PCA-Based Multi-Wavelength Photoplethysmography Algorithm for Cuffless Blood Pressure Measurement on Elderly Subjects

The prevalence of hypertension has made blood pressure (BP) measurement one of the most wanted functions in wearable devices for convenient and frequent self-assessment of health conditions. The widely adopted principle for cuffless BP monitoring is based on arterial pulse transit time (PTT), which...

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Vydané v:IEEE journal of biomedical and health informatics Ročník 25; číslo 3; s. 663 - 673
Hlavní autori: Liu, Jing, Qiu, Shirong, Luo, Ningqi, Lau, Sze-Kei, Yu, Hui, Kwok, Timothy, Zhang, Yuan-Ting, Zhao, Ni
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States IEEE 01.03.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Algorithms
Biomedical monitoring
Blood
Blood pressure
cuffless blood pressure measurement
EKG
Electrocardiography
Hypertension
Lag time
Medical equipment
multi-wavelength photoplethysmography
Older people
Pressure measurement
Principal component analysis
Principal components analysis
Reactive power
Self-assessment
Sensors
Skin
Statistical analysis
Telemedicine
Transit time
Waveforms
Wavelength
Wearable technology
ISSN:2168-2194, 2168-2208, 2168-2208
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Popis
Shrnutí:The prevalence of hypertension has made blood pressure (BP) measurement one of the most wanted functions in wearable devices for convenient and frequent self-assessment of health conditions. The widely adopted principle for cuffless BP monitoring is based on arterial pulse transit time (PTT), which is measured with electrocardiography and photoplethysmography (PPG). To achieve cuffless BP monitoring with more compact wearable electronics, we have previously conceived a multi-wavelength PPG (MWPPG) strategy to perform BP estimation from arteriolar PTT, requiring only a single sensing node. However, challenges remain in decoding the compounded MWPPG signals consisting of both heterogeneous physiological information and motion artifact (MA). In this work, we proposed an improved MWPPG algorithm based on principal component analysis (PCA) which matches the statistical decomposition results with the arterial pulse and capillary pulse. The arteriolar PTT is calculated accordingly as the phase shift based on the entire waveforms, instead of local peak lag time, to enhance the feature robustness. Meanwhile, the PCA-derived MA component is employed to identify and exclude the MA-contaminated segments. To evaluate the new algorithm, we performed a comparative experiment (N = 22) with a cuffless MWPPG measurement device and used double-tube auscultatory BP measurement as a reference. The results demonstrate the accuracy improvement enabled by the PCA-based operations on MWPPG signals, yielding errors of 1.44 ± 6.89 mmHg for systolic blood pressure and −1.00 ± 6.71 mm Hg for diastolic blood pressure. In conclusion, the proposed PCA-based method can improve the performance of MWPPG in wearable medical devices for cuffless BP measurement.
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ISSN:2168-2194
2168-2208
2168-2208
DOI:10.1109/JBHI.2020.3004032