Machine Learning Classification and Feature Extraction of Arrhythmic ECG Data
Sumanta Kuila1, Sayandeep Maity2, Suman Kumar Mal3, Subhankar Joardar4
1Sumanta Kuila, Dept. of Computer Sc. & Engineering , Haldia Institute of Technology, Haldia-721657, West Bengal, India.
2Sayandeep Maity, Dept. of Computer Sc. & Engineering, Haldia Institute of Technology, Haldia-721657, West Bengal, India.
3Suman Kumar Mal, Dept. of Computer Sc. & Engineering, Haldia Institute of Technology, Haldia, West Bengal, India.
4Subhankar Joardar, Dept. of Computer Sc. & Engineering, Haldia Institute of Technology, Haldia-721657, West Bengal, India.
Manuscript received on May 25, 2020. | Revised Manuscript received on June 29, 2020. | Manuscript published on July 30, 2020. | PP: 6-12 | Volume-9 Issue-2, July 2020. | Retrieval Number: B3548079220/2020©BEIESP | DOI: 10.35940/ijrte.B3548.079220
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Electrocardiogram (ECG) is the analysis of the electrical movement of the heart over a period of time. The detailed information about the condition of the heart is measured by analyzing the ECG signal. Wavelet transform, fast Fourier transform are the different methods to disorganize cardiac disease. The paper elaborates the survey on ECG signal analysis and related study on arrhythmic and non arrhythmic data. Here we discuss the efficient feature extraction process for electrocardiogram, where based on position and priority six best P-QRS-T fragments are studied. This survey examines the the outcome of the system by using various Machine learning classification algorithms for feature extraction and analysis of ECG Signals. Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Artificial Neural Network (ANN) are the most important algorithms used here for this purpose. There are several publicly available data sets which are used for arrhythmia analysis and among them MIT-BIH ECG-ID database is mostly used. The drawbacks and limitations are also discussed here and from there future challenges and concluding remarks can be done.
Keywords: Electrocardiogram, Machine learning , Classification , Arrhythmia Database ,Physionet.