Key result
A compound multi-channel convolutional neural network model using PPG signal features provided blood pressure estimation results closer to actual values than traditional machine learning methods.
Why the study?
Most neural networks for blood pressure estimation require both ECG and PPG, but ECG acquisition requires electrodes and wires that reduce the convenience of signal acquisition.
Does a compound multi-channel convolutional neural network model using PPG features improve blood pressure estimation compared to traditional machine learning methods?
Does a compound multi-channel convolutional neural network model using PPG features improve blood pressure estimation compared to traditional machine learning methods?
A novel compound multi-channel convolutional neural network model using single PPG measurement features improves continuous non-invasive blood pressure estimation over traditional machine learning methods.
May enable refined non-invasive BP monitoring; leaves open clinical adoption pending prospective validation.
Photoplethysmography (PPG) changes according to arterial blood pressure (ABP). The change in the physiological signal reflects the change in the functional state of the heart and vascular system. To prevent cardiovascular disease due to hypertension, the PPG signal has been widely used for non-invasive continuous estimation of blood pressure. In recent years, artificial neural network-based algorithms have found the relationship between ABP and related physiological signals. Compared with traditional cuff measurement methods, it is more intuitive to understand the overall blood pressure trend and avoid invasive pressure Measured bleeding risk. Most artificial neural networks use an electrocardiogram (ECG) and PPG at the same time, and the ECG acquisition device needs electrodes and wires to the chest cavity, which indirectly affects the convenience of physiological signal acquisition. Using three feature extraction techniques with different detailed attributes for a single PPG measurement and substituting short-time Fourier transform (STFT), continuous wavelet transform (CWT), and Hilbert-Huang transform (HHT) into the "compound multi-channel convolutional neural network(CMCNN)" model, we evaluate the overall blood pressure value. Considering that the distorted data caused by motion artifacts causes the training results to fail to converge, we select Physionet's MIMIC-III database and use data purification-based processing steps. We screen out the artifact labels for PPG and ABP at first and then use the artifact labels to perform seven-level data segmentation. Finally, using STFT, CWT, and HHT, we obtain spectrogram, scalogram, and Hilber spectrum. The original waveform of PPG and the spectrum, the scalogram, and the Hilbert spectrum are used as input data for learning and analysis. Compare with the traditional machine learning method, the blood pressure estimation results is closer to the actual situation.
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Lu et al. (2023) studied Blood pressure measurement. Compound multi-channel convolutional neural network (CMCNN) model using PPG signal features vs. Traditional machine learning method was evaluated on Blood pressure estimation. A compound multi-channel convolutional neural network model using PPG signal features provided blood pressure estimation results closer to actual values than traditional machine learning methods.
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