Key result
CNN-based model using ECG and PPG achieves ~5.8 mm Hg error for systolic BP.
Why the study?
Measuring blood pressure with wearable watches requires models that accommodate limited computing power for signal processing and input signals subject to noise interference.
Can a convolutional neural network accurately estimate blood pressure using ECG and PPG signals without calibration?
Can a convolutional neural network accurately estimate blood pressure using ECG and PPG signals without calibration?
A CNN-based model using ECG and PPG signals can accurately estimate blood pressure without calibration, with ECG signals demonstrating greater robustness to noise than PPG signals.
May enable calibration-free BP monitoring from ECG/PPG; leaves open prospective validation before clinical adoption.
In this study, we conducted research on a deep learning-based blood pressure (BP) estimation model suitable for wearable environments. To measure BP while wearing a wearable watch, it needs to be considered that computing power for signal processing is limited and the input signals are subject to noise interference. Therefore, we employed a convolutional neural network (CNN) as the BP estimation model and utilized time-series electrocardiogram (ECG) and photoplethysmogram (PPG) signals, which are quantifiable in a wearable context. We generated periodic input signals and used differential and thresholding methods to decrease noise in the preprocessing step. We then applied a max-pooling technique with filter sizes of 2 × 1 and 5 × 1 within a 3-layer convolutional neural network to estimate BP. Our method was trained, validated, and tested using 2.4 million data samples from 49 patients in the intensive care unit. These samples, totaling 3.1 GB were obtained from the publicly accessible MIMIC database. As a result of a test with 480,000 data samples, the average root mean square error in BP estimation was 3.41, 5.80, and 2.78 mm Hg in the prediction of pulse pressure, systolic BP (SBP), and diastolic BP (DBP), respectively. The cumulative error percentage less than 5 mm Hg was 68% and 93% for SBP and DBP, respectively. In addition, the cumulative error percentage less than 15 mm Hg was 98% and 99% for SBP and DBP. Subsequently, we evaluated the impact of changes in input signal length (1 cycle vs. 30 s) and the introduction of noise on BP estimation results. The experimental results revealed that the length of the input signal did not significantly affect the performance of CNN-based analysis. When estimating BP using noise-added ECG signals, the mean absolute error (MAE) for SBP and DBP was 9.72 and 6.67 mm Hg, respectively. Meanwhile, when using noise-added PPG signals, the MAE for SBP and DBP was 26.85 and 14.00 mm Hg, respectively. Therefore, this study confirmed that using ECG signals rather than PPG signals is advantageous for noise reduction in a wearable environment. Besides, short sampling frames without calibration can be effective as input signals. Furthermore, it demonstrated that using a model suitable for information extraction rather than a specialized deep learning model for sequential data can yield satisfactory results in BP estimation.
No takes yet. Share an insight, caveat, or question.
Cho et al. (2023) studied Blood pressure estimation (n=49). Convolutional neural network (CNN) based blood pressure estimation was evaluated on Root mean square error in blood pressure estimation. A CNN-based blood pressure estimation model achieved an average root mean square error of 5.80 mm Hg for systolic and 2.78 mm Hg for diastolic blood pressure.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: