Depression recognition according to heart rate variability using Bayesian Networks

Abstract Background Doctors mainly use scale tests and subjective judgment in the clinical diagnosis of depression. Researches have demonstrated that depression is associated with the dysfunction of the autonomic nervous system (ANS), where its modulation can be evaluated by heart rate variability (HRV). Depression patients have lower HRV than healthy subjects. Therefore, HRV may be used to distinguish depression patients from healthy people. Methods HRV signals were collected from 76 female subjects composed of 38 depression patients and 38 healthy people. Time domain, frequency domain, and non-linear features were extracted from the HRV signals of these subjects, who were subjected to the Ewing test as an ANS stimulus. Then, these multiple features were input into Bayesian networks, served as a classifier, to distinguish depression patients from healthy people. Hence, accuracy, sensitivity, and specificity were calculated to evaluate the performance of the classifier. Results Recognition results indicate 86.4% accuracy, 89.5% sensitivity, and 84.2% specificity. The individuals subjected to the Ewing test showed better recognition results than those at individual test states (resting state, deep breathing state, Valsalva state, and standing state) of the Ewing test. The root mean square of successive differences (RMSSD) of the HRV exhibits a significant relevance with recognition. Conclusion Bayesian networks can be applied to the recognition of depression patients from healthy people and the recognition results demonstrate the significant association between depression and HRV. The Ewing test is a good ANS stimulus for acquiring the difference of HRV between depression patients and healthy people to recognize depression. The RMSSD of the HRV is important in recognition and may be a significant index in distinguishing depression patients from healthy people.