Depression Detection Using Distribution of Microstructures from Actigraph Information

Depression negatively affects the daily life of an individual and may even lead to suicidal tendencies. The problem is compounded by the scarcity of trained psychologists and psychiatrists in developing countries due to which many cases go undetected. The automated diagnosis of depression can, therefore, assist clinicians to screen the patients and help them to handle the symptoms. The advent of wearable devices in the past decade has helped in capturing signals, which can be used to diagnose depression. This work uses a publicly available dataset and develops a model based on the distribution of microstructures from the temporal data to accomplish the given task. The results are encouraging and better than the state-of-the-art. An accuracy of 86.90% is obtained by using the proposed pipeline. This work is part of a larger project that aims to detect depression using multi-modality data.