Depress-DCNF: A deep convolutional neuro-fuzzy model for detection of depression episodes using IoMT

Abstract

Discernible patterns of a person’s daily activities can be utilized to detect behavioral symptomatology of mental illness at early stages. Wearable Internet of Medical Things (IoMT) devices with sensors that collect motion data and provide objective measures of physical activity can help to better monitor and detect potential episodes related to the mental health conditions at earlier, more treatable stages. This research puts forward a neuro-symbolic model which uses learnable parameters with integrated knowledge for detection of depression episodes using IoMT based actigraphic input. A novel deep fuzzy model, Depress-DCNF is a hybrid of convolutional neural network (CNN) and an adaptive neuro fuzzy inference system (ANFIS) where CNN is used to extract high-level features from the motor activity recordings which are eventually combined with the discriminative statistical features to produce an optimized feature map. This optimized feature map is finally used to train the ANFIS model which accurately performs the depression classification task. The model is validated on the Depresjon benchmark dataset and compares favorably to state-of-the-art approach giving a superior performance accuracy of 85.10%.