Danni Kuang1, Rongqian Yang2, Xiuwen Chen1, Guohui Lao3, Fengchun Wu3, Xiong Huang3, Ruixue Lv4, Lei Zhang4, Chuanxu Song4, Shanxing Ou5. 1. Department of Biomedical Engineering, South China University of Technology, Guangzhou, China. 2. Department of Biomedical Engineering, South China University of Technology, Guangzhou, China. Electronic address: bmeyrq@foxmail.com. 3. The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital), Guangzhou, China. 4. Shenzhen Sayes Medical Technology Co., Ltd., Shenzhen, China. 5. General Hospital of Guangzhou Military Command of PLA, Guangzhou, China.
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.
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). Depressionpatients have lower HRV than healthy subjects. Therefore, HRV may be used to distinguish depressionpatients from healthy people. METHODS: HRV signals were collected from 76 female subjects composed of 38 depressionpatients 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 depressionpatients 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 depressionpatients 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 depressionpatients and healthy people to recognize depression. The RMSSD of the HRV is important in recognition and may be a significant index in distinguishing depressionpatients from healthy people.
Authors: Carmen Schiweck; Ali Gholamrezaei; Maxim Hellyn; Thomas Vaessen; Elske Vrieze; Stephan Claes Journal: Front Psychiatry Date: 2022-04-18 Impact factor: 5.435