Abstract
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. Keywords: Arrhythmia; classification; feature extraction; support vector machine; wavelet decomposition
Highlights
Cardiac diseases are the most common cause of death around the globe
A set of 27 features are extracted from each ECG beat resulting in 702 combinations of input pairs taking two features at a time. Out of these only 351 feature pair combinations are valid without repeating any input feature pair
The results shows that the proposed method considers increased number of wavelet features of ECG and support vector machine (SVM) classifier results in considerable accuracy of classification of higher number of arrhythmia beat types in comparison with other methods using the same database. [ Table 10]
Summary
Cardiac diseases are the most common cause of death around the globe. The design of health monitoring systems is always a topic of active research to support the cardiac patient. Electrocardiogram (ECG) provides detailed information of the condition of the heart [1,2]. Cardiologists can infer heart conditions from ECG wave patterns and inter wave intervals. To assist the medical doctors, researchers have proposed many algorithms for segmentation and classification of ECG signals more precisely and correctly in real-time [3]. An arrhythmia classification system includes the pre-processing of ECG signal, abnormal beat segmentation, extraction of wavelet domain features and beat classification [4,5,6]. The objective is to identify the various ECG arrhythmias as per AAMI standard thereby assisting the cardiologist for early diagnosis of heart disease. Arrhythmia detection procedure differs in selecting the size of ECG signal window, ECG feature extraction and classification approaches[7]. ECG feature selection is implemented by Bacterial Forging Optimization (BFO) and Particle Swarm
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