Robust Algorithm for Heart Rate (HR) Detection and Heart Rate Variability (HRV) Estimation

Abstract

We present algorithm for Heart Rate detection based on Short-Term Autocorrelation Center Clipping method. This algorithm is dedicated for biological signal detection, electrocardiogram, in noisy environment with lot of artifacts. Using this algorithm is also possible detect the R pointers in the PQRST complex of the ECG signal. In this paper the new implementation of the heart rate variability estimation is also presented. HRV module is based on parametric and non-parametric methods of the power spectral density computation. This paper presents algorithm for Heart Rate (HR) detection used in electrocardiogram monitoring system as well as algorithm for Heart Rate Variability (HRV) estimation. During the training dedicated flights, military pilots are monitored for checking their susceptibility to stress and self-control. ECG data-logger as a personal portable device is collecting biological signal during the testing flight. Computed HR values together with Accumulated Reference Pattern PQRST complex (ARP) are estimates of pilot’s health. ECG analysis is performed using Short-term Autocorrelation Center Clipping (SACC) method. SACC method dedicated for HR detection and ECG R-pointers analysis is very robust for the noisy environment. HRV parameters estimation is based on advanced spectral analysis of electrocardiogram (tachogram of RR values). HRV estimation is noninvasive method of estimation the Sympathetic and Parasympathetic Nervous System influence on the heart rate.