Classification of Cardiac Signals Using Time Domain Methods

Electrocardiography (ECG) deals with the electrical activity of the heart. The condition of cardiac health is given by ECG and heart rate. A study of the non-linear dynamics of ECG signals for arrhythmia characterization is considered. The statistical analysis of the calculated features indicate that they differ significantly between normal heart rhythm and the different arrhythmia types and hence, can be rather useful in ECG arrhythmia detection. The discrimination of ECG signals using statistical parameters is of crucial importance in the cardiac disease therapy. The four statistical parameters considered for cardiac arrhythmia classification of the ECG signals are the standard deviation of the NN intervals (SDNN), the standard deviation of differences between adjacent NN intervals (SDSD), the root mean square successive difference of intervals which are extracted from heart rate signals (RMSSD) and the proportion derived by dividing NN50 by the total number of NN intervals (pNN50). The inclusion of Adaptive neuro fuzzy interface system (ANFIS) in the complex investigating algorithms yield very interesting recognition and classification capabilities across a broad spectrum of biomedical problem domains. Using the computed statistical parameter classification was done using Analytical method and an accuracy of 66% was achieved. The ANFIS method was compared with Analytical method. ANFIS classifier was used for the classification and an accuracy of 94% was achieved which shows that ANFIS classifier is the best of the two methods compared.