Improving the Accuracy in Classification of Blood Pressure from Photoplethysmography Using Continuous Wavelet Transform and Deep Learning
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 learni...
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Series: | International Journal of Hypertension |
Online Access: | http://dx.doi.org/10.1155/2021/9938584 |
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doaj-b748f7a59142490699b94bb5c9b6ef512021-08-16T00:01:24ZengHindawi LimitedInternational Journal of Hypertension2090-03922021-01-01202110.1155/2021/9938584Improving the Accuracy in Classification of Blood Pressure from Photoplethysmography Using Continuous Wavelet Transform and Deep LearningJiaze Wu0Hao Liang1Changsong Ding2Xindi Huang3Jianhua Huang4Qinghua Peng5Institute of TCM DiagnosticsInstitute of TCM DiagnosticsSchool of Informatics and EngineeringSchool of Informatics and EngineeringInstitute of HerbsInstitute of TCM DiagnosticsBackground. 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.http://dx.doi.org/10.1155/2021/9938584 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jiaze Wu Hao Liang Changsong Ding Xindi Huang Jianhua Huang Qinghua Peng |
spellingShingle |
Jiaze Wu Hao Liang Changsong Ding Xindi Huang Jianhua Huang Qinghua Peng Improving the Accuracy in Classification of Blood Pressure from Photoplethysmography Using Continuous Wavelet Transform and Deep Learning International Journal of Hypertension |
author_facet |
Jiaze Wu Hao Liang Changsong Ding Xindi Huang Jianhua Huang Qinghua Peng |
author_sort |
Jiaze Wu |
title |
Improving the Accuracy in Classification of Blood Pressure from Photoplethysmography Using Continuous Wavelet Transform and Deep Learning |
title_short |
Improving the Accuracy in Classification of Blood Pressure from Photoplethysmography Using Continuous Wavelet Transform and Deep Learning |
title_full |
Improving the Accuracy in Classification of Blood Pressure from Photoplethysmography Using Continuous Wavelet Transform and Deep Learning |
title_fullStr |
Improving the Accuracy in Classification of Blood Pressure from Photoplethysmography Using Continuous Wavelet Transform and Deep Learning |
title_full_unstemmed |
Improving the Accuracy in Classification of Blood Pressure from Photoplethysmography Using Continuous Wavelet Transform and Deep Learning |
title_sort |
improving the accuracy in classification of blood pressure from photoplethysmography using continuous wavelet transform and deep learning |
publisher |
Hindawi Limited |
series |
International Journal of Hypertension |
issn |
2090-0392 |
publishDate |
2021-01-01 |
description |
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. |
url |
http://dx.doi.org/10.1155/2021/9938584 |
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