Key result
PSO-FCM clustering with an enhanced SVM classifier achieves ~98% accuracy in ECG beat classification.
Why the study?
Heart Rate Variability is an essential feature for assessing heart condition, and improved classification methods for ECG signals are needed to enhance diagnostic accuracy.
A novel machine learning approach combining PSO, FCM clustering, and Enhanced SVM achieves 98.38% accuracy in classifying ECG beats from the MIT-BIH arrhythmia database.
Background/Objectives: Heart Rate Variability is an essential feature which decides the condition of human heart. ECG is used as diagnostic tool to access the electrical function of the heart. Methods/Statistical Analysis: The nine linear and nonlinear features are derived from the HRV signals. The feature extraction is carried out with the help of Particle Swarm Optimization (PSO) for data reduction. In proposed scheme Fuzzy C-Means (FCM) clustering and classifier integrated to enhance the accuracy result for ECG beat classification. Findings: The Enhanced SVM classifier classifies the heart rate data. Enhanced SVM classifier groups the linear and non-linear parameters as inputs, which are derived from the HRV signal. The denoise signals are classified and identifies the pattern for better classification of ECG signal. Application/Improvements: The proposed scheme is experimented with the assistance of the most commonly used MIT-BIH arrhythmia database and adequate results were obtained with an accuracy level of 98.38% than the other well-known approaches.
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Kavitha et al. (2015) studied Arrhythmia. PSO-FCM Clustering and Enhanced Support Vector Machine vs. other well-known approaches was evaluated on Accuracy level for ECG beat classification. An integrated scheme using PSO-FCM clustering and an Enhanced SVM classifier achieved an accuracy level of 98.38% for ECG beat classification on the MIT-BIH arrhythmia database.
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