Key result
A chair-shaped ballistocardiogram-based system using convolutional neural networks accurately estimated blood pressure at rest, satisfying AAMI international standards.
Why the study?
Continuous blood pressure monitoring systems are required to accurately diagnose hypertension.
Does a chair-shaped ballistocardiogram-based system accurately estimate systolic and diastolic blood pressure in resting and recovery states?
Does a chair-shaped ballistocardiogram-based system accurately estimate systolic and diastolic blood pressure in resting and recovery states?
A novel chair-shaped BCG system using convolutional neural networks can accurately estimate resting blood pressure according to AAMI standards, offering a potential continuous, sensor-free monitoring solution.
May enable unobtrusive BP monitoring; leaves open large-scale validation before clinical adoption.
Hypertension is a chronic disease that kills 7.6 million people worldwide annually. A continuous blood pressure monitoring system is required to accurately diagnose hypertension. Here, a chair-shaped ballistocardiogram (BCG)-based blood pressure estimation system was developed with no sensors attached to users. Two experimental sessions were conducted with 30 subjects. In the first session, two-channel BCG and blood pressure data were recorded for each subject. In the second session, the two-channel BCG and blood pressure data were recorded after running on a treadmill and then resting on the newly developed system. The empirical mode decomposition algorithm was used to remove noise in the two-channel BCG, and the instantaneous phase was calculated by applying a Hilbert transform to the first intrinsic mode functions. After training a convolutional neural network regression model that predicts the systolic and diastolic blood pressures (SBP and DBP) from the two-channel BCG phase, the results of the first session (rest) and second session (recovery) were compared. The results confirmed that the proposed model accurately estimates the rapidly rising blood pressure in the recovery state. Results from the rest sessions satisfied the Association for the Advancement of Medical Instrumentation (AAMI) international standards. The standard deviation of the SBP results in the recovery session exceeded 0.7.
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Seok et al. (2021) studied this question. Chair-shaped ballistocardiogram (BCG)-based blood pressure estimation system was evaluated on Systolic and diastolic blood pressure (SBP and DBP) prediction. A chair-shaped ballistocardiogram-based system using convolutional neural networks accurately estimated blood pressure at rest, satisfying AAMI international standards.
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