Accurate Estimation of Heart and Respiration Rates Based on an Optical Fiber Sensor Using Adaptive Regulations and Statistical Classifications Spectrum Analysis

The aim of this work is to present a method for accurate estimating of heart and respiration rate under any condition based on a mattress which was integrated an optical fiber sensor into. During the estimation, Ballistocardiogram (BCG) signal, which is obtained from the optical fiber sensor, is used for the extracting the heart rate and the respiration rate. However, due to the detrimental effects of differential detector, self-interference and variation of installation status of the sensor, ballistocardiogram (BCG) signal is difficult to be detected. In order to resolve the potential concerns of individual differences and body interferences, adaptive regulations and statistical classifications spectrum analysis are used in this paper. Experiments are carried out to quantify heart and respiration rates of healthy volunteers under different breathing and postures conditions. From the experimental results, it could be concluded that :1) the heart rates of 40-150bpm and respiration rates of 10-20bpm are measured for individual differences; 2) for the same individuals under four different posture contacts ,the mean error of heart rates are separately 1.60±0.98 beats per minute (bpm), 1.94±0.83bpm, 1.24±0.59bpm and 1.06±0.62 bpm ,in contrast, the mean error of polar beat device are 1.09±0.96bpm, 1.44±0.99bpm, 1.78±0.94bpm. Furthermore, the experimental results are validated by conventional counterparts which use the skin-contacting electrodes as their measurements. It was reported that the heart rate is 0.26±2.80 bpm in 95% confidence intervals (± 1.96SD) in comparison with Philips sure-signs of the VM6 medical monitor and the respiration rate is 0.41 ± 1.49 bpm in 95% confidence intervals (± 1.96SD) in comparison with ECG-derived Respiratory (EDR) for respiration rates. It is indicated that the developed system using adaptive regulations and statistical classifications spectrum analysis performs better and can be used under complex environments senselessly .