An Effective Algorithms for Measuring Heart Rate Variability

Electrocardiogram (ECG) signal provides valuable information about the functional aspects of the cardiovascular system. Cardiac arrhythmia is a condition of abnormal activity in the heart which could be analyzed by ECG signal analysis. Ventricular premature complexes (VPCs) detected have been using wavelet transforms features. Atrial premature complexes (APCs) were detected on small ECG datasets using autoregressive (AR) coefficients as features and generalized linear model classifier. We proposed an algorithm based on wavelet transforms and for detecting ECG characteristic points suchas Regular rhythmatarate of 60–100 bpm. Each QRS complex is preceded by a normal P wave. Normal P wave axis: P waves should be upright in leads I and II, inverted in aVR. The PR interval remains constant. QRS complexesare < 100 ms wide. With the multiscale feature of wavelets, the QRS complex can be distinguished from high P or T waves, noise, baseline drift, and artifacts. The detection rate of QRS complexes and the P and T waves can also be detected, even with serious base line drift and noise. The proposed wavelet algorithm is applied to different ECG samples (1-normal, 2-abormal) and the HRV is determined. Results demonstrate the effectiveness of our algorithm in detecting cardiac arrhythmia