An ECG data sampling method for home-use IoT ECG monitor system optimization based on brick-up metaheuristic algorithm

With the rise in the popularity of Internet of Things (IoT) in-home health monitoring, the demand of data processing and analysis increases at the server. This is especially true for ECG data which has to be collected and analyzed continuously in real time. The data transmission and storage capacity...

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Autores principales:	Qun Song, Tengyue Li, Simon Fong, Feng Wu
Formato:	article
Lenguaje:	EN
Publicado:	AIMS Press 2021
Materias:	ecg data sampling in-home iot monitoring system brick-up metaheuristic optimization algorithm long short-term memory networks Biotechnology TP248.13-248.65 Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/c3c52bc657bc4eddbefc4a7f0f75a148
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id	oai:doaj.org-article:c3c52bc657bc4eddbefc4a7f0f75a148
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spelling	oai:doaj.org-article:c3c52bc657bc4eddbefc4a7f0f75a1482021-11-29T03:03:56ZAn ECG data sampling method for home-use IoT ECG monitor system optimization based on brick-up metaheuristic algorithm10.3934/mbe.20214471551-0018https://doaj.org/article/c3c52bc657bc4eddbefc4a7f0f75a1482021-10-01T00:00:00Zhttps://www.aimspress.com/article/doi/10.3934/mbe.2021447?viewType=HTMLhttps://doaj.org/toc/1551-0018With the rise in the popularity of Internet of Things (IoT) in-home health monitoring, the demand of data processing and analysis increases at the server. This is especially true for ECG data which has to be collected and analyzed continuously in real time. The data transmission and storage capacity of a simple home-use IoT system is often limited. In order to provide a responsive and reasonably high-resolution analysis over the data, the ECG recorder sampling rate must be tuned to an acceptable level such as 50Hz (compared to between 100Hz and 500Hz in lab), a huge amount of time series are to be gathered and dealt with. Therefore, a suitable sampling method that helps shorten the ECG data transformation time and uploading time is very important for cost saving.. In this paper, how to down sample the ECG data is investigated; instead of traditional data sampling methods, the use of a novel Brick-up Metaheuristic Optimization Algorithm (BMOA) that automatically optimizes the sampling of ECG data is proposed. By its adaptive design in choosing the most appropriate components, BMOA can build in real-time a best metaheuristic optimization algorithm for each device user assuming no two ECG data series are exactly identical. This dynamic pre-processing approach ensures each time the most optimal part of the ECG data series is harvested for health analysis from the raw data, in different scenarios from different users. In this study various application scenarios using real ECG datasets are simulated. The experimentation is tested with one of the most commonly used ECG classification methods, Long Short-Term Memory Network. The result shows the ECG data sampling by BMOA is indeed adaptive, the classification efficiency is improved, and the data storage requirement is reduced.Qun SongTengyue Li Simon FongFeng WuAIMS Pressarticleecg data samplingin-home iot monitoring systembrick-up metaheuristic optimization algorithmlong short-term memory networksBiotechnologyTP248.13-248.65MathematicsQA1-939ENMathematical Biosciences and Engineering, Vol 18, Iss 6, Pp 9076-9093 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	ecg data sampling in-home iot monitoring system brick-up metaheuristic optimization algorithm long short-term memory networks Biotechnology TP248.13-248.65 Mathematics QA1-939
spellingShingle	ecg data sampling in-home iot monitoring system brick-up metaheuristic optimization algorithm long short-term memory networks Biotechnology TP248.13-248.65 Mathematics QA1-939 Qun Song Tengyue Li Simon Fong Feng Wu An ECG data sampling method for home-use IoT ECG monitor system optimization based on brick-up metaheuristic algorithm
description	With the rise in the popularity of Internet of Things (IoT) in-home health monitoring, the demand of data processing and analysis increases at the server. This is especially true for ECG data which has to be collected and analyzed continuously in real time. The data transmission and storage capacity of a simple home-use IoT system is often limited. In order to provide a responsive and reasonably high-resolution analysis over the data, the ECG recorder sampling rate must be tuned to an acceptable level such as 50Hz (compared to between 100Hz and 500Hz in lab), a huge amount of time series are to be gathered and dealt with. Therefore, a suitable sampling method that helps shorten the ECG data transformation time and uploading time is very important for cost saving.. In this paper, how to down sample the ECG data is investigated; instead of traditional data sampling methods, the use of a novel Brick-up Metaheuristic Optimization Algorithm (BMOA) that automatically optimizes the sampling of ECG data is proposed. By its adaptive design in choosing the most appropriate components, BMOA can build in real-time a best metaheuristic optimization algorithm for each device user assuming no two ECG data series are exactly identical. This dynamic pre-processing approach ensures each time the most optimal part of the ECG data series is harvested for health analysis from the raw data, in different scenarios from different users. In this study various application scenarios using real ECG datasets are simulated. The experimentation is tested with one of the most commonly used ECG classification methods, Long Short-Term Memory Network. The result shows the ECG data sampling by BMOA is indeed adaptive, the classification efficiency is improved, and the data storage requirement is reduced.
format	article
author	Qun Song Tengyue Li Simon Fong Feng Wu
author_facet	Qun Song Tengyue Li Simon Fong Feng Wu
author_sort	Qun Song
title	An ECG data sampling method for home-use IoT ECG monitor system optimization based on brick-up metaheuristic algorithm
title_short	An ECG data sampling method for home-use IoT ECG monitor system optimization based on brick-up metaheuristic algorithm
title_full	An ECG data sampling method for home-use IoT ECG monitor system optimization based on brick-up metaheuristic algorithm
title_fullStr	An ECG data sampling method for home-use IoT ECG monitor system optimization based on brick-up metaheuristic algorithm
title_full_unstemmed	An ECG data sampling method for home-use IoT ECG monitor system optimization based on brick-up metaheuristic algorithm
title_sort	ecg data sampling method for home-use iot ecg monitor system optimization based on brick-up metaheuristic algorithm
publisher	AIMS Press
publishDate	2021
url	https://doaj.org/article/c3c52bc657bc4eddbefc4a7f0f75a148
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An ECG data sampling method for home-use IoT ECG monitor system optimization based on brick-up metaheuristic algorithm

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