The following information was submitted:
Transactions: WSEAS TRANSACTIONS ON COMPUTERS
Transactions ID Number: 52-403
Full Name: Karpagachelvi Sakthivel
Position: Ph.D. Candidate
Age: ON
Sex: Female
Address: 5/20, sivalingam pillai layout, s.v. mills (po), Udumalpet
Country: INDIA
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E-mail address: karpagachelvis@yahoo.com
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Title of the Paper: classification of ecg signals using extreme learning machine
Authors as they appear in the Paper: Karpagachelvi Sakthivel, Arthanari Mariappan,Sivakumar Madesan
Email addresses of all the authors: karpagachelvis@yahoo.com ,arthanarimsvc@gmail.com,sivala@gmail.com
Number of paper pages: 10
Abstract: An Electrocardiogram or ECG is an electrical recording of the heart and is used in the investigation of heart disease. 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 to show the superiority of the generalization capability of the Extreme Learning Machine (ELM) compared with support vector machine (SVM) approach in the automatic classification of ECG beats. The generalization performance of the SVM classifier is not sufficient for the correct classification of ECG signals. To overcome this problem the ELM 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 t!
he 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 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.
Keywords: Electrocardiogram (ECG) signals classification, Feature detection, Feature reduction, Generalization capability, Model selection issue, Extreme Learning Machine (ELM), Support Vector Machine (SVM).
EXTENSION of the file: .pdf
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