Analysis of QRS Detection Algorithm for Cardiac Abnormalities – A Review
R. Harikumar1, S. N. Shivappriya2
1Dr. R. Harikumar, Professor, Department of ECE , Bannari Amman Institute of Technology, Sathyamangalam, India.
2S.N. Shivappriya, Assistant Professor, ECE Department, Kumaraguru College of Technology, Coimbatore, India.
Manuscript received on October 02, 2011. | Revised Manuscript received on October 19, 2011. | Manuscript published on November 05, 2011. | PP: 80-88 | Volume-1 Issue-5, November 2011. | Retrieval Number: E0154081511/2011©BEIESP
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© The Authors. Published By: 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: This work investigates and compares a set of efficient techniques to extract and select striking features from the ECG data applicable in automatic cardiac beat classification. Each method was applied to a pre-selected data segment from the MIT-BIH (Massachusetts Institute of Technology / Beth Isrel Hospital) database. The classification and optimization of different heart beat methods were performed based upon the extracted features (morphological and statistical feature). The morphological features were found as the most important for arrhythmia classification. However, because of ECG signal variability in different patients, the statistical approach is favoured for a precise and robust feature extraction. Among all these feature extraction, feature selection, classification and optimization techniques, SVM based PSO gives higher classification accuracy with curse of dimensionality.
Keywords: Cardiac beat classifier, Feature Extraction, Feature Selection, SVM, PSO.