  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. Therefore, there is a need for new strategies for diagnosis of mental
health problems. The recent introduction of body-area networks consisting of a
plethora of accurate sensors embedded in smartwatches and smartphones and deep
neural networks (DNNs) points towards a possible solution. However, disease
diagnosis based on WMSs and DNNs, and their deployment on edge devices, 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. Due to limited available data,
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 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 data instance level and patient level evaluations.
MHDeep achieves an average test accuracy of 90.4%, 87.3%, and 82.4%,
respectively, for classifications between healthy instances and schizoaffective
disorder instances, major depressive disorder instances, 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.
