Key result
Lightweight neural network on ultra-low-power ASIC estimates systolic BP with ~2.5 mmHg error.
Why the study?
Blood pressure estimation is essential to prevent hypertension, creating a need for low-power designs for wearable blood pressure estimation devices.
A lightweight artificial neural network implemented on an ultra-low-power ASIC demonstrates high accuracy for real-time wearable blood pressure estimation.
Answers are grounded in this paper and cite their sources.
Should not yet change practice for BP monitoring; leaves open on-device neural networks in wearables pending clinical validation.
Blood pressure (BP) is an important indicator of the state of cardiovascular health. BP estimation is an essential method to prevent the occurrence of hypertension. Currently, there is a strong focus on low power design for a wearable BP estimation device. This paper proposes a lightweight artificial neural network (ANN) for BP estimation and implements it on an ultra-low-power application-specific integrated circuit (ASIC). On the test set, the mean absolute error (MAE) and standard deviation (SD) of the estimated systolic BP and diastolic BP are 2.47 ± 3.48 mmHg and 1.45 ± 1.88 mmHg. Besides, in the case of 8-bit quantization, the MAE ± SD of the estimated systolic BP and diastolic BP are 12.41 ± 5.32 mmHg and 6.29 ± 3.03 mmHg respectively. The regression result R 2 of overall SBP and DBP is 0.9702. This ASIC whose power is 19.72 µW is validated via the 0.18 µm CMOS process, occupying an area of 730 µmx 730 µm.
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Zhang et al. (2022) studied Blood pressure estimation. Lightweight artificial neural network (ANN) on an ultra-low-power ASIC was evaluated on Mean absolute error (MAE) of estimated systolic and diastolic BP (R2 0.9702). A lightweight artificial neural network on an ultra-low-power ASIC estimated systolic and diastolic blood pressure with a mean absolute error of 2.47 and 1.45 mmHg, respectively (overall R2 0.9702).
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