Identification of ECG beats from cross-spectrum information aided learning vector quantization |
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Authors: | Saibal Dutta Amitava Chatterjee Sugata Munshi |
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Affiliation: | aHeritage Institute of Technology, Electrical Engineering Department, Kolkata 700 107, India;bJadavpur University, Electrical Engineering Department, Kolkata 700 032, India |
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Abstract: | This work describes the development of a computerized medical diagnostic tool for heart beat categorization. The main objective is to achieve an accurate, timely detection of cardiac arrhythmia for providing appropriate medical attention to a patient. The proposed scheme employs a feature extractor coupled with an Artificial Neural Network (ANN) classifier. The feature extractor is based on cross-correlation approach, utilizing the cross-spectral density information in frequency domain. The ANN classifier uses a Learning Vector Quantization (LVQ) scheme which classifies the ECG beats into three categories: normal beats, Premature Ventricular Contraction (PVC) beats and other beats. To demonstrate the generalization capability of the scheme, this classifier is developed utilizing a small training dataset and then tested with a large testing dataset. Our proposed scheme was employed for 40 benchmark ECG files of the MIT/BIH database. The system could produce classification accuracy as high as 95.24% and could outperform several competing algorithms. |
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Keywords: | Heart beat classification Feature extraction Cross-correlation Cross-spectral density Artificial Neural Network |
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