Cargando…
Using CNN and HHT to Predict Blood Pressure Level Based on Photoplethysmography and Its Derivatives
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...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8070388/ https://www.ncbi.nlm.nih.gov/pubmed/33924324 http://dx.doi.org/10.3390/bios11040120 |
_version_ | 1783683458472607744 |
---|---|
author | Sun, Xiaoxiao Zhou, Liang Chang, Shendong Liu, Zhaohui |
author_facet | Sun, Xiaoxiao Zhou, Liang Chang, Shendong Liu, Zhaohui |
author_sort | Sun, Xiaoxiao |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8070388 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-80703882021-04-26 Using CNN and HHT to Predict Blood Pressure Level Based on Photoplethysmography and Its Derivatives Sun, Xiaoxiao Zhou, Liang Chang, Shendong Liu, Zhaohui Biosensors (Basel) Article 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. MDPI 2021-04-13 /pmc/articles/PMC8070388/ /pubmed/33924324 http://dx.doi.org/10.3390/bios11040120 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Sun, Xiaoxiao Zhou, Liang Chang, Shendong Liu, Zhaohui Using CNN and HHT to Predict Blood Pressure Level Based on Photoplethysmography and Its Derivatives |
title | Using CNN and HHT to Predict Blood Pressure Level Based on Photoplethysmography and Its Derivatives |
title_full | Using CNN and HHT to Predict Blood Pressure Level Based on Photoplethysmography and Its Derivatives |
title_fullStr | Using CNN and HHT to Predict Blood Pressure Level Based on Photoplethysmography and Its Derivatives |
title_full_unstemmed | Using CNN and HHT to Predict Blood Pressure Level Based on Photoplethysmography and Its Derivatives |
title_short | Using CNN and HHT to Predict Blood Pressure Level Based on Photoplethysmography and Its Derivatives |
title_sort | using cnn and hht to predict blood pressure level based on photoplethysmography and its derivatives |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8070388/ https://www.ncbi.nlm.nih.gov/pubmed/33924324 http://dx.doi.org/10.3390/bios11040120 |
work_keys_str_mv | AT sunxiaoxiao usingcnnandhhttopredictbloodpressurelevelbasedonphotoplethysmographyanditsderivatives AT zhouliang usingcnnandhhttopredictbloodpressurelevelbasedonphotoplethysmographyanditsderivatives AT changshendong usingcnnandhhttopredictbloodpressurelevelbasedonphotoplethysmographyanditsderivatives AT liuzhaohui usingcnnandhhttopredictbloodpressurelevelbasedonphotoplethysmographyanditsderivatives |