Abstract
In this study, a new approach using a novel ensemble classification algorithm based on ECG morphological features is proposed for accurate detection of heart ventricular and atrial abnormalities. First, the raw ECG signal is preprocessed and the main character waves are detected. Second, a combination of ECG morphological features is proposed and extracted from the selected ECG segments. The proposed feature set contains morphological parameters, morphological visual pattern of QRS complex, and principle components of the third level and fourth level of a four-level Sym8 wavelet-decomposed ECG waveform. Next, a novel ensemble classification algorithm, with the key idea of integrating the knowledge acquired by several popular classification algorithms for this task into an ensemble system, is proposed so that the accuracy and robustness over various arrhythmia types could be improved. Finally, the features are applied to the proposed ensemble classification algorithm for abnormality detection. The proposed approach achieved an overall accuracy of 98.68% when it was validated on fifteen heartbeat types from the MIT-BIH arrhythmia database (MITDB), according to the Association for Advancement of Medical Instrumentation (AAMI) standard. The classification accuracies of the six main types – normal beat (N), right bundled branch blocks beat (R), left bundled branch blocks beat (L), atrial premature beat (A), premature ventricular contractions beat (V), and paced beat (P) are 98.75%, 99.77%, 99.70%, 94.81%, 98.57%, and 99.94%, respectively. The proposed approach proves a solid result in comparison with component classification algorithms as well as recent peer works.
Highlights
According to a recent report of the World Health Organization (WHO), cardiovascular disease (CVD) is the leading cause of noncommunicable disease deaths, which takes an estimated 17.9 million lives each year, accounting for 44% of all deaths from noncommunicable diseases in the world [1]
A great number of cardiac arrests are associated with cardiac arrhythmias, which are caused by abnormal formation and conduction of electrical impulses through the myocardial
This paper provides a novel approach for heart ventricular and atrial abnormalities detection using a novel ensemble classifier based on ECG morphological features
Summary
According to a recent report of the World Health Organization (WHO), cardiovascular disease (CVD) is the leading cause of noncommunicable disease deaths, which takes an estimated 17.9 million lives each year, accounting for 44% of all deaths from noncommunicable diseases in the world [1]. A great number of cardiac arrests are associated with cardiac arrhythmias, which are caused by abnormal formation and conduction of electrical impulses through the myocardial. Early and timely detection of cardiac arrhythmia is crucial for saving people’s lives. The heart rhythm is controlled by an electrical impulse originated from the atrial sine node (SA node) located in the right atrium of the heart. The electrical activity propagating all over the heart causes electrical potential difference on the skin surface which could be measured with electrodes added to patient’s body surface and graphically recorded in Electrocardiogram (ECG). ECG is a well-known technique for non-invasively measuring the cardiac activity of a patient. A typical normal ECG cycle is composed of several individual components
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