Smart Computing- Book ISBN- 978-81-953658-1-4

Authors:-Vishakha Khambhati, Dimpal Khambhati

Abstract :-Nowadays, for the diagnosis of several diseases and to limit some dangerous diseases to spread up, can be easily done with the help of physiological signals of human beings, such as temperature, blood pressure, heart rate, respiratory rate etc. For example, Heart rate precisely and reliably plays a significant role in the primary recognition of heart attack and several heart rate associated syndromes. The electrocardiogram (ECG) is an outstanding strategy that can be utilized to measure Heart Rate Variability (HRV). This paper depicts a strategy for handling electrocardiogram signals (ECG) to recognize Heart Rate Variability (HRV). The HRV gives data about the thoughtful parasympathetic autonomic dependability and subsequently about the danger of unpredicted heart demise. We have actualized our technique utilizing MATLAB on ECG signal which is gotten from MIT/BIH arrhythmia database. In this strategy, first the ECG signal is pre-processed by band-pass filter; later the Hilbert Transform is applied on filtered ECG signal to improve the nearness of QRS peak. By computing Hilbert transform and applying moving window integration R-Peaks are identified. R-Peaks are detected by setting a threshold and after that RR-intervals are calculated to determine Heart Rate. Our MATLAB implementation results in the detection of QRS Complexes in ECG signal, locate the R-Peaks, computes Heart Rate (HR) by calculating RR-internal and plotting of HR signal to show the information about HRV.

Keywords: - Physiological signal, ECG, QRS Complex, R Peaks, Heart rate, Hilbert transform, MIT-BIH Arrhythmia, MATLAB