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%.


Supported by : 정보통신산업진흥원


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