Dr. Narendra G. Bawane Profile

Dr. Narendra G. Bawane

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Proceedings Article | 26 February 2010 Paper

Efficient ECG signal analysis using wavelet technique for arrhythmia detection: an ANFIS approach

P. Khandait, N. Bawane, S. Limaye

Proceedings Volume 7546, 75461G (2010) https://doi.org/10.1117/12.853576

KEYWORDS: Electrocardiography, Wavelets, Wavelet transforms, Feature extraction, Databases, Signal detection, Heart, Algorithm development, Signal analyzers, Signal analysis

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This paper deals with improved ECG signal analysis using Wavelet Transform Techniques and employing subsequent modified feature extraction for Arrhythmia detection based on Neuro-Fuzzy technique. This improvement is based on suitable choice of features in evaluating and predicting life threatening Ventricular Arrhythmia . Analyzing electrocardiographic signals (ECG) includes not only inspection of P, QRS and T waves, but also the causal relations they have and the temporal sequences they build within long observation periods. Wavelet-transform is used for effective feature extraction and Adaptive Neuro-Fuzzy Inference System (ANFIS) is considered for the classifier model. In a first step, QRS complexes are detected. Then, each QRS is delineated by detecting and identifying the peaks of the individual waves, as well as the complex onset and end. Finally, the determination of P and T wave peaks, onsets and ends is performed. We evaluated the algorithm on several manually annotated databases, such as MIT-BIH Arrhythmia and CSE databases, developed for validation purposes. Features based on the ECG waveform shape and heart beat intervals are used as inputs to the classifiers. The performance of the ANFIS model is evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed ANFIS model has potential in classifying the ECG signals. Cross validation is used to measure the classifier performance. A testing classification accuracy of 95.13% is achieved which is a significant improvement.

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