Cardiac Episode Detection and Classification – A Systematic Review
Avvaru Srinivasulu1, Srinivasa Rao Lam2
1Avvaru Srinivasulu, Department of Electronics and Instrumentation Engineering, GITAM Deemed to be University, Bengaluru Campus, (Karnataka), India.
2Srinivasa Rao Lam, Department of Electronics and Instrumentation Engineering, GITAM Deemed to be University, Visakhapatnam (Andhra Pradesh), India.
Manuscript received on 26 February 2019 | Revised Manuscript received on 13 March 2019 | Manuscript Published on 17 March 2019 | PP: 35-42 | Volume-7 Issue-ICETESM18, March 2019 | Retrieval Number: ICETESM10|19©BEIESP
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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: As the death rate is increasing in India due to heart diseases, there is a need for developing an automatic cardiac diagnostic system. Therefore, the heartbeat classification is a key to diagnose arrhythmias automatically. Here, a review is presented on different Cardiac Episode detection and classification methods, which help to develop the automatic cardiac diagnostic system. This review includes online datasets available for cardiac episode classification, features extracted and the extraction methods, Feature selection for reduced feature vector and methods of classification. Finally, the review discusses the limitations of the existed methods.
Keywords: Cardiac Episode, Electrocardiogram (ECG), Feature Extraction, Feature Selection, Heartbeat Classification.
Scope of the Article: Classification