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. 2021 Aug 5:2021:9938584.
doi: 10.1155/2021/9938584. eCollection 2021.

Improving the Accuracy in Classification of Blood Pressure from Photoplethysmography Using Continuous Wavelet Transform and Deep Learning

Jiaze Wu  1 , Hao Liang  1   2 , Changsong Ding  3 , Xindi Huang  3 , Jianhua Huang  4 , Qinghua Peng  1   2
Affiliations

Improving the Accuracy in Classification of Blood Pressure from Photoplethysmography Using Continuous Wavelet Transform and Deep Learning

Jiaze Wu et al. Int J Hypertens. .

Abstract

Background: Continuous wavelet transform (CWT) based scalogram can be used for photoplethysmography (PPG) signal transformation to classify blood pressure (BP) with deep learning. We aimed to investigate the determinants that can improve the accuracy of BP classification based on PPG and deep learning and establish a better algorithm for the prediction.

Methods: The dataset from PhysioNet was accessed to extract raw PPG signals for testing and its corresponding BPs as category labels. The BP category of normal or abnormal followed the criteria of the 2017 American College of Cardiology/American Heart Association (ACC/AHA) Hypertension Guidelines. The PPG signals were transformed into 224 ∗ 224 ∗ 3-pixel scalogram via different CWTs and segment units. All of them are fed into different convolutional neural networks (CNN) for training and validation. The receiver-operating characteristic and loss and accuracy curves were used to evaluate and compare the performance of different methods.

Results: Both wavelet type and segment length could affect the accuracy, and Cgau1 wavelet and segment-300 revealed the best performance (accuracy 90%) without obvious overfitting. This method performed better than previously reported MATLAB Morse wavelet transformed scalogram on both of our proposed CNN and CNN-GoogLeNet.

Conclusions: We have established a new algorithm with high accuracy to predict BP classification from PPG via matching of CWT type and segment length, which is a promising solution for rapid prediction of BP classification from real-time processing of PPG signal on a wearable device.

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Conflict of interest statement

Jiaze Wu, Hao Liang, Xindi Huang, Jianghua Huang, and Qinghua Peng declare that they have no conflicts of interest.

Figures

Figure 1
Figure 1
The transformed colorful 2D scalograms by different wavelets.
Figure 2
Figure 2
The layers of the proposed convolutional neural networks.
Figure 3
Figure 3
The accuracies of different algorithms by matching CWTs with different segment lengths.
Figure 4
Figure 4
The receiver-operating characteristic curves of cgau1 and segment-300; gaus1 and segment-250; and mexh and segment-250 for prediction of blood pressure classification.
Figure 5
Figure 5
The image examples transformed from cgau1 for different blood pressure categories.
Figure 6
Figure 6
The accuracy of cgau1 and segment-300 and MATLAB scalogram and segment-300 in our proposed CNN from the receiver-operating characteristic curves.
Figure 7
Figure 7
The loss and accuracy training process of cgau1 and segment-300 and MATLAB scalogram and segment-300 in CNN-GoogLeNet by transfer learning.
See this image and copyright information in PMC

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