A support vector machine (SVM) classifier outperformed other machine learning-based classifiers for arrhythmic beat classification using preprocessed ECG signals.
An SVM classifier combined with a delayed error normalized LMS adaptive filter for preprocessing improves arrhythmic beat classification in remote healthcare applications.
Medical expert systems are part of the portable and smart healthcare monitoring devices used in day-to-day life. Arrhythmic beat classification is mainly used in electrocardiogram (ECG) abnormality detection for identifying heart related problems. In this paper, ECG signal preprocessing and support vector machine-based arrhythmic beat classification are performed to categorize into normal and abnormal subjects. In ECG signal preprocessing, a delayed error normalized LMS adaptive filter is used to achieve high speed and low latency design with less computational elements. Since the signal processing technique is developed for remote healthcare systems, white noise removal is mainly focused. Discrete wavelet transform is applied on the preprocessed signal for HRV feature extraction and machine learning techniques are used for performing arrhythmic beat classification. In this paper, SVM classifier and other popular classifiers have been used on noise removed feature extracted signal for beat classification. Results indicate that the performance of SVM classifier is better than other machine learning-based classifiers.
Venkatesan et al. (Mon,) conducted a other in Arrhythmia. Support vector machine (SVM) classifier vs. Other machine learning-based classifiers was evaluated on Beat classification performance. A support vector machine (SVM) classifier outperformed other machine learning-based classifiers for arrhythmic beat classification using preprocessed ECG signals.