Feature Extraction and Classification for ECG Signal Processing based on Artificial Neural Network and Machine Learning Approach

Stalin Subbiah¹, Rajkumar Patro², P Subbuthai³

¹Bharathiar University
²Guru Nanak Engineering College
³Bharathiar University

Abstract

In present day, several types of developments are carried toward the medical application. There has been increased improvement in the processing of ECG signals. Get the accurate detection of ECG signals with the help of detection of P, Q, R and S waveform. However these waveforms are suffered from some disturbances like noise. Initially denoising the ECG signal using filters and detect the PQRS waveforms. ECG signal is analyzed or classify using Extreme Learning Machine (ELM) and it compared with Support Vector Machine (SVM) and Back Propagation Neural Network (BPN). The paper classifies the ECG signal into two classes, Normal and Abnormal. ECG waveform is detected and analyzed using the 48 records of the MIT-BIH arrhythmia database. The classifier performance is measured in terms of Sensitivity (Se), Positive Predictivity (PP) and Specificity (SP).

Keywords

• Electrocardiogram, Extreme Learning Machine, Support Vector Machine, Back Prorogation Neural Network, MIT-BIH arrhythmia database

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Cite this Article as Follows

Stalin Subbiah, Rajkumar Patro, Subbuthai P. Feature Extraction and Classification for ECG Signal Processing based on Artificial Neural Network and Machine Learning Approach.. The International Conference on Inter Disciplinary Research in Engineering and Technology 2015. Vol. 1. Chennai: Association of Scientists, Developers and Faculties, 2015. 50-57. Print.