Paper Title
Cardiac Arrhythmia Detection through ECG Signals

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)