Rapid Vital Sign Extraction for Real-Time Opto-Physiological Monitoring at Varying Physical Activity Intensity Levels

• Published in: IEEE journal of biomedical and health informatics Vol. 27; no. 7; pp. 3107 - 3118
• Main Authors: Zheng, Xiaoyu, Dwyer, Vincent M., Barrett, Laura A., Derakhshani, Mahsa, Hu, Sijung
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.07.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accelerometers; Biomedical monitoring; Datasets; Electrocardiography; Exercise; Exercise - physiology; Frequency measurement; Heart rate; Heart Rate - physiology; Humans; Mathematical analysis; minimum residual (MR); Monitoring; Monitoring, Physiologic; motion artefacts (MAs); multi-wavelength illumination optoelectronic patch sensor (mOPES); Optoelectronic devices; Oxygen content; Photoplethysmography; Photoplethysmography (PPG); Physical activity; Physiology; Real time; real-time health monitoring; Real-time systems; Reference signals; Signal processing; Signal Processing, Computer-Assisted; Signal quality; triaxial accelerometer; Wavelength; Wearable Electronic Devices
• ISSN: 2168-2194; 2168-2208; 2168-2208
• DOI: 10.1109/JBHI.2023.3268240

Robustness of physiological parameters obtained from photoplethysmographic (PPG) signals is highly dependent on a signal quality that is often affected by the motion artefacts (MAs) generated during physical activity. This study aims to suppress MAs and obtain reliable physiological readings using the part of the pulsatile signal, captured by a multi-wavelength illumination optoelectronic patch sensor (mOEPS), that minimizes the residual between the measured signal and the motion estimates obtained from an accelerometer. The minimum residual (MR) method requires the simultaneous collection of (1) multiple wavelength data from the mOEPS, and (2) motion reference signals from a triaxial accelerometer attached to the mOEPS. The MR method suppresses those frequencies associated with motion in a manner that is easily embedded on a microprocessor. The performance of the method in reducing both in-band and out-of-band frequencies of MAs is evaluated through two protocols with 34 subjects engaged in the study. The MA-suppressed PPG signal, obtained through MR, enables the calculation of the heart rate (HR) with an average absolute error of 1.47 beats/min for the IEEE-SPC datasets, and the calculation of HR and respiration rate (RR) to 1.44 beats/min and 2.85 breaths/min respectively for our in-house datasets. Oxygen saturation (SpO<inline-formula><tex-math notation="LaTeX">_{2}</tex-math></inline-formula>) levels calculated from the minimum residual wave forms were consistently <inline-formula><tex-math notation="LaTeX">\geq \text{95}\%</tex-math></inline-formula>. The comparison with the reference HR and RR show errors with an absolute accuracy of <inline-formula><tex-math notation="LaTeX">< \text{5}\%</tex-math></inline-formula> and the Pearson correlation (<inline-formula><tex-math notation="LaTeX">R</tex-math></inline-formula>) for HR and RR are 0.9976 and 0.9118, respectively. These outcomes demonstrate that MR is capable of effective suppression of MAs for a range of physical activity intensities and to achieve real-time signal processing for wearable health monitoring.