Why the study?
Conventional cuff-based devices for measuring blood pressure limit continuous monitoring.
A novel 73 μW cuff-less BP estimation processor using PPG and DNN achieved highly accurate systolic and diastolic BP estimation, outperforming state-of-the-art algorithms.
Answers are grounded in this paper and cite their sources.
Does not support clinical adoption; leaves open validation of ultralow-power cuffless BP processors.
Blood pressure (BP) is considered one of the key vital signs that provide valuable medical information about cardiovascular activity. Conventionally, cuff-based devices are used to measure BP which limits their usage for continuous monitoring. This paper presents a cuff-less BP estimation processor using photoplethysmography (PPG) signals with a Deep Neural Network (DNN). Spectral and temporal features are extracted from the PPG signals and then used to train and evaluate the machine learning (ML) algorithms. The proposed algorithm is evaluated using the MIMIC II database for systolic blood pressure (SBP) and diastolic blood pressure (SBP) estimation. The proposed BP estimation processor is implemented using a 180nm CMOS process with an area of 3.45mm 2 and consumes 73 μ W. It achieves a mean absolute error in systolic BP of 0.0657 ± 4.7 mmHg and diastolic BP of 0.792 ± 4.61 mmHg which outperforms the state-of-the-art BP estimation algorithms.
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Rehman et al. (2022) studied this question.
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