Title :
Cardiac arrhythmia detection using linear and non-linear features of HRV signal
Author :
Sivanantham, A. ; Shenbaga Devi, S.
Author_Institution :
Coll. of Eng., Anna Univ., Chennai, India
Abstract :
Earlier detection of Cardiac arrhythmias from long term ECG recording is one of the complex problems in signal processing. In this paper, we proposed an effective algorithm to detect and classify the cardiac abnormalities. By extracting different features in time domain, frequency domain and nonlinear features from heart rate variability (HRV) signals, the algorithm can differentiate between the types of arrhythmias. The features extracted from HRV signal are used to train and test the Support Vector Machine (SVM) classifier to classify Normal Beat, Premature Atrial Contraction (PAC), Right Bundle Branch Block (RBBB), and Paced Beat. The ECG signal is downloaded from MIT-BIH database. Training and testing of classification algorithm yields overall accuracy of 90.26%.
Keywords :
electrocardiography; feature extraction; frequency-domain analysis; medical signal processing; signal classification; support vector machines; time-domain analysis; ECG recording; ECG signal; HRV signal processing; MIT-BIH database; cardiac abnormalities classification; cardiac arrhythmia detection; feature extraction; frequency domain analysis; heart rate variability signals; nonlinear features; normal beat classification; paced beat classification; premature atrial contraction classification; right bundle branch block classification; support vector machine classifier; time domain analysis; Electrocardiography; Hafnium; Heart rate variability; Support vector machines; Electrocardiogram (ECG); Heart Rate Variability (HRV); Support Vector Machine (SVM);
Conference_Titel :
Advanced Communication Control and Computing Technologies (ICACCCT), 2014 International Conference on
Conference_Location :
Ramanathapuram
Print_ISBN :
978-1-4799-3913-8
DOI :
10.1109/ICACCCT.2014.7019200
