ECG SIGNAL CLASSIFICATION BASED ON STATISTICAL FEATURES WITH SVM CLASSIFICATION

• Main Author: Vijaya Arjunan R
• Format: Article
• Language: English
• Published: XLESCIENCE 2016-06-01
• Series: International Journal of Advances in Signal and Image Sciences
• Subjects: ecg signal, arrhythmia, statistical features, svm
• Online Access: https://xlescience.org/index.php/IJASIS/article/view/8

An important diagnostic technique to detect the abnormalities in the human heart is Electrocardiogram (ECG). The growing number of heart patients increases the physicians work load. To reduce their work load, a computerized automated detection system is required. In this paper, a computerized system is presented to categorize the ECG signals. MIT-BIH ECG arrhythmia database is used for analysis purpose. After de-noising the ECG signal in the preprocessing stage, the following features; mean, variance, standard deviation, and skewness are extracted in the feature extraction stage and Support Vector Machine (SVM) is developed to classify the ECG signal into two categories; normal or abnormal. Results show that the system classifies the given ECG signal with 90% of sensitivity and specificity as well.