Classification of Electrocardiogram Signals With Extreme Learning Machine and Relevance Vector Machine
The ECG is one of the most effective diagnostic tools to detect cardiac diseases. It is a method to measure and record different electrical potentials of the heart. The electrical potential generated by electrical activity in cardiac tissue is measured on the surface of the human body. Current flow, in the form of ions, signals contraction of cardiac muscle fibers leading to the heart's pumping action. This ECG can be classified as normal and abnormal signals. In this paper, a thorough experimental study was conducted to show the superiority of the generalization capability of the Relevance Vector Machine (RVM) compared with Extreme Learning Machine (ELM) approach in the automatic classification of ECG beats. The generalization performance of the ELM classifier has not achieved the nearest maximum accuracy of ECG signal classsification. To achieve the maximum accuracy the RVM classifier design by searching for the best value of the parameters that tune its discriminant function, and upstream by looking for the best subset of features that feed the classifier. The experiments were conducted on the ECG data from the Massachusetts Institute of Technology–Beth Israel Hospital (MIT– BIH) arrhythmia database to classify five kinds of abnormal waveforms and normal beats. In particular, the sensitivity of the RVM classifier is tested and that is compared with ELM. Both the approaches are compared by giving raw input data and preprocessed data. The obtained results clearly confirm the superiority of the RVM approach when compared to traditional classifiers.
Keywords: Electrocardiogram (ECG) signals classification, feature detection, feature reduction, generalization capability, model selection issue, extreme learning machine (ELM), relevance vector machine (RVM)
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