Key result
1D Squeeze U-net model accurately estimates BP from raw PPG signals with ~4 mmHg systolic error.
Why the study?
Measuring continuous BP in real time via an mHealth application could advance healthcare, prompting development of a cuffless measurement method from a digital artery.
Does a 1D Squeeze U-net model using raw PPG signals accurately estimate continuous blood pressure in real time?
Does a 1D Squeeze U-net model using raw PPG signals accurately estimate continuous blood pressure in real time?
A novel 1D Squeeze U-net deep learning model using raw PPG signals accurately estimates continuous blood pressure, meeting British Hypertension Society and AAMI standards.
May support real-time cuffless BP monitoring; leaves open prospective validation before clinical use.
Measuring continuous blood pressure (BP) in real time by using a mobile health (mHealth) application would open a new door in the advancement of the healthcare system. This study aimed to propose a real-time method and system for measuring BP without using a cuff from a digital artery. An energy-efficient real-time smartphone-application-friendly one-dimensional (1D) Squeeze U-net model is proposed to estimate systolic and diastolic BP values, using only raw photoplethysmogram (PPG) signal. The proposed real-time cuffless BP prediction method was assessed for accuracy, reliability, and potential usefulness in the hypertensive assessment of 100 individuals in two publicly available datasets: Multiparameter Intelligent Monitoring in Intensive Care (MIMIC-I) and Medical Information Mart for Intensive Care (MIMIC-III) waveform database. The proposed model was used to build an android application to measure BP at home. This proposed deep-learning model performs best in terms of systolic BP, diastolic BP, and mean arterial pressure, with a mean absolute error of 4.42, 2.25, and 2.56 mmHg and standard deviation of 4.78, 2.98, and 3.21 mmHg, respectively. The results meet the grade A performance requirements of the British Hypertension Society and satisfy the AAMI error range. The result suggests that only using a short-time PPG signal is sufficient to obtain accurate BP measurements in real time. It is a novel approach for real-time cuffless BP estimation by implementing an mHealth application and can measure BP at home and assess hypertension.
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Athaya et al. (2022) studied Blood pressure estimation (n=100). 1D Squeeze U-net model using raw PPG signal was evaluated on Mean absolute error for systolic BP, diastolic BP, and mean arterial pressure. A 1D Squeeze U-net model using raw PPG signals accurately estimated systolic BP, diastolic BP, and MAP with mean absolute errors of 4.42, 2.25, and 2.56 mmHg, respectively.
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