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Multimedia Tools and Applications

, Volume 78, Issue 24, pp 35351–35372 | Cite as

GABC based neuro-fuzzy classifier with hybrid features for ECG Beat classification

  • K. MuthuvelEmail author
  • S. Anto
  • T. Jerry Alexander
Article
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Abstract

The main objective of the proposed methodology is to classify an ECG signal as normal or abnormal using the optimal neuro-fuzzy classifier. The proposed work consists of two phases namely, feature extraction and neuro-fuzzy classifier based classification. The beat signals are initially taken from the physio-bank ATM. Then, three types of features are extracted from each signal namely, Morphological-based features, Haar wavelet-based features, and Tri-spectrum based features. After feature extraction, the optimal neuro-fuzzy classifier is classifying the beat signal as normal or abnormal. Here, Artificial Bee Colony (ABC) algorithm is combined with Genetic Algorithm (GA) for training the neuro-fuzzy classifier. For experimental evaluation, the MIT-BIH Arrhythmia Database is utilized and the performances are analyzed in terms of accuracy, sensitivity, and specificity. The experimental results clearly demonstrated that the proposed technique outperformed by having better accuracy of 93% when compared existing technique achieved 84% only.

Keywords

ECG Neuro-fuzzy classifier Artificial bee colony 
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Notes

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Electrical and Electronics EngineeringNoorul Islam UniversityKumaracoilIndia
  2. 2.School of Computer Science and EngineeringVellore Institute of TechnologyVelloreIndia
  3. 3.Department of Electronics and Communication EngineeringSathyabama UniversityChennaiIndia

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