Task-State Heart Rate Variability Parameter- Based Depression Detection Model and Effect of Therapy on the Parameters

Abstract

Depression is a common psychiatric disease. At present, psychometric scales are the main methods for detecting depression in patients and evaluating the clinical treatment effect of depression. However, the accuracy of the scales is influenced by the subjective factors of patients and doctors. This paper explored the construction of a depression detection model based on task-state heart rate variability (HRV) parameters and the effect of therapy on the related HRV parameters. The candidate HRV parameters were first extracted from the task-state electrocardiogram (ECG) collected before treatment and at three observation points during treatment. Then, a statistical t-test was used to screen those characteristic HRV parameters with a significant difference between the depressed and normal groups before treatment. The characteristic HRV parameters and a support-vector machine (SVM) were combined to construct the detection model. Finally, a score model was designed to reveal dynamic changes in the HRV parameters during the treatment process. This paper constructed an automatic, simple, and efficient depression detection model: peakHF+SVM. Detection accuracy reached 89.66%, and this model had comprehensive advantages compared with other related methods. During the entire treatment process, the change in the scores and the time to achieve the maximal scores were different among patients. The type and number of HRV parameters related to the maximal score of each patient also were different. The depression detection model has good application prospects in the objective, quantitative, and automatic detection of depression. The same curative method produced different effects on each patient with depression. The proposed score model may be helpful for the quantitative assessment of the therapeutic effect.