Wavelet-based real-time calculation of multiple physiological parameters on an embedded platform

This paper aims to present how physiological signals can be processed based on wavelet decomposition to calculate multiple physiological parameters in real-time on an embedded platform.

An ECG and PPG are decomposed to the appropriate scale based on a quadratic spline wavelet base in order to obtain high and narrow pulse peaks at the location of the mutation points. Based on the decomposed waveforms, feature points are positioned to calculate physiological parameters in real-time, including heart rate, pulse rate, blood oxygen, and blood pressure. The proposed algorithm has been implemented on a Texas Instruments' CC2640R2F.

The misdetection rate of feature point location based on the square wavelet decomposition waveform is only 0.57% in the acquired ECG and 0.23% in the acquired PPG. Heart rate and pulse rate are both highly correlated with the reference, both having correlation coefficients of 0.99. The pulse rate and heart rate are 3.85% (51/1326) and 2.94% (39/1326) outside the 95% consistency limit, respectively. The systolic and diastolic blood pressures are significantly associated with standard equipment measurements, with correlation coefficients of 0.87 and 0.83. The systolic and diastolic blood pressures were 5.88% (21/357) and 5.32% (19/357) outside the 95% consistency limit, respectively.

The real-time calculation of multiple physiological parameters based on wavelet decomposition on an embedded platform presented here shows outstanding accuracy.