HEART RATE DETECTION USING HILBERT TRANSFORM

Abstract

The electrocardiogram (ECG) is a well known method that can be used to measure Heart Rate Variability (HRV). This paper describes a procedure for processing electrocardiog ram signals (ECG) to detect Heart Rate Variability (HRV). In recent years, there have been wide-ranging studies on Heart rate variab ility in ECG signals and analysis of Respiratory Si nus Arrhythmia (RSA). Normally the Heart rate variability is studied base d on cycle length variability, heart period variabi lity, RR variability and RR interval tachogram. The HRV provides information ab out the sympathetic-parasympathetic autonomic stabi lity and consequently about the risk of unpredicted cardiac death. The he art beats in ECG signal are detected by detecting R -Peaks in ECG signals and used to determine useful information about the vari ous cardiac abnormalities. The temporal locations o f the R-wave are identified as the locations of the QRS complexes. In the presence of poor signal-to-noise ratios or pathological sig nals and wrong placement of ECG electrodes, the QRS complex may be missed or fa lsely detected and may lead to poor results in calc ulating heart beat in turn inter-beat intervals. We have studied the effects o f number of common elements of QRS detection method s using MIT/BIH arrhythmia database and devised a simple and effective method. In this method, first the ECG signal is preprocess ed using band-pass filter; later the Hilbert Transform is applied on filtered ECG si gnal to enhance the presence of QRS complexes, to d etect R-Peaks by setting a threshold and finally the RR-intervals are calcul ated to determine Heart Rate. We have implemented o ur method using MATLAB on ECG signal which is obtained from MIT/BIH arrhythmi a database. Our MATLAB implementation results in t he detection of QRS complexes in ECG signal, locate the R-Peaks, comput es Heart Rate (HR) by calculating RR-internal and pof HR signal to show the information about HRV.