Improving Remote Health Monitoring: A Low-Complexity ECG Compression Approach

Recent advances in mobile technology have created a shift towards using battery-driven devices in remote monitoring settings and smart homes. Clinicians are carrying out diagnostic and screening procedures based on the electrocardiogram (ECG) signals collected remotely for outpatients who need conti...
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Autor*in:	Mohamed Elgendi [verfasserIn] Abdulla Al-Ali [verfasserIn] Amr Mohamed [verfasserIn] Rabab Ward [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	wearable sensors telemedicine digital medicine smart healthcare wireless systems remote healthcare mobile health e-Health

Übergeordnetes Werk:	In: Diagnostics - MDPI AG, 2012, 8(2018), 1, p 10
Übergeordnetes Werk:	volume:8 ; year:2018 ; number:1, p 10

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/diagnostics8010010

Katalog-ID:	DOAJ025859722

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allfields	10.3390/diagnostics8010010 doi (DE-627)DOAJ025859722 (DE-599)DOAJaa3a31a8578b4e999cb61f78f06126f9 DE-627 ger DE-627 rakwb eng R5-920 Mohamed Elgendi verfasserin aut Improving Remote Health Monitoring: A Low-Complexity ECG Compression Approach 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Recent advances in mobile technology have created a shift towards using battery-driven devices in remote monitoring settings and smart homes. Clinicians are carrying out diagnostic and screening procedures based on the electrocardiogram (ECG) signals collected remotely for outpatients who need continuous monitoring. High-speed transmission and analysis of large recorded ECG signals are essential, especially with the increased use of battery-powered devices. Exploring low-power alternative compression methodologies that have high efficiency and that enable ECG signal collection, transmission, and analysis in a smart home or remote location is required. Compression algorithms based on adaptive linear predictors and decimation by a factor B / K are evaluated based on compression ratio (CR), percentage root-mean-square difference (PRD), and heartbeat detection accuracy of the reconstructed ECG signal. With two databases (153 subjects), the new algorithm demonstrates the highest compression performance ( CR = 6 and PRD = 1.88 ) and overall detection accuracy (99.90% sensitivity, 99.56% positive predictivity) over both databases. The proposed algorithm presents an advantage for the real-time transmission of ECG signals using a faster and more efficient method, which meets the growing demand for more efficient remote health monitoring. wearable sensors telemedicine digital medicine smart healthcare wireless systems remote healthcare mobile health e-Health Medicine (General) Abdulla Al-Ali verfasserin aut Amr Mohamed verfasserin aut Rabab Ward verfasserin aut In Diagnostics MDPI AG, 2012 8(2018), 1, p 10 (DE-627)718627814 (DE-600)2662336-5 20754418 nnns volume:8 year:2018 number:1, p 10 https://doi.org/10.3390/diagnostics8010010 kostenfrei https://doaj.org/article/aa3a31a8578b4e999cb61f78f06126f9 kostenfrei http://www.mdpi.com/2075-4418/8/1/10 kostenfrei https://doaj.org/toc/2075-4418 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2018 1, p 10
spelling	10.3390/diagnostics8010010 doi (DE-627)DOAJ025859722 (DE-599)DOAJaa3a31a8578b4e999cb61f78f06126f9 DE-627 ger DE-627 rakwb eng R5-920 Mohamed Elgendi verfasserin aut Improving Remote Health Monitoring: A Low-Complexity ECG Compression Approach 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Recent advances in mobile technology have created a shift towards using battery-driven devices in remote monitoring settings and smart homes. Clinicians are carrying out diagnostic and screening procedures based on the electrocardiogram (ECG) signals collected remotely for outpatients who need continuous monitoring. High-speed transmission and analysis of large recorded ECG signals are essential, especially with the increased use of battery-powered devices. Exploring low-power alternative compression methodologies that have high efficiency and that enable ECG signal collection, transmission, and analysis in a smart home or remote location is required. Compression algorithms based on adaptive linear predictors and decimation by a factor B / K are evaluated based on compression ratio (CR), percentage root-mean-square difference (PRD), and heartbeat detection accuracy of the reconstructed ECG signal. With two databases (153 subjects), the new algorithm demonstrates