Machine Learning and Electrocardiography Signal-Based Minimum Calculation Time Detection for Blood Pressure Detection.

Publisher: Hindawi Country of Publication: United States NLM ID: 101277751 Publication Model: eCollection Cited Medium: Internet ISSN: 1748-6718 (Electronic) Linking ISSN: 1748670X NLM ISO Abbreviation: Comput Math Methods Med Subsets: MEDLINE

Publication: 2011-2024 : New York : Hindawi
Original Publication: London : Taylor & Francis, c2006-

Artificial Intelligence* ; Blood Pressure Determination*/methods; Algorithms ; Blood Pressure/physiology ; Electrocardiography ; Humans ; Machine Learning

Objective: Measurement and monitoring of blood pressure are of great importance for preventing diseases such as cardiovascular and stroke caused by hypertension. Therefore, there is a need for advanced artificial intelligence-based systolic and diastolic blood pressure systems with a new technological infrastructure with a noninvasive process. The study is aimed at determining the minimum ECG time required for calculating systolic and diastolic blood pressure based on the Electrocardiography (ECG) signal. Methodology . The study includes ECG recordings of five individuals taken from the IEEE database, measured during daily activity. For the study, each signal was divided into epochs of 2-4-6-8-10-12-14-16-18-20 seconds. Twenty-five features were extracted from each epoched signal. The dimension of the dataset was reduced by using Spearman's feature selection algorithm. Analysis based on metrics was carried out by applying machine learning algorithms to the obtained dataset. Gaussian process regression exponential (GPR) machine learning algorithm was preferred because it is easy to integrate into embedded systems.
Results: The MAPE estimation performance values for diastolic and systolic blood pressure values for 16-second epochs were 2.44 mmHg and 1.92 mmHg, respectively.
Conclusion: According to the study results, it is evaluated that systolic and diastolic blood pressure values can be calculated with a high-performance ratio with 16-second ECG signals.
Competing Interests: The authors declare no conflict of interest.
(Copyright © 2022 Majid Nour et al.)

Clin Chim Acta. 2017 Feb;465:131-143. (PMID: 28007614)
J Clin Hypertens (Greenwich). 2018 Jul;20(7):1116-1121. (PMID: 30003694)
J Am Coll Cardiol. 2015 May 12;65(18):1998-2038. (PMID: 25840655)
Biomed Opt Express. 2016 Jul 12;7(8):3007-20. (PMID: 27570693)
Heart Fail Clin. 2019 Oct;15(4):531-541. (PMID: 31472888)
IEEE J Biomed Health Inform. 2021 Jul;25(7):2510-2520. (PMID: 33497346)
IEEE Trans Biomed Eng. 2007 Oct;54(10):1892-5. (PMID: 17926688)
Heart Fail Clin. 2020 Jan;16(1):99-106. (PMID: 31735319)
Artif Intell Med. 2019 Jun;97:143-151. (PMID: 30587391)
Can J Cardiol. 2018 May;34(5):506-525. (PMID: 29731013)
Curr Med Sci. 2021 Dec;41(6):1134-1150. (PMID: 34939144)
Pregnancy Hypertens. 2022 Mar;27:110-114. (PMID: 34998224)
IEEE Trans Inf Technol Biomed. 2012 Nov;16(6):1135-42. (PMID: 22203720)
IEEE Internet Things J. 2020 Nov 13;8(21):15652-15662. (PMID: 35582243)
J Hypertens. 1990 Jul;8(7):607-19. (PMID: 2168451)
Lancet. 2017 Dec 9;390(10112):2549-2558. (PMID: 29102084)
J Surg Res. 2022 Jul;275:181-193. (PMID: 35287027)
Sensors (Basel). 2018 Apr 11;18(4):. (PMID: 29641430)
Circ Res. 2019 Mar 29;124(7):990-1008. (PMID: 30920932)
Blood Press Monit. 2010 Feb;15(1):23-38. (PMID: 20110786)
J Am Coll Cardiol. 2017 Aug 1;70(5):572-586. (PMID: 28750701)
Cardiovasc Eng. 2009 Mar;9(1):32-8. (PMID: 19381806)
Phys Eng Sci Med. 2021 Mar;44(1):63-77. (PMID: 33398636)
Nat Biomed Eng. 2018 Oct;2(10):719-731. (PMID: 31015651)
J Am Coll Cardiol. 2017 Aug 1;70(5):587-589. (PMID: 28750702)
Annu Int Conf IEEE Eng Med Biol Soc. 2016 Aug;2016:3382-3385. (PMID: 28269029)
Nutr Metab Cardiovasc Dis. 2022 Jan;32(1):21-31. (PMID: 34690044)
J Hypertens. 2018 Dec;36(12):2284-2309. (PMID: 30379783)
Hypertension. 2018 Jun;71(6):1269-1324. (PMID: 29133354)
Comput Biol Med. 2020 May;120:103719. (PMID: 32421641)

Date Created: 20220729 Date Completed: 20220801 Latest Revision: 20240901

20250114

PMC9325348

10.1155/2022/5714454

35903432