Key result
A proposed arrhythmia classifier using wavelet decomposition and a multiclass support vector machine achieved a classification accuracy of up to 98.63% for various classes of arrhythmia.
Why the study?
The study was conducted to extract the features of single arrhythmia ECG beats and develop efficient algorithms for automated detection of arrhythmia based on ECG.
Does an algorithm based on wavelet decomposition and support vector machine accurately classify arrhythmia from ECG beats?
Does an algorithm based on wavelet decomposition and support vector machine accurately classify arrhythmia from ECG beats?
An automated arrhythmia classifier using wavelet decomposition and SVM achieved up to 98% accuracy in classifying various arrhythmias from ECG beats.
Hypothesis-generating for automated ECG arrhythmia detection; requires prospective clinical validation before practice integration.
Objectives: To extract the features of single arrhythmia ECG beat. To develop efficient algorithms for automated detection of arrhythmia based on ECG. Methods/Statistical analysis: The methodology includes pre-processing and segmentation of ECG. Extraction of ECG features are to support the ECG beat classification and analysis of cardiac abnormalities using machine learning techniques. Wavelet decomposition is considered for feature extraction and classification with multiclass support vector machine. Findings: This work evaluates the suitability of the wavelet features of ECG for classifier. The proposed arrhythmia classifier results in an accuracy up to 98% for various classes of arrhythmia considered in this work. Novelty/Applications: This work is an assistive tool for medical practitioners to examine ECG in a limited time with their expertise to make the accurate abnormality diagnosis of the arrhythmia.
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Sundari Tribhuvanam (2020) studied Arrhythmia (n=47). Wavelet decomposition and Support Vector Machine (SVM) classifier was evaluated on Classification accuracy of arrhythmia beats. A proposed arrhythmia classifier using wavelet decomposition and a multiclass support vector machine achieved a classification accuracy of up to 98.63% for various classes of arrhythmia.
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