanford focuses on developing robust protocols and systems for blockchain
technologies.
---|---
|  Hongxu Yin received his Ph.D. from Princeton University in 2020. He
received his B.Eng. degree from Nanyang Technological University, Singapore,
in 2015. He is now a Research Scientist with NVIDIA Research. He is a
recipient of Princeton Yan Huo 94* Graduate Fellowship, Princeton Natural
Sciences and Engineering Fellowship, Defense Science & Technology Agency gold
medal, and Thomson Asia Pacific Holdings gold medal. His research focuses on
efficient deep neural networks, data-free and hardware-guided model
compression, and efficient inference for healthcare applications.
---|---
|  Niraj K. Jha received his B.Tech. degree in Electronics and Electrical
Communication Engineering from Indian Institute of Technology, Kharagpur,
India in 1981 and Ph.D. degree in Electrical Engineering from University of
Illinois at Urbana-Champaign, IL in 1985. He has been a faculty member of the
Department of Electrical Engineering, Princeton University, since 1987. He is
a Fellow of IEEE and ACM, and was given the Distinguished Alumnus Award by
I.I.T., Kharagpur. He has received the Princeton Graduate Mentoring Award. He
has served as the Editor-in-Chief of IEEE Transactions on VLSI Systems and an
Associate Editor of several other journals. He has co-authored five widely
used books. His research has won 20 best paper awards or nominations and 21
patents. His research interests include smart healthcare, cybersecurity,
machine learning, and monolithic 3D IC design. He has given several keynote
speeches in the areas of nanoelectronic design/test, smart healthcare, and
cybersecurity.
---|---