Neuro-Fuzzy Network-based Depression Diagnosis Algorithm Using Optimal Features of HRV

뉴로-퍼지 신경망 기반 최적의 HRV특징을 이용한 우울증진단 알고리즘

  • Received : 2011.11.25
  • Accepted : 2012.02.06
  • Published : 2012.02.28


This paper presents an algorithm for depression diagnosis using the Neural Network with Weighted Fuzzy Membership functions (NEWFM) and heart rate variability (HRV). In the algorithm, 22 different features were initially extracted from the HRV signal by frequency domain, time domain, wavelet transformed, and Poincar$\acute{e}$ transformed feature extraction methods; of these 6 optimal features were selected by significance evaluation using Non-overlap Area Distribution Measurement (NADM) based on NEWFM. The proposed algorithm uses these 6 optimal features to diagnose depression with an accuracy of 95.83%.


Neuro-Fuzzy Network;Feature Extraction;Feature Selection;Heart Rate Variability;Depression


Supported by : 정보통신산업진흥원


  1. B. Hosseinifard, M. H. Moradi, and R. Rostami, "Classifying Depression Patients and Normal Subjects Using Machine Learning Techniques," Proceeding of Iranian Conference Electrical Engineering, pp.131-134, 2011.
  2. Y. J. Li and F. Y. Fan, "Classification of Schizophrenia and Depression by EEG with ANNs," Proceeding of IEEE Engineering in Medicine and Biology Society, pp.2679-2682, 2005.
  3. I. Kalatzis, N. Piliourasa, E. Ventourasa, C. C. Papageorgioub, A. D. Rabavilas, and D. Cavourasa, "Design and Implementation of an SVM-based Computer Classification System for Discriminating Depressive Patients from Healthy Controls Using the P600 Component of ERP Signals," Computer Methods and Programs in Biomedicine, Vol.75, No.1, pp.11-22, 2004.
  4. A. K. Rostamabad, J. P. Reilly, G. Hasey, H. Bruin, and D. MacCrimmon, "Diagnosis of Psychiatric Disorders Using EEG Data and Employing a Statistical Decision Model," Proceeding of IEEE Engineering in Medicine and Biology Society, pp.4006-4009, 2010.
  5. G. Licht, E. Geus, F. Zitman, W. Hoogendijk, R. Dyck, and B. Penninx, "Association Between Major Depressive Disorder and Heart Rate Variability in the Netherlands Study of Depression and Anxiety," Archives of General Psychiatry, Vol.65, No.12, pp.1358-1367, 2008.
  6. M. Karavidas, "Heart Rate Variability Biofeedback for Major Depression," Applied Psychophysiology and Biofeedback, Vol.36, No.1, pp.18-21, 2008.
  7. M. W. Agelink, C. Boz, H. Ullrich, and J. Andrich, "Relationship between Major Depression and Heart Rate Variability," Clinical Consequences and Implications for Antidepressive Treatment. Vol.113, No.2, pp.139-149, 2002.
  8. J. S. Lim, "Finding Features for Real-Time Premature Ventricular Contraction Detection Using a Fuzzy Neural Network System," IEEE Trans. on Neural, Vol.20, No.3, pp.522-527, 2009.
  9. 신동근, 장진흥, 이상홍, 임준식, 이정현, "가중 퍼지소속함수 기반 신경망과 웨이블릿 변환을 이용한 심실빈맥/세동 검출", 한국콘텐츠학회논문지, 제9권, 제7호, pp.19-26, 2009.
  10. 이상홍, 신동근, 임준식, "운동 형상 분류를 위한 웨이블릿 기반 최소의 특징 선택", 한국콘텐츠학회논문지, 제10권, 제6호, pp.27-34, 2010.
  11. 신동근, 정경용, "웨이블릿 변환과 퍼지 신경망을 이용한 단기 KOSPI 예측", 한국콘텐츠학회논 문지, 제11권, 제6호, pp.1-7, 2011.
  12. 신동근, "주성분 분석과 수면 2기를 이용한 수면 장애 분류", 한국콘텐츠학회논문지, 제11권, 제4 호, pp.27-32, 2011.
  13. W. W. Zung, "A Self-rating Depression Scale," Archives of General Psychiatry, Vol.12, No.1, pp.63-70, 1965.
  14. P. S. Hamilton and W. J. Tompkins, "Quantitative Investigation of QRS Detection Rules Using the MIT/BIH Arrhythmia Database," IEEE Trans. on Biomedical Engineering, Vol.33, No.12, pp.1157-1165, 1986.
  15. E. Nahshont, D. Aravot, D. Aizenberg, M. Sigler, G. Zalsman, B. Strasberg, S. Imbra, E. Adler, and A. Weizman, "Heart Rate Variability in Patients With Major Depression," Psychosomatics, Vol.45, No.2, pp.129-134, 2004.
  16. C. K. Lee, S. K. Yoo, Y. J. Park, K. NamHyun, K. S. Jeong, and B. Lee, "Using Neural Network to Recognize Human Emotions from Heart Rate Variability and Skin Resistance," Proceeding of Engineering in Medicine and Biology Society, pp.5523-5525, 2005.
  17. M. Malik, "Heart Rate Variability Standards of Measurement, Physiological Interpretation, and Clinical Use," European Heart Journal, Vol.17, No.3, pp.354-381, 1996.
  18. A. Subasi, "Automatic Recognition of Alertness Level from EEG by Using Neural Network and Wavelet Coefficients," Expert Systems with Applications, Vol.28, No.4, pp.701-711, 2005.
  19. M. Brennan, M. Palaniswami, and P. Kamen "Do Existing Measures of Poincare Plot Geometry Reflect Nonlinear Features of Heart Rate Variability?" IEEE Trans. on Biomedical Engineering, Vol.48, No.11, pp.1342-1347, 2001.
  20. A. Kandaswamy, C. S. Kumar, R. P. Ramanathan, S. Jayaraman, N. Malmurugan, "Neural Classification of Lung Sounds Using Wavelet Coefficients," Computers in Biology and Medicine, Vol.34, No.6, pp.523-537, 2004.