Deep learning-based robust automatic non-invasive measurement of blood pressure using Korotkoff sounds

Abstract This paper proposes a method that automatically measures non-invasive blood pressure (BP) based on an auscultatory approach using Korotkoff sounds (K-sounds). There have been methods utilizing K-sounds that were more accurate in general than those using cuff pressure signals only under well...

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Autores principales:	Ji-Ho Chang, Il Doh
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/4002f671429b4a669355bf525ed8a833
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spelling	oai:doaj.org-article:4002f671429b4a669355bf525ed8a8332021-12-05T12:14:16ZDeep learning-based robust automatic non-invasive measurement of blood pressure using Korotkoff sounds10.1038/s41598-021-02513-72045-2322https://doaj.org/article/4002f671429b4a669355bf525ed8a8332021-12-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-02513-7https://doaj.org/toc/2045-2322Abstract This paper proposes a method that automatically measures non-invasive blood pressure (BP) based on an auscultatory approach using Korotkoff sounds (K-sounds). There have been methods utilizing K-sounds that were more accurate in general than those using cuff pressure signals only under well-controlled environments, but most were vulnerable to the measurement conditions and to external noise because blood pressure is simply determined based on threshold values in the sound signal. The proposed method enables robust and precise BP measurements by evaluating the probability that each sound pulse is an audible K-sound based on a deep learning using a convolutional neural network (CNN). Instead of classifying sound pulses into two categories, audible K-sounds and others, the proposed CNN model outputs probability values. These values in a Korotkoff cycle are arranged in time order, and the blood pressure is determined. The proposed method was tested with a dataset acquired in practice that occasionally contains considerable noise, which can degrade the performance of the threshold-based methods. The results demonstrate that the proposed method outperforms a previously reported CNN-based classification method using K-sounds. With larger amounts of various types of data, the proposed method can potentially achieve more precise and robust results.Ji-Ho ChangIl DohNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ji-Ho Chang Il Doh Deep learning-based robust automatic non-invasive measurement of blood pressure using Korotkoff sounds
description	Abstract This paper proposes a method that automatically measures non-invasive blood pressure (BP) based on an auscultatory approach using Korotkoff sounds (K-sounds). There have been methods utilizing K-sounds that were more accurate in general than those using cuff pressure signals only under well-controlled environments, but most were vulnerable to the measurement conditions and to external noise because blood pressure is simply determined based on threshold values in the sound signal. The proposed method enables robust and precise BP measurements by evaluating the probability that each sound pulse is an audible K-sound based on a deep learning using a convolutional neural network (CNN). Instead of classifying sound pulses into two categories, audible K-sounds and others, the proposed CNN model outputs probability values. These values in a Korotkoff cycle are arranged in time order, and the blood pressure is determined. The proposed method was tested with a dataset acquired in practice that occasionally contains considerable noise, which can degrade the performance of the threshold-based methods. The results demonstrate that the proposed method outperforms a previously reported CNN-based classification method using K-sounds. With larger amounts of various types of data, the proposed method can potentially achieve more precise and robust results.
format	article
author	Ji-Ho Chang Il Doh
author_facet	Ji-Ho Chang Il Doh
author_sort	Ji-Ho Chang
title	Deep learning-based robust automatic non-invasive measurement of blood pressure using Korotkoff sounds
title_short	Deep learning-based robust automatic non-invasive measurement of blood pressure using Korotkoff sounds
title_full	Deep learning-based robust automatic non-invasive measurement of blood pressure using Korotkoff sounds
title_fullStr	Deep learning-based robust automatic non-invasive measurement of blood pressure using Korotkoff sounds
title_full_unstemmed	Deep learning-based robust automatic non-invasive measurement of blood pressure using Korotkoff sounds
title_sort	deep learning-based robust automatic non-invasive measurement of blood pressure using korotkoff sounds
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/4002f671429b4a669355bf525ed8a833
work_keys_str_mv	AT jihochang deeplearningbasedrobustautomaticnoninvasivemeasurementofbloodpressureusingkorotkoffsounds AT ildoh deeplearningbasedrobustautomaticnoninvasivemeasurementofbloodpressureusingkorotkoffsounds
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Deep learning-based robust automatic non-invasive measurement of blood pressure using Korotkoff sounds

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