the highest compression performance ( CR = 6 and PRD = 1.88 ) and overall detection accuracy (99.90% sensitivity, 99.56% positive predictivity) over both databases. The proposed algorithm presents an advantage for the real-time transmission of ECG signals using a faster and more efficient method, which meets the growing demand for more efficient remote health monitoring. wearable sensors telemedicine digital medicine smart healthcare wireless systems remote healthcare mobile health e-Health Medicine (General) Abdulla Al-Ali verfasserin aut Amr Mohamed verfasserin aut Rabab Ward verfasserin aut In Diagnostics MDPI AG, 2012 8(2018), 1, p 10 (DE-627)718627814 (DE-600)2662336-5 20754418 nnns volume:8 year:2018 number:1, p 10 https://doi.org/10.3390/diagnostics8010010 kostenfrei https://doaj.org/article/aa3a31a8578b4e999cb61f78f06126f9 kostenfrei http://www.mdpi.com/2075-4418/8/1/10 kostenfrei https://doaj.org/toc/2075-4418 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2018 1, p 10
allfields_unstemmed	10.3390/diagnostics8010010 doi (DE-627)DOAJ025859722 (DE-599)DOAJaa3a31a8578b4e999cb61f78f06126f9 DE-627 ger DE-627 rakwb eng R5-920 Mohamed Elgendi verfasserin aut Improving Remote Health Monitoring: A Low-Complexity ECG Compression Approach 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Recent advances in mobile technology have created a shift towards using battery-driven devices in remote monitoring settings and smart homes. Clinicians are carrying out diagnostic and screening procedures based on the electrocardiogram (ECG) signals collected remotely for outpatients who need continuous monitoring. High-speed transmission and analysis of large recorded ECG signals are essential, especially with the increased use of battery-powered devices. Exploring low-power alternative compression methodologies that have high efficiency and that enable ECG signal collection, transmission, and analysis in a smart home or remote location is required. Compression algorithms based on adaptive linear predictors and decimation by a factor B / K are evaluated based on compression ratio (CR), percentage root-mean-square difference (PRD), and heartbeat detection accuracy of the reconstructed ECG signal. With two databases (153 subjects), the new algorithm demonstrates the highest compression performance ( CR = 6 and PRD = 1.88 ) and overall detection accuracy (99.90% sensitivity, 99.56% positive predictivity) over both databases. The proposed algorithm presents an advantage for the real-time transmission of ECG signals using a faster and more efficient method, which meets the growing demand for more efficient remote health monitoring. wearable sensors telemedicine digital medicine smart healthcare wireless systems remote healthcare mobile health e-Health Medicine (General) Abdulla Al-Ali verfasserin aut Amr Mohamed verfasserin aut Rabab Ward verfasserin aut In Diagnostics MDPI AG, 2012 8(2018), 1, p 10 (DE-627)718627814 (DE-600)2662336-5 20754418 nnns volume:8 year:2018 number:1, p 10 https://doi.org/10.3390/diagnostics8010010 kostenfrei https://doaj.org/article/aa3a31a8578b4e999cb61f78f06126f9 kostenfrei http://www.mdpi.com/2075-4418/8/1/10 kostenfrei https://doaj.org/toc/2075-4418 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2018 1, p 10
allfieldsGer	10.3390/diagnostics8010010 doi (DE-627)DOAJ025859722 (DE-599)DOAJaa3a31a8578b4e999cb61f78f06126f9 DE-627 ger DE-627 rakwb eng R5-920 Mohamed Elgendi verfasserin aut Improving Remote Health Monitoring: A Low-Complexity ECG Compression Approach 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Recent advances in mobile technology have created a shift towards using battery-driven devices in remote monitoring settings and smart homes. Clinicians are carrying out diagnostic and screening procedures based on the electrocardiogram (ECG) signals collected remotely for outpatients who need continuous monitoring. High-speed transmission and analysis of large recorded ECG signals are essential, especially with the increased use of battery-powered devices. Exploring low-power alternative compression methodologies that have high efficiency and that enable ECG signal collection, transmission, and analysis in a smart home or remote location is required. Compression algorithms based on adaptive linear predictors and decimation by a factor B / K are evaluated based on compression ratio (CR), percentage root-mean-square difference (PRD), and heartbeat detection accuracy of the reconstructed ECG signal. With two databases (153 subjects), the new