Real-time smart monitoring system for atrial fibrillation pathology
Atrial Fibrillation (AF) is a common cardiac pathology and, due to its unpredictability, it sometimes remains not detected. Aim of this work is to present a new version of the already published eHealth system, that includes a new real-time Android application for AF detection and monitoring. The pro...
Gespeichert in:
| Veröffentlicht in: | Journal of ambient intelligence and humanized computing 2021-04, Vol.12 (4), p.4461-4469 |
|---|---|
| Hauptverfasser: | , , , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 4469 |
|---|---|
| container_issue | 4 |
| container_start_page | 4461 |
| container_title | Journal of ambient intelligence and humanized computing |
| container_volume | 12 |
| creator | Pierleoni, Paola Belli, Alberto Gentili, Andrea Incipini, Lorenzo Palma, Lorenzo Raggiunto, Sara Sbrollini, Agnese Burattini, Laura |
| description | Atrial Fibrillation (AF) is a common cardiac pathology and, due to its unpredictability, it sometimes remains not detected. Aim of this work is to present a new version of the already published eHealth system, that includes a new real-time Android application for AF detection and monitoring. The proposed eHealth system is composed of a commercial wearable sensor device (Bioharness 3.0 by Zephyr) for cardiac monitoring and a specially developed Android smartphone application. This application is able to real-time processing the raw data sensed from the wearable sensor, providing stress detection, calories consumption estimation, sinus arrhythmia detection, sinus rhythm classification, and apnea detection. As novelty, the new smartphone application also implemented a SVM-based algorithm designed to detect AF episodes by handling electrocardiogram and the heart-rate sequence of the subjects. The performance of the new SVM-based algorithm implemented in eHealth was tested on AF recordings and evaluated in term of sensitivity and specificity. The results show a sensitivity of 78% and a specificity of 66%, making this version of eHealth system suitable for real-time monitoring of AF events. |
| doi_str_mv | 10.1007/s12652-019-01602-w |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2919990005</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2919990005</sourcerecordid><originalsourceid>FETCH-LOGICAL-c319t-5d90f8da5c4b872ab2b106314157bdef396c0428f1f97afb702917c7baa9177c3</originalsourceid><addsrcrecordid>eNp9UE1LAzEUDKJgqf0DnhY8R_OS3c3mKMWPQkEQPYdkm9SU3c2apJT-e1NX9OaDx7zDzDxmELoGcguE8LsItK4oJiDy1oTiwxmaQVM3uIKyOv-9Gb9Eixh3JA8TDABmaPlqVIeT600RexVS0fvBJR_csC3iMSbTF9aHQqXgVFdYp4PrOpWcH4pRpQ_f-e3xCl1Y1UWz-ME5en98eFs-4_XL02p5v8YtA5FwtRHENhtVtaVuOFWaaiA1gxIqrjfGMlG3pKSNBSu4spoTKoC3XCuVkbdsjm4m3zH4z72JSe78Pgz5pcxMIUTOVWUWnVht8DEGY-UYXI52lEDkqS859SVzX_K7L3nIIjaJ4niKbsKf9T-qL2WqbiY</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2919990005</pqid></control><display><type>article</type><title>Real-time smart monitoring system for atrial fibrillation pathology</title><source>SpringerLink Journals</source><source>ProQuest Central</source><creator>Pierleoni, Paola ; Belli, Alberto ; Gentili, Andrea ; Incipini, Lorenzo ; Palma, Lorenzo ; Raggiunto, Sara ; Sbrollini, Agnese ; Burattini, Laura</creator><creatorcontrib>Pierleoni, Paola ; Belli, Alberto ; Gentili, Andrea ; Incipini, Lorenzo ; Palma, Lorenzo ; Raggiunto, Sara ; Sbrollini, Agnese ; Burattini, Laura</creatorcontrib><description>Atrial Fibrillation (AF) is a common cardiac pathology and, due to its unpredictability, it sometimes remains not detected. Aim of this work is to present a new version of the already published eHealth system, that includes a new real-time Android application for AF detection and monitoring. The proposed eHealth system is composed of a commercial wearable sensor device (Bioharness 3.0 by Zephyr) for cardiac monitoring and a specially developed Android smartphone application. This application is able to real-time processing the raw data sensed from the wearable sensor, providing stress detection, calories consumption estimation, sinus arrhythmia detection, sinus rhythm classification, and apnea detection. As novelty, the new smartphone application also implemented a SVM-based algorithm designed to detect AF episodes by handling electrocardiogram and the heart-rate sequence of the subjects. The performance of the new SVM-based algorithm implemented in eHealth was tested on AF recordings and evaluated in term of sensitivity and specificity. The results show a sensitivity of 78% and a specificity of 66%, making this version of eHealth system suitable for real-time monitoring of AF events.