Efficient ECG Compression and QRS Detection for E-Health Applications

Abstract Current medical screening and diagnostic procedures have shifted toward recording longer electrocardiogram (ECG) signals, which have traditionally been processed on personal computers (PCs) with high-speed multi-core processors and efficient memory processing. Battery-driven devices are now...

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Autores principales:	Mohamed Elgendi, Amr Mohamed, Rabab Ward
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/9e2a150e1f704e3da7331c72b5dc89fa
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spelling	oai:doaj.org-article:9e2a150e1f704e3da7331c72b5dc89fa2021-12-02T15:04:52ZEfficient ECG Compression and QRS Detection for E-Health Applications10.1038/s41598-017-00540-x2045-2322https://doaj.org/article/9e2a150e1f704e3da7331c72b5dc89fa2017-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-00540-xhttps://doaj.org/toc/2045-2322Abstract Current medical screening and diagnostic procedures have shifted toward recording longer electrocardiogram (ECG) signals, which have traditionally been processed on personal computers (PCs) with high-speed multi-core processors and efficient memory processing. Battery-driven devices are now more commonly used for the same purpose and thus exploring highly efficient, low-power alternatives for local ECG signal collection and processing is essential for efficient and convenient clinical use. Several ECG compression methods have been reported in the current literature with limited discussion on the performance of the compressed and the reconstructed ECG signals in terms of the QRS complex detection accuracy. This paper proposes and evaluates different compression methods based not only on the compression ratio (CR) and percentage root-mean-square difference (PRD), but also based on the accuracy of QRS detection. In this paper, we have developed a lossy method (Methods III) and compared them to the most current lossless and lossy ECG compression methods (Method I and Method II, respectively). The proposed lossy compression method (Method III) achieves CR of 4.5×, PRD of 0.53, as well as an overall sensitivity of 99.78% and positive predictivity of 99.92% are achieved (when coupled with an existing QRS detection algorithm) on the MIT-BIH Arrhythmia database and an overall sensitivity of 99.90% and positive predictivity of 99.84% on the QT database.Mohamed ElgendiAmr MohamedRabab WardNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-16 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Mohamed Elgendi Amr Mohamed Rabab Ward Efficient ECG Compression and QRS Detection for E-Health Applications
description	Abstract Current medical screening and diagnostic procedures have shifted toward recording longer electrocardiogram (ECG) signals, which have traditionally been processed on personal computers (PCs) with high-speed multi-core processors and efficient memory processing. Battery-driven devices are now more commonly used for the same purpose and thus exploring highly efficient, low-power alternatives for local ECG signal collection and processing is essential for efficient and convenient clinical use. Several ECG compression methods have been reported in the current literature with limited discussion on the performance of the compressed and the reconstructed ECG signals in terms of the QRS complex detection accuracy. This paper proposes and evaluates different compression methods based not only on the compression ratio (CR) and percentage root-mean-square difference (PRD), but also based on the accuracy of QRS detection. In this paper, we have developed a lossy method (Methods III) and compared them to the most current lossless and lossy ECG compression methods (Method I and Method II, respectively). The proposed lossy compression method (Method III) achieves CR of 4.5×, PRD of 0.53, as well as an overall sensitivity of 99.78% and positive predictivity of 99.92% are achieved (when coupled with an existing QRS detection algorithm) on the MIT-BIH Arrhythmia database and an overall sensitivity of 99.90% and positive predictivity of 99.84% on the QT database.
format	article
author	Mohamed Elgendi Amr Mohamed Rabab Ward
author_facet	Mohamed Elgendi Amr Mohamed Rabab Ward
author_sort	Mohamed Elgendi
title	Efficient ECG Compression and QRS Detection for E-Health Applications
title_short	Efficient ECG Compression and QRS Detection for E-Health Applications
title_full	Efficient ECG Compression and QRS Detection for E-Health Applications
title_fullStr	Efficient ECG Compression and QRS Detection for E-Health Applications
title_full_unstemmed	Efficient ECG Compression and QRS Detection for E-Health Applications
title_sort	efficient ecg compression and qrs detection for e-health applications
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/9e2a150e1f704e3da7331c72b5dc89fa
work_keys_str_mv	AT mohamedelgendi efficientecgcompressionandqrsdetectionforehealthapplications AT amrmohamed efficientecgcompressionandqrsdetectionforehealthapplications AT rababward efficientecgcompressionandqrsdetectionforehealthapplications
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Efficient ECG Compression and QRS Detection for E-Health Applications

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