Biosignal Time-Series Analysis
| dc.contributor.author | Kiranyaz S. | |
| dc.contributor.author | İnce, Türker | |
| dc.contributor.author | Chowdhury M.E.H. | |
| dc.contributor.author | Degerli A. | |
| dc.contributor.author | Gabbouj, Moncef | |
| dc.date.accessioned | 2023-06-16T14:59:23Z | |
| dc.date.available | 2023-06-16T14:59:23Z | |
| dc.date.issued | 2022 | |
| dc.description.abstract | In this chapter, recent state-of-the-art techniques in biosignal time-series analysis will be presented. We shall start with the problem of patient-specific ECG beat classification where the objective is to discriminate the arrhythmic beats from the normal (healthy) beats of an individual patient. So, we will answer the ultimate question of how to design person-specific, real-time, and accurate monitoring of ECG signals. We shall then move on to the recent solution of a related problem, an early warning system that can alert an individual the instant his/her heart deviates from its normal rhythm. This is a far challenging problem since the detection of the arrhythmia beats should be performed without knowing them. © 2022 Elsevier Inc. All rights reserved. | en_US |
| dc.identifier.doi | 10.1016/B978-0-32-385787-1.00024-5 | |
| dc.identifier.isbn | 9780323857871 | |
| dc.identifier.isbn | 9780323885720 | |
| dc.identifier.scopus | 2-s2.0-85130653569 | |
| dc.identifier.uri | https://doi.org/10.1016/B978-0-32-385787-1.00024-5 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.14365/3437 | |
| dc.language.iso | en | en_US |
| dc.publisher | Elsevier | en_US |
| dc.relation.ispartof | Deep Learning for Robot Perception and Cognition | en_US |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.subject | Arrhythmia detection | en_US |
| dc.subject | COVID-19 | en_US |
| dc.subject | Deep learning | en_US |
| dc.subject | Mortality risk prediction | en_US |
| dc.subject | Myocardial infarction | en_US |
| dc.title | Biosignal Time-Series Analysis | en_US |
| dc.type | Book Part | en_US |
| dspace.entity.type | Publication | |
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| gdc.description.departmenttemp | Kiranyaz, S., Department of Electrical Engineering, Qatar University, Doha, Qatar; İnce, Türker, Department of Electrical and Electronics Engineering, Izmir University of Economics, Izmir, Turkey; Chowdhury, M.E.H., Department of Electrical Engineering, Qatar University, Doha, Qatar; Degerli, A., Faculty of Information Technology and Communication Sciences, Tampere University, Tampere, Finland; Gabbouj, M., Faculty of Information Technology and Communication Sciences, Tampere University, Tampere, Finland | en_US |
| gdc.description.endpage | 539 | en_US |
| gdc.description.publicationcategory | Kitap Bölümü - Uluslararası | en_US |
| gdc.description.scopusquality | N/A | |
| gdc.description.startpage | 491 | en_US |
| gdc.description.wosquality | N/A | |
| gdc.identifier.openalex | W4211162123 | |
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| gdc.oaire.keywords | Myocardial infarction | |
| gdc.oaire.keywords | Arrhythmia detection | |
| gdc.oaire.keywords | COVID-19 | |
| gdc.oaire.keywords | Deep learning | |
| gdc.oaire.keywords | Mortality risk prediction | |
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| gdc.virtual.author | İnce, Türker | |
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