Noninvasive Vital Signs Detection using Gaussian Pulse Signal

Monitoring vital signs helps evaluate health status and provides essential information about abnormal conditions. A pragmatic solution to measure both the respiration rate and heartbeat rate noninvasively is reported. The respiration rate is primarily estimated by FFT based power spectrum analysis and secondarily by the Burg method. The former provides high computational efficiency, and the latter offers enhanced robustness. The complexity increase is carefully controlled by clear switching rules between the two methods. Based on reliable respiratory rate estimation, the heartbeat rate is separated from the respiratory harmonics via a statistical operation by exploiting the ratio of candidate heartbeat peaks and respiratory frequency. Experimental tests on various people using a Gaussian pulsed based sensor show the effectiveness and accuracy.