Multi‐model fusion of classifiers for blood pressure estimation

Abstract Prehypertension is a new risky disease defined in the seventh report issued by the Joint National Commission. Hence, detecting prehypertension in time plays a very important role in protecting human lives. This study proposes a method for categorising blood pressure values into two classes,...

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Autores principales:	Qi Ye, Bingo Wing‐Kuen Ling, Nuo Xu, Yuxin Lin, Lingyue Hu
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/9108d0ff0f834487bd3a9cbfacc36edc
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spelling	oai:doaj.org-article:9108d0ff0f834487bd3a9cbfacc36edc2021-11-05T12:10:49ZMulti‐model fusion of classifiers for blood pressure estimation1751-88571751-884910.1049/syb2.12033https://doaj.org/article/9108d0ff0f834487bd3a9cbfacc36edc2021-12-01T00:00:00Zhttps://doi.org/10.1049/syb2.12033https://doaj.org/toc/1751-8849https://doaj.org/toc/1751-8857Abstract Prehypertension is a new risky disease defined in the seventh report issued by the Joint National Commission. Hence, detecting prehypertension in time plays a very important role in protecting human lives. This study proposes a method for categorising blood pressure values into two classes, namely the class of healthy blood pressure values and the class of prehypertension blood pressure values, as well as estimating the blood pressure values continuously only by employing photoplethysmograms. First, the denoising of photoplethysmograms is performed via a discrete cosine transform approach. Then, the features of the photoplethysmograms in both the time domain and the frequency domain are extracted. Next, the feature vectors are categorised into the two classes of blood pressure values by a multi‐model fusion of the classifiers. Here, the support vector machine, the random forest and the K‐nearest neighbour classifier are employed for performing the fusion. There are two types of blood pressure values. They are the systolic blood pressure values and the diastolic blood pressure values. For each class and each type of blood pressure values, support vector regression is used to estimate the blood pressure values. Since different classes and different types of blood pressure values are considered separately, the proposed method achieves an accurate estimation. The computed numerical simulation results show that the proposed method based on the multi‐model fusion of the classifiers achieves both higher classification accuracy and higher regression accuracy than the individual classification methods.Qi YeBingo Wing‐Kuen LingNuo XuYuxin LinLingyue HuWileyarticleBiology (General)QH301-705.5ENIET Systems Biology, Vol 15, Iss 6, Pp 184-191 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Qi Ye Bingo Wing‐Kuen Ling Nuo Xu Yuxin Lin Lingyue Hu Multi‐model fusion of classifiers for blood pressure estimation
description	Abstract Prehypertension is a new risky disease defined in the seventh report issued by the Joint National Commission. Hence, detecting prehypertension in time plays a very important role in protecting human lives. This study proposes a method for categorising blood pressure values into two classes, namely the class of healthy blood pressure values and the class of prehypertension blood pressure values, as well as estimating the blood pressure values continuously only by employing photoplethysmograms. First, the denoising of photoplethysmograms is performed via a discrete cosine transform approach. Then, the features of the photoplethysmograms in both the time domain and the frequency domain are extracted. Next, the feature vectors are categorised into the two classes of blood pressure values by a multi‐model fusion of the classifiers. Here, the support vector machine, the random forest and the K‐nearest neighbour classifier are employed for performing the fusion. There are two types of blood pressure values. They are the systolic blood pressure values and the diastolic blood pressure values. For each class and each type of blood pressure values, support vector regression is used to estimate the blood pressure values. Since different classes and different types of blood pressure values are considered separately, the proposed method achieves an accurate estimation. The computed numerical simulation results show that the proposed method based on the multi‐model fusion of the classifiers achieves both higher classification accuracy and higher regression accuracy than the individual classification methods.
format	article
author	Qi Ye Bingo Wing‐Kuen Ling Nuo Xu Yuxin Lin Lingyue Hu
author_facet	Qi Ye Bingo Wing‐Kuen Ling Nuo Xu Yuxin Lin Lingyue Hu
author_sort	Qi Ye
title	Multi‐model fusion of classifiers for blood pressure estimation
title_short	Multi‐model fusion of classifiers for blood pressure estimation
title_full	Multi‐model fusion of classifiers for blood pressure estimation
title_fullStr	Multi‐model fusion of classifiers for blood pressure estimation
title_full_unstemmed	Multi‐model fusion of classifiers for blood pressure estimation
title_sort	multi‐model fusion of classifiers for blood pressure estimation
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/9108d0ff0f834487bd3a9cbfacc36edc
work_keys_str_mv	AT qiye multimodelfusionofclassifiersforbloodpressureestimation AT bingowingkuenling multimodelfusionofclassifiersforbloodpressureestimation AT nuoxu multimodelfusionofclassifiersforbloodpressureestimation AT yuxinlin multimodelfusionofclassifiersforbloodpressureestimation AT lingyuehu multimodelfusionofclassifiersforbloodpressureestimation
_version_	1718444303191113728

Multi‐model fusion of classifiers for blood pressure estimation

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