Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14365/3437
Title: | Biosignal time-series analysis | Authors: | Kiranyaz S. İnce, Türker Chowdhury M.E.H. Degerli A. Gabbouj, Moncef |
Keywords: | Arrhythmia detection COVID-19 Deep learning Mortality risk prediction Myocardial infarction |
Publisher: | Elsevier | 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. | URI: | https://doi.org/10.1016/B978-0-32-385787-1.00024-5 https://hdl.handle.net/20.500.14365/3437 |
ISBN: | 9780323857871 9780323885720 |
Appears in Collections: | Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection |
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File | Size | Format | |
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AT15-3437-Biosignal.pdf Restricted Access | 3.45 MB | Adobe PDF | View/Open Request a copy |
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