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

Files in This Item:
File SizeFormat 
AT15-3437-Biosignal.pdf
  Restricted Access
3.45 MBAdobe PDFView/Open    Request a copy
Show full item record



CORE Recommender

SCOPUSTM   
Citations

3
checked on Nov 20, 2024

Page view(s)

246
checked on Nov 18, 2024

Download(s)

2
checked on Nov 18, 2024

Google ScholarTM

Check




Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.