An Exploration of ECG Signal Feature Selection and Classification using Machine Learning Techniques
M. Gowri Shankar1, C. Ganesh Babu2

1M. Gowri Shankar*, Assistant Professor, Department of EEE, Gnanamani College of Technology, Namakkal, India.
2Dr. C. Ganesh Babu, Professor, Department of ECE, Bannari Amman Institute of Technology, Sathyamangalam, India.
Manuscript received on December 18, 2019. | Revised Manuscript received on December 22, 2019. | Manuscript published on January 10, 2020. | PP: 797-804 | Volume-9 Issue-3, January 2020. | Retrieval Number: C8728019320/2020©BEIESP | DOI: 10.35940/ijitee.C8728.019320
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Abstract: This effort examines and likens a collection of active methods to dimensionally reduction and select salient features since the electrocardiogram database. ECG signal classification and feature selection plays a vital part in identifies of cardiac illness. An accurate ECG classification could be a difficult drawback. This effort also examines of ECG classification into arrhythmia kinds. This effort discusses the problems concerned in Classification ECG signal and exploration of ECG databases (MIT-BIH), pre-processing, dimensionally reduction, Feature selection techniques, classification and optimization techniques. Machine learning techniques give offers developed classification accurateness with imprecation of dimensionality. 
Keywords: Feature Selection, Classification, Arrhythmia, MIT-BIH, Machine learning techniques
Scope of the Article: Classification