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http://dx.doi.org/10.5392/JKCA.2012.12.02.001

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

Zhang, Zhen-Xing (경원대학교 IT대학)
Tian, Xue-Wei (경원대학교 IT대학)
Lim, Joon-S. (경원대학교 IT대학)
Publication Information
The Journal of the Korea Contents Association / v.12, no.2, 2012 , pp. 1-9 More about this Journal
Abstract
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%.
Keywords
Neuro-Fuzzy Network; Feature Extraction; Feature Selection; Heart Rate Variability; Depression;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
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