Does a neuro-fuzzy algorithm using HRV features accurately distinguish patients with depression from healthy controls?
A neuro-fuzzy algorithm using heart rate variability features can accurately classify depression, highlighting the link between depression and autonomic nervous system dysfunction and offering potential for smartphone-based monitoring.
Recent research indicates a significant relationship between the severity of depression and heart rate variability (HRV). This paper presents a neuro-fuzzy approach-based classification algorithm, which distinguishes patients with depression from controls by a neuro-fuzzy network with a weighted fuzzy membership function (NEWFM) using the two time domain and four frequency domain features of HRV. The HRV data were collected from 10 patients with depression and an equal number of healthy controls. Wearing a wireless Holter monitor, each subject underwent a 13-minute multimodal affective contents stimulus, which can induce a variety of emotions. HRV activity was transformed and recorded from periods of 13-minute ECG signals. With a reliable accuracy rate of 95%, the six HRV features were extracted and used as NEWFM input features for depression classification. The standard deviation of the RR intervals (SDNN) and very low frequency (VLF) of HRV were evaluated as good features-from six features-by a non-overlap area distribution measurement method. The two features reflected conspicuous differences between the depression diagnosed and the healthy subjects, which indicates a significant association between depression and the autonomic nervous system. The proposed algorithm will be implemented as a depression monitoring system in a Smartphone application.
Zhang et al. (Tue,) studied this question.