algorithm demonstrates the highest compression performance ( CR = 6 and PRD = 1.88 ) and overall detection accuracy (99.90% sensitivity, 99.56% positive predictivity) over both databases. The proposed algorithm presents an advantage for the real-time transmission of ECG signals using a faster and more efficient method, which meets the growing demand for more efficient remote health monitoring. wearable sensors telemedicine digital medicine smart healthcare wireless systems remote healthcare mobile health e-Health Medicine (General) Abdulla Al-Ali verfasserin aut Amr Mohamed verfasserin aut Rabab Ward verfasserin aut In Diagnostics MDPI AG, 2012 8(2018), 1, p 10 (DE-627)718627814 (DE-600)2662336-5 20754418 nnns volume:8 year:2018 number:1, p 10 https://doi.org/10.3390/diagnostics8010010 kostenfrei https://doaj.org/article/aa3a31a8578b4e999cb61f78f06126f9 kostenfrei http://www.mdpi.com/2075-4418/8/1/10 kostenfrei https://doaj.org/toc/2075-4418 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2018 1, p 10
allfieldsSound	10.3390/diagnostics8010010 doi (DE-627)DOAJ025859722 (DE-599)DOAJaa3a31a8578b4e999cb61f78f06126f9 DE-627 ger DE-627 rakwb eng R5-920 Mohamed Elgendi verfasserin aut Improving Remote Health Monitoring: A Low-Complexity ECG Compression Approach 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Recent advances in mobile technology have created a shift towards using battery-driven devices in remote monitoring settings and smart homes. Clinicians are carrying out diagnostic and screening procedures based on the electrocardiogram (ECG) signals collected remotely for outpatients who need continuous monitoring. High-speed transmission and analysis of large recorded ECG signals are essential, especially with the increased use of battery-powered devices. Exploring low-power alternative compression methodologies that have high efficiency and that enable ECG signal collection, transmission, and analysis in a smart home or remote location is required. Compression algorithms based on adaptive linear predictors and decimation by a factor B / K are evaluated based on compression ratio (CR), percentage root-mean-square difference (PRD), and heartbeat detection accuracy of the reconstructed ECG signal. With two databases (153 subjects), the new algorithm demonstrates the highest compression performance ( CR = 6 and PRD = 1.88 ) and overall detection accuracy (99.90% sensitivity, 99.56% positive predictivity) over both databases. The proposed algorithm presents an advantage for the real-time transmission of ECG signals using a faster and more efficient method, which meets the growing demand for more efficient remote health monitoring. wearable sensors telemedicine digital medicine smart healthcare wireless systems remote healthcare mobile health e-Health Medicine (General) Abdulla Al-Ali verfasserin aut Amr Mohamed verfasserin aut Rabab Ward verfasserin aut In Diagnostics MDPI AG, 2012 8(2018), 1, p 10 (DE-627)718627814 (DE-600)2662336-5 20754418 nnns volume:8 year:2018 number:1, p 10 https://doi.org/10.3390/diagnostics8010010 kostenfrei https://doaj.org/article/aa3a31a8578b4e999cb61f78f06126f9 kostenfrei http://www.mdpi.com/2075-4418/8/1/10 kostenfrei https://doaj.org/toc/2075-4418 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2018 1, p 10
language	English
source	In Diagnostics 8(2018), 1, p 10 volume:8 year:2018 number:1, p 10
sourceStr	In Diagnostics 8(2018), 1, p 10 volume:8 year:2018 number:1, p 10
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	wearable sensors telemedicine digital medicine smart healthcare wireless systems remote healthcare mobile health e-Health Medicine (General)
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container_title	Diagnostics
authorswithroles_txt_mv	Mohamed Elgendi @@aut@@ Abdulla Al-Ali @@aut@@ Amr Mohamed @@aut@@ Rabab Ward @@aut@@
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callnumber-first	R - Medicine
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spellingShingle	Mohamed Elgendi misc R5-920 misc wearable sensors misc telemedicine misc digital medicine misc smart healthcare misc wireless systems misc remote healthcare misc mobile health misc e-Health misc Medicine (General) Improving Remote Health Monitoring: A Low-Complexity ECG Compression Approach
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topic	misc R5-920 misc wearable sensors misc telemedicine misc digital medicine misc smart healthcare misc wireless systems misc remote healthcare misc mobile health misc e-Health misc Medicine (General)
