Motion-resistant pulse oximetry

The measurement of vital signs ? such as peripheral capillary oxygen saturation (SpO2) and heart rate (HR) levels ? by a pulse oximeter is studied. The pulse oximeter is a non-invasive device that measures photoplethysmography (PPG) signals and extracts vital signs from them. However, the quality of the PPG signal measured by oximetry sensors is known to deteriorate in the presence of substantial human and sensor movements contributing to the measurement noise. Methods to suppress such noise from PPG signals measured by an oximeter and to calculate the associated vital signs with high accuracy even when the wearer is under substantial motion are presented in this study. The spectral components of the PPG waveform are known to appear at a fundamental frequency that corresponds to the participant\u27s HR and at its harmonics. To match this signal, a time-varying comb filter tuned to the participant\u27s HR is employed. The filter captures the HR components and eliminates most other artifacts. A significant improvement in the accuracy of SpO2 calculated from the comb-filtered PPG signals is observed, when tested on data collected from human participants while they are at rest and while they are exercising. In addition, an architecture that integrates SpO2 levels from multiple PPG channels mounted on different parts of the wearer\u27s arm is presented. The SpO2 levels are integrated using a Kalman filter that uses past measurements and modeling of the SpO2 dynamics to attenuate the effect of the motion artifacts. Again, data collected from human participants while they are at rest and while they are exercising are used. The integrated SpO2 levels are shown to be more accurate and reliable than those calculated from individual channels. Motion-resistant algorithms typically require an additional noise reference signal to produce high quality vital signs such as HR. A framework that employs PPG sensors only ? one in the green and one in the infrared spectrum ? to compute high quality HR levels is developed. Our framework is tested on experimental data collected from human participants while at rest and while running at various speeds. Our PPG-only framework generates HR levels with high accuracy and low computational complexity as compared to leading HR calculation methods in the literature that require the availability of a noise reference signal. The methods for SpO2 and HR calculation presented in this study are desirable since (1) they yield high accuracy in estimating vital signs under substantial level of motion artifacts and (2) they are computationally efficient, (and therefore are capable to be implemented in wearable devices)

Dual Wavelength Photoplethysmography Framework for Heart Rate Calculation

The quality of heart rate (HR) measurements extracted from human photoplethysmography (PPG) signals are known to deteriorate under appreciable human motion. Auxiliary signals, such as accelerometer readings, are usually employed to detect and suppress motion artifacts. A 2019 study by Yifan Zhang and his coinvestigatorsused the noise components extracted from an infrared PPG signal to denoise a green PPG signal from which HR was extracted. Until now, this approach was only tested on “micro-motion” such as finger tapping. In this study, we extend this technique to allow accurate calculation of HR under high-intensity full-body repetitive “macro-motion”. Our Dual Wavelength (DWL) framework was tested on PPG data collected from 14 human participants while running on a treadmill. The DWL method showed the following attributes: (1) it performed well under high-intensity full-body repetitive “macro-motion”, exhibiting high accuracy in the presence of motion artifacts (as compared to the leading accelerometer-dependent HR calculation techniques TROIKA and JOSS); (2) it used only PPG signals; auxiliary signals such as accelerometer signals were not needed; and (3) it was computationally efficient, hence implementable in wearable devices. DWL yielded a Mean Absolute Error (MAE) of 1.22|0.57 BPM, Mean Absolute Error Percentage (MAEP) of 0.95|0.38%, and performance index (PI) (which is the frequency, in percent, of obtaining an HR estimate that is within ±5 BPM of the HR ground truth) of 95.88|4.9%. Moreover, DWL yielded a short computation period of 3.0|0.3 s to process a 360-second-long run