DOIONLINE 
DOIONLINE Publish In |
International Journal of Electrical, Electronics and Data Communication (IJEEDC)-IJEEDC |
 Journal Home Volume Issue |
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Issue |
Volume-6,Issue-8 ( Aug, 2018 ) | |||||||||
Paper Title |
Cardiac Arrhythmia Detection through ECG Signals | |||||||||
Author Name |
Ravina D. Edake, Anita Patil | |||||||||
Affilition |
M.Tech. 2nd Year Student, Cummins College of Engineering For Women, Survey No. 11/2, Cummins College Road, Karvenagar, Pune, Maharashtra 411052 Assistant Professor & Research Centre Coordinator, Cummins College of Engineering for Women, Survey No. 11/2, Cummins College Road, Karvenagar, Pune, Maharashtra 411052 | |||||||||
Pages |
61-64 | |||||||||
Abstract |
Electrocardiogram (ECG) signals are widely used for detecting & diagnosing the abnormalities related to heart (CVDs). ECG signal has number of cardiac cycles & each cardiac cycle has PQRST wave. Cardiovascular diseases are widely classified as hypertensive, rheumatic, congenital etc. One of the abnormalities related to heart is cardiac arrhythmia. In this paper, the ECG signals were taken from MITBIH arrhythmia database. To extract the features from the filtered signal, discrete wavelet transform was used. Finally, all the features calculated were given to classifiers such as K Nearest Neighbor & Support Vector Machine to classify the signals into normal & abnormal signal class. Keywords - Electrocardiogram (ECG), Cardiovascular Diseases (CVDs), beats per minute (bpm), Discrete Wavelet Transform (DWT), Daubechies Wavelet 4 (Db4), K Nearest Neighbor (KNN), Support Vector Machine (SVM), Accuracy (Acc), Sensitivity (Se), Specificity (Sp) | |||||||||
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