HRV ANALYSIS USING TIME DOMAIN METHODS

Abstract

Abstract - Electrocardiography (ECG) deals with the electrical activity of the heart. The condition of cardiac health is given by ECG and heart rate. The useful information about the functionality of the human heart is provided by the ECG interpretation. The intervals and amplitudes of the ECG waves describe the different features required for the ECG signal analysis such as statistical feature, morphological feature, and temporal features etc. The P-QRS-T waves in ECG signal represent one cardiac cycle and the normal heartbeat ranges between 60 to100 beats per minute .The Heart Rate Variability (HRV) has attracted considerable attention in psychology, medicine and has become important dependent measure in psychophysiology and behavioral medicine. 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 adjacent RR intervals mean, 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 mean hrv is also calculated.. Keywords: arrhythmia detection, ECG, heart rate, SDNN, RR MEAN, RMSD, NN50