Detection of Congestive Heart Failure Based on Spectral Features and Extreme Learning Machine
Sulekha Saxena1, P. N. Hrisheekesha2, Vijay Kumar Gupta3, Ram Sewak Singh4

1Sulekha Saxena, Department of Electrical & Electronics Engineering, IMS Engineering College, Ghaziabad (Uttar pradesh),  India.

2P. N. Hrisheekesha, Department of Campus Director, Chandigarh Enginnering College (Mohali), India.

3Vijay Kumar Gupta, Department of Electronics & Communication Engineering, Inderprastha Engineering College, Ghaziabad (Uttar pradesh),  India.

4Ram Sewak Singh, Department of Electronics & Communication Engineering, IMS Engineering College, Ghaziabad (Uttar pradesh),  India.

Manuscript received on 05 August 2019 | Revised Manuscript received on 12 August 2019 | Manuscript Published on 26 August 2019 | PP: 851-862 | Volume-8 Issue-9S August 2019 | Retrieval Number: I11370789S19/19©BEIESP | DOI: 10.35940/ijitee.I1137.0789S19

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Abstract: In this paper we proposed a novel approach to evaluate the classification performance of features derived from various spectral investigation methods for congestive Heart Failure (CHF) analysis using ranking methods, Kernel Principal Component Analysis (KPCA) and binary classifier as 1-norm linear programming extreme learning machine (1-NLPELM). For this study, thirty different features are extracted from heart rate variability (HRV) signal by using spectral methods like multiscale Wavelet packet (MSWP), higher order spectra (HOS) and auto regression (AR) model. Top ten features were extracted by ranking methods and then reduced to only one feature by KPCA having kernel function as radial basis function (RBF) which wasfurther applied to 1-NLPELM binary classifier. For this purpose, the HRV data were taken from standard database of Normal sinus rhythm (NSR),elderly (ELY) and Congestive heart failure (CHF) subjects. Numerical experiments were being done on the combination of database sets as NSR-CHF, NSR-ELY, and ELY-CHF subjects. The numerical results show that features at third level of decomposition of HRV data sets MSWP shows lowest p-value . Thus, third level of MSWP features are better than other features extracted by auto regression (AR) model and higher order spectra (HOS) spectral methods.

Keywords: 1-Norm Linear Programming Extreme Learning Machine (1-NLPELM),Higher order Spectra (HOS), Kernel Principal Component Analysis (KPCA), Ranking Methods.
Scope of the Article: Advanced Computing Architectures and New Programming Models