Classifying Normal Sinus Rhythm and Cardiac Arrhythmias in ECG Signals Using Statistical Features in Temporal Domain

Any morphological abnormality or atypical group of conditions in a cardiac rhythm indicates a typical class of arrhythmias. ECG plays a vital role in diagnosis of many such cardiac disorders. Some arrhythmias including ventricular fibrillation and premature ventricular contraction can be fatal if not dealt on time. Clinical analysis of ECGs by physicians may result into an inaccurate as well as a time-consuming analysis of a critically serious arrhythmia patient mostly involving measuring the ECG statistics from calipers. Considering the importance of morphological shapes and statistics of ECG signals in arrhythmia diagnosis, these features are input to a dedicated system which act as key markers to categorize various arrhythmias automatically. This paper highlights the development of an algorithm for classifying 15 different cardiac arrhythmias using a novel statistical feature set of ECG signals in time domain. Rhythm annotations from the bench mark MIT-BIH Cardiac Arrhythmia database have been used to organize the data as rhythms. The proposed method has been simulated and tested in MATLAB and results have been discussed in detail.