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title	Improving Remote Health Monitoring: A Low-Complexity ECG Compression Approach
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title_sort	improving remote health monitoring: a low-complexity ecg compression approach
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title_auth	Improving Remote Health Monitoring: A Low-Complexity ECG Compression Approach
abstract	Recent advances in mobile technology have created a shift towards using battery-driven devices in remote monitoring settings and smart homes. Clinicians are carrying out diagnostic and screening procedures based on the electrocardiogram (ECG) signals collected remotely for outpatients who need continuous monitoring. High-speed transmission and analysis of large recorded ECG signals are essential, especially with the increased use of battery-powered devices. Exploring low-power alternative compression methodologies that have high efficiency and that enable ECG signal collection, transmission, and analysis in a smart home or remote location is required. Compression algorithms based on adaptive linear predictors and decimation by a factor B / K are evaluated based on compression ratio (CR), percentage root-mean-square difference (PRD), and heartbeat detection accuracy of the reconstructed ECG signal. With two databases (153 subjects), the new algorithm demonstrates the highest compression performance ( CR = 6 and PRD = 1.88 ) and overall detection accuracy (99.90% sensitivity, 99.56% positive predictivity) over both databases. The proposed algorithm presents an advantage for the real-time transmission of ECG signals using a faster and more efficient method, which meets the growing demand for more efficient remote health monitoring.
abstractGer	Recent advances in mobile technology have created a shift towards using battery-driven devices in remote monitoring settings and smart homes. Clinicians are carrying out diagnostic and screening procedures based on the electrocardiogram (ECG) signals collected remotely for outpatients who need continuous monitoring. High-speed transmission and analysis of large recorded ECG signals are essential, especially with the increased use of battery-powered devices. Exploring low-power alternative compression methodologies that have high efficiency and that enable ECG signal collection, transmission, and analysis in a smart home or remote location is required. Compression algorithms based on adaptive linear predictors and decimation by a factor B / K are evaluated based on compression ratio (CR), percentage root-mean-square difference (PRD), and heartbeat detection accuracy of the reconstructed ECG signal. With two databases (153 subjects), the new algorithm demonstrates the highest compression performance ( CR = 6 and PRD = 1.88 ) and overall detection accuracy (99.90% sensitivity, 99.56% positive predictivity) over both databases. The proposed algorithm presents an advantage for the real-time transmission of ECG signals using a faster and more efficient method, which meets the growing demand for more efficient remote health monitoring.
abstract_unstemmed	Recent advances in mobile technology have created a shift towards using battery-driven devices in remote monitoring settings and smart homes. Clinicians are carrying out diagnostic and screening procedures based on the electrocardiogram (ECG) signals collected remotely for outpatients who need continuous monitoring. High-speed transmission and analysis of large recorded ECG signals are essential, especially with the increased use of battery-powered devices. Exploring low-power alternative compression methodologies that have high efficiency and that enable ECG signal collection, transmission, and analysis in a smart home or remote location is required. Compression algorithms based on adaptive linear predictors and decimation by a factor B / K are evaluated based on compression ratio (CR), percentage root-mean-square difference (PRD), and heartbeat detection accuracy of the reconstructed ECG signal. With two databases (153 subjects), the new algorithm demonstrates the highest compression performance ( CR = 6 and PRD = 1.88 ) and overall detection accuracy (99.90% sensitivity, 99.56% positive predictivity) over both databases. The proposed algorithm presents an advantage for the real-time transmission of ECG signals using a faster and more efficient method, which meets the growing demand for more efficient remote health monitoring.
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