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Using CNN and HHT to Predict Blood Pressure Level Based on Photoplethysmography and Its Derivatives
Xiaoxiao Sun1,2, Liang Zhou1, Shendong Chang3
1Xi'an Institute of Optics and Precision Mechanics of CAS, Xi'an 710119, China.
Insights
A new method using convolutional neural networks and the Hilbert-Huang Transform on photoplethysmography signals accurately predicts blood pressure risk levels. This approach offers a low-cost, continuous monitoring solution for hypertension management.
Area of Science:
- Biomedical Engineering
- Artificial Intelligence in Healthcare
- Cardiovascular Monitoring
Background:
- Hypertension affects over a billion people globally, with many lacking symptoms.
- Traditional blood pressure monitoring is insufficient for continuous assessment.
- There is a need for accessible, continuous, and low-cost blood pressure monitoring devices.
Purpose of the Study:
- To develop a novel method for predicting blood pressure risk levels.
- To utilize photoplethysmography (PPG) signals and their derivatives for blood pressure assessment.
- To evaluate the performance of a convolutional neural network (CNN) combined with the Hilbert-Huang Transform (HHT) for this task.
Main Methods:
- A dataset (PPG+) was created incorporating PPG signals and their derivatives, informed by their relation to vascular health.
- The Hilbert-Huang Transform (HHT) was applied to PPG signals.
- An 8-layer convolutional neural network (AlexNet) was employed for classification tasks on the PPG+ dataset.
Main Results:
- Classification experiments achieved high F1 scores: 98.90% for normotension vs. hypertension, 85.80% for normotension vs. prehypertension, and 93.54% for (normotension + prehypertension) vs. hypertension.
- The HHT-based dataset demonstrated strong performance in blood pressure grade prediction.
- The simple, periodic nature of PPG's Hilbert spectra favored the shallower AlexNet architecture.
Conclusions:
- The HHT method effectively enhances PPG data for blood pressure risk prediction.
- CNNs, particularly simpler architectures like AlexNet, can achieve high accuracy in blood pressure classification using PPG data.
- This approach offers a promising avenue for developing advanced, non-invasive blood pressure monitoring systems.
Abstract:
According to the WTO, there were 1.13 billion hypertension patients worldwide in 2015. The WTO encouraged people to check the blood pressure regularly because a large amount of patients do not have any symptoms. However, traditional cuff measurement results are not enough to represent the patient's blood pressure status over a period of time. Therefore, there is an urgent need for portable, easy to operate, continuous measurement, and low-cost blood pressure measuring devices. In this paper, we adopted the convolutional neural network (CNN), based on the Hilbert-Huang Transform (HHT) method, to predict blood pressure (BP) risk level using photoplethysmography (PPG). Considering that the PPG's first and second derivative signals are related to atherosclerosis and vascular elasticity, we created a dataset called PPG+; the images of PPG+ carry information on PPG and its derivatives. We built three classification experiments by collecting 582 data records (the length of each record is 10 s) from the Medical Information Mart for Intensive Care (MIMIC) database: NT (normotension) vs. HT (hypertension), NT vs. PHT (prehypertension), and (NT + PHT) vs. HT; the F1 scores of the PPG + experiments using AlexNet were 98.90%, 85.80%, and 93.54%, respectively. We found that, first, the dataset established by the HHT method performed well in the BP grade prediction experiment. Second, because the Hilbert spectra of the PPG are simple and periodic, AlexNet, which has only 8 layers, got better results. More layers instead increased the cost and difficulty of training.
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