Abstract
An Electrocardiogram or ECG is an electrical recording of the heart and is used in the investigation of heart disease. This ECG can be classified as normal and abnormal signals. The classification of the ECG signals is presently performed with the support vector machine. The generalization performance of the SVM classifier is not sufficient for the correct classification of ECG signals. To overcome this problem the ELM classifier is used which works 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 Physionet arrhythmia database to classify five kinds of abnormal waveforms and normal beats. In this paper a thorough experimental study was done to show the superiority of the generalization capability of the Extreme Learning Machine (ELM) is presented and compared with support vector machine (SVM) approach in the automatic classification of ECG beats. In particular, the sensitivity of the ELM classifier is tested and that is compared with SVM combined with two classifiers, they are the k-nearest neighbor classifier (kNN) and the radial basis function neural network classifier (RBF), with respect to the curse of dimensionality and the number of available training beats. The obtained results clearly confirm the superiority of the ELM approach as compared to traditional classifiers.
Highlights
ECG is a technique which captures transthoracic interpretation of the electrical activity of the heart over time and externally recorded by skin electrodes
In order to feed the classification process, in this paper, the two following kinds of features are adopted: 1) ECG morphology features and 2) three ECG temporal features, i.e., the QRS complex duration, the RR interval, and the RR interval averaged over the ten last beats
From the obtained experimental results, it can be strongly recommended that the use of the Extreme Learning Machine (ELM) approach for classifying ECG signals on account of their superior generalization capability as compared to traditional classification techniques
Summary
ECG is a technique which captures transthoracic interpretation of the electrical activity of the heart over time and externally recorded by skin electrodes. In the form of ions, signals contraction of cardiac muscle fibers leading to the heart's pumping action. It is a non persistent recording produced by an electrocardiographic device. Feature selection and feature detection have the common characteristic of searching for the best discriminative features The latter, has the advantage of determining their number automatically. The detection process is implemented through AR Modeling framework that exploits a criterion intrinsically related to ELM classifier properties This framework is formulated in such a way that it solves the model selection issue, i.e., to estimate the best values of the ELM classifier parameters, which are the regularization and kernel parameters.
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