Depression Level Classification Using Machine Learning Classifiers Based on Actigraphy Data

Estimating and classifying depression status are critical in the clinical and psychological domains to map the course of treatment. Prior researchers used biosignal time-series data to reflect the variation in factors associated with depression. In addition, machine learning algorithms were applied to determine the underlying relationships between depressive symptoms and these factors. In this study, we introduce a classification framework for depression levels using actigraphy data based on machine learning algorithms. Fourteen circadian rhythm features (minimum, amplitude, alpha, beta, acrotime, upmesor, downmesor, mesor, f_pseudo, interdaily stability (IS), intradaily variability (IV), relative amplitude (RA), M10, and L5) extracted from accelerometer-based actigraphy data were used to model depression status with survey variables. Six evaluation metrics (accuracy, precision, recall, F1-score, receiver operating characteristic curve, and area under the curve) were applied to validate the performance of the proposed framework. Among the four candidate classifiers (XGBoost classifier, support vector classifier, multilayer perceptron, and logistic regression), the XGBoost classifier was the best at classifying depression levels. Moreover, we confirmed that the actigraphy data of two days were optimal for feature extraction and classification. The results of this study provide novel insights into the relationship between depression and physical activity in terms of both identification of depression and application of actigraphy data.