Depression diagnosis based on Deep Learning Using Time-series Sleep Quality Data

Depressive disorder is one of the most common mental health conditions worldwide, with a high risk of suicide and a significant likelihood of becoming chronic. Currently, the diagnosis of depressive disorder relies on clinical interviews and self-report questionnaires, highlighting the need for an objective, digital biomarker-based time-series depression diagnosis model. This study aims to develop a deep learning-based multivariate time-series depression classification model using sleep data collected from wearable devices. The model architecture employs MLSTM-FCN, InceptionTime, and Time-series Transformer, extracting features from a total of ten sleep biomarker candidates to classify depression status. The performance of the models was evaluated, yielding AUC scores of 0.91, 0.82, and 0.78, respectively, with MLSTM-FCN demonstrating the highest performance. Among the sleep biomarker candidates, total time spent in bed, REM sleep latency, and light sleep duration were identified as significant indicators. The proposed model offers a cost-effective and objective method for depression diagnosis and is expected to be applicable to depression patients in community settings in the future.