</description><identifier>ISSN: 1868-5137</identifier><identifier>EISSN: 1868-5145</identifier><identifier>DOI: 10.1007/s12652-019-01602-w</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Algorithms ; Artificial Intelligence ; Cardiac arrhythmia ; Classification ; Computational Intelligence ; Electrocardiography ; Engineering ; Fibrillation ; Heart ; Heart rate ; Monitoring ; Monitoring systems ; Original Research ; Pathology ; Patients ; Real time ; Robotics and Automation ; Sensors ; Signal processing ; Sinuses ; Smartphones ; Software ; User Interfaces and Human Computer Interaction ; Wearable technology</subject><ispartof>Journal of ambient intelligence and humanized computing, 2021-04, Vol.12 (4), p.4461-4469</ispartof><rights>Springer-Verlag GmbH Germany, part of Springer Nature 2019</rights><rights>Springer-Verlag GmbH Germany, part of Springer Nature 2019.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c319t-5d90f8da5c4b872ab2b106314157bdef396c0428f1f97afb702917c7baa9177c3</citedby><cites>FETCH-LOGICAL-c319t-5d90f8da5c4b872ab2b106314157bdef396c0428f1f97afb702917c7baa9177c3</cites><orcidid>0000-0002-9692-582X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s12652-019-01602-w$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2919990005?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,776,780,21367,27901,27902,33721,41464,42533,43781,51294</link.rule.ids></links><search><creatorcontrib>Pierleoni, Paola</creatorcontrib><creatorcontrib>Belli, Alberto</creatorcontrib><creatorcontrib>Gentili, Andrea</creatorcontrib><creatorcontrib>Incipini, Lorenzo</creatorcontrib><creatorcontrib>Palma, Lorenzo</creatorcontrib><creatorcontrib>Raggiunto, Sara</creatorcontrib><creatorcontrib>Sbrollini, Agnese</creatorcontrib><creatorcontrib>Burattini, Laura</creatorcontrib><title>Real-time smart monitoring system for atrial fibrillation pathology</title><title>Journal of ambient intelligence and humanized computing</title><addtitle>J Ambient Intell Human Comput</addtitle><description>Atrial Fibrillation (AF) is a common cardiac pathology and, due to its unpredictability, it sometimes remains not detected. Aim of this work is to present a new version of the already published eHealth system, that includes a new real-time Android application for AF detection and monitoring. The proposed eHealth system is composed of a commercial wearable sensor device (Bioharness 3.0 by Zephyr) for cardiac monitoring and a specially developed Android smartphone application. This application is able to real-time processing the raw data sensed from the wearable sensor, providing stress detection, calories consumption estimation, sinus arrhythmia detection, sinus rhythm classification, and apnea detection. As novelty, the new smartphone application also implemented a SVM-based algorithm designed to detect AF episodes by handling electrocardiogram and the heart-rate sequence of the subjects. The performance of the new SVM-based algorithm implemented in eHealth was tested on AF recordings and evaluated in term of sensitivity and specificity. The results show a sensitivity of 78% and a specificity of 66%, making this version of eHealth system suitable for real-time monitoring of AF events.</description><subject>Algorithms</subject><subject>Artificial Intelligence</subject><subject>Cardiac arrhythmia</subject><subject>Classification</subject><subject>Computational Intelligence</subject><subject>Electrocardiography</subject><subject>Engineering</subject><subject>Fibrillation</subject><subject>Heart</subject><subject>Heart rate</subject><subject>Monitoring</subject><subject>Monitoring systems</subject><subject>Original Research</subject><subject>Pathology</subject><subject>Patients</subject><subject>Real time</subject><subject>Robotics and Automation</subject><subject>Sensors</subject><subject>Signal processing</subject><subject>Sinuses</subject><subject>Smartphones</subject><subject>Software</subject><subject>User Interfaces and Human Computer Interaction</subject><subject>Wearable technology</subject><issn>1868-5137</issn><issn>1868-5145</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNp9UE1LAzEUDKJgqf0DnhY8R_OS3c3mKMWPQkEQPYdkm9SU3c2apJT-e1NX9OaDx7zDzDxmELoGcguE8LsItK4oJiDy1oTiwxmaQVM3uIKyOv-9Gb9Eixh3JA8TDABmaPlqVIeT600RexVS0fvBJR_csC3iMSbTF9aHQqXgVFdYp4PrOpWcH4pRpQ_f-e3xCl1Y1UWz-ME5en98eFs-4_XL02p5v8YtA5FwtRHENhtVtaVuOFWaaiA1gxIqrjfGMlG3pKSNBSu4spoTKoC3XCuVkbdsjm4m3zH4z72JSe78Pgz5pcxMIUTOVWUWnVht8DEGY-UYXI52lEDkqS859SVzX_K7L3nIIjaJ4niKbsKf9T-qL2WqbiY</recordid><startdate>20210401</startdate><enddate>20210401</enddate><creator>Pierleoni, Paola</creator><creator>Belli, Alberto</creator><creator>Gentili, Andrea</creator><creator>Incipini, Lorenzo</creator><creator>Palma, Lorenzo</creator><creator>Raggiunto, Sara</creator><creator>Sbrollini, Agnese</creator><creator>Burattini, Laura</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>P5Z</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><orcidid>https://orcid.org/0000-0002-9692-582X</orcidid></search><sort><creationdate>20210401</creationdate><title>Real-time smart monitoring system for atrial fibrillation pathology</title><author>Pierleoni, Paola ; Belli, Alberto ; Gentili, Andrea ; Incipini, Lorenzo ; Palma, Lorenzo ; Raggiunto, Sara ; Sbrollini, Agnese ; Burattini, Laura</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c319t-5d90f8da5c4b872ab2b106314157bdef396c0428f1f97afb702917c7baa9177c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Algorithms</topic><topic>Artificial Intelligence</topic><topic>Cardiac arrhythmia</topic><topic>Classification</topic><topic>Computational Intelligence</topic><topic>Electrocardiography</topic><topic>Engineering</topic><topic>Fibrillation</topic><topic>Heart</topic><topic>Heart rate</topic><topic>Monitoring</topic><topic>Monitoring systems</topic><topic>Original Research</topic><topic>Pathology</topic><topic>Patients</topic><topic>Real time</topic><topic>Robotics and Automation</topic><topic>Sensors</topic><topic>Signal processing</topic><topic>Sinuses</topic><topic>Smartphones</topic><topic>Software</topic><topic>User Interfaces and Human Computer Interaction</topic><topic>Wearable technology</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Pierleoni, Paola</creatorcontrib><creatorcontrib>Belli, Alberto</creatorcontrib><creatorcontrib>Gentili, Andrea</creatorcontrib><creatorcontrib>Incipini, Lorenzo</creatorcontrib><creatorcontrib>Palma, Lorenzo</creatorcontrib><creatorcontrib>Raggiunto, Sara</creatorcontrib><creatorcontrib>Sbrollini, Agnese</creatorcontrib><creatorcontrib>Burattini, Laura</creatorcontrib><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection (ProQuest)</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><jtitle>Journal of ambient intelligence and humanized computing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Pierleoni, Paola</au><au>Belli, Alberto</au><au>Gentili, Andrea</au><au>Incipini, Lorenzo</au><au>Palma, Lorenzo</au><au>Raggiunto, Sara</au><au>Sbrollini, Agnese</au><au>Burattini, Laura</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Real-time smart monitoring system for atrial fibrillation pathology</atitle><jtitle>Journal of ambient intelligence and humanized computing</jtitle><stitle>J Ambient Intell Human Comput</stitle><date>2021-04-01</date><risdate>2021</risdate><volume>12</volume><issue>4</issue><spage>4461</spage><epage>4469</epage><pages>4461-4469</pages><issn>1868-5137</issn><eissn>1868-5145</eissn><abstract>Atrial Fibrillation (AF) is a common cardiac pathology and, due to its unpredictability, it sometimes remains not detected. Aim of this work is to present a new version of the already published eHealth system, that includes a new real-time Android application for AF detection and monitoring. The proposed eHealth system is composed of a commercial wearable sensor device (Bioharness 3.0 by Zephyr) for cardiac monitoring and a specially developed Android smartphone application. This application is able to real-time processing the raw data sensed from the wearable sensor, providing stress detection, calories consumption estimation, sinus arrhythmia detection, sinus rhythm classification, and apnea detection. As novelty, the new smartphone application also implemented a SVM-based algorithm designed to detect AF episodes by handling electrocardiogram and the heart-rate sequence of the subjects. The performance of the new SVM-based algorithm implemented in eHealth was tested on AF recordings and evaluated in term of sensitivity and specificity. The results show a sensitivity of 78% and a specificity of 66%, making this version of eHealth system suitable for real-time monitoring of AF events.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/s12652-019-01602-w</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0002-9692-582X</orcidid></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1868-5137 |
| ispartof | Journal of ambient intelligence and humanized computing, 2021-04, Vol.12 (4), p.4461-4469 |
| issn | 1868-5137 1868-5145 |
| language | eng |
| recordid | cdi_proquest_journals_2919990005 |
| source | SpringerLink Journals; ProQuest Central |
| subjects | Algorithms Artificial Intelligence Cardiac arrhythmia Classification Computational Intelligence Electrocardiography Engineering Fibrillation Heart Heart rate Monitoring Monitoring systems Original Research Pathology Patients Real time Robotics and Automation Sensors Signal processing Sinuses Smartphones Software User Interfaces and Human Computer Interaction Wearable technology |
| title | Real-time smart monitoring system for atrial fibrillation pathology |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-08T03%3A52%3A40IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Real-time%20smart%20monitoring%20system%20for%20atrial%20fibrillation%20pathology&rft.jtitle=Journal%20of%20ambient%20intelligence%20and%20humanized%20computing&rft.au=Pierleoni,%20Paola&rft.date=2021-04-01&rft.volume=12&rft.issue=4&rft.spage=4461&rft.epage=4469&rft.pages=4461-4469&rft.issn=1868-5137&rft.eissn=1868-5145&rft_id=info:doi/10.1007/s12652-019-01602-w&rft_dat=%3Cproquest_cross%3E2919990005%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2919990005&rft_id=info:pmid/&rfr_iscdi=true |