Abstract
The electrocardiogram signal (ECG) represents the electrical activity of the heart measured at the body surface. Characteristic waves and their delineation marks are studied to define cardiac markers without using invasive procedures. Among them, slower adaptation of the QT interval, the time needed for ventricular depolarization plus repolarization, to sudden abrupt changes in heart rate (HR) has been identified as a marker of arrhythmic risk. Such abrupt HR changes are difficult to induce, leading here to explore estimation of this delay from the ramp-like HR variations observed in exercise stress test. However, stress test ECG signals are often corrupted by muscular activity and electrode motion, limiting the robustness of the information that can be extracted from them, as the identification of the T-wave end. The aim of this study is to find proper methods to emphasize the T-wave in order to improve delineation accuracy. Stress test ECG recordings from 447 subjects were analyzed. The first spatially-transformed lead based on two different lead-space reduction (LSR) techniques, and different learning versions, was used to delineate and obtain the QT series. Assuming that QT delineation errors will lead to a high variability in the QT interval series, the power of the high-pass filtered QT interval series was used as performance marker (the lower the power, the most stable the delineation). Periodic component analysis technique showed the lowest power, with no significant differences between its different learning versions.
Original language | English |
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Title of host publication | 55th Asilomar Conference on Signals, Systems and Computers, ACSSC 2021 |
Subtitle of host publication | October 31 - November 3, 2021 Pacific Grove, California |
Editors | Michael B. Matthews |
Publisher | IEEE |
Pages | 261-264 |
Number of pages | 4 |
ISBN (Electronic) | 9781665458283 |
DOIs | |
Publication status | Published - 2022 |
Publication type | A4 Article in conference proceedings |
Event | Asilomar Conference on Signals, Systems and Computers - Virtual, Pacific Grove, United States Duration: 31 Oct 2021 → 3 Nov 2021 |
Publication series
Name | Conference Record - Asilomar Conference on Signals, Systems and Computers |
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Volume | 2021-October |
ISSN (Print) | 1058-6393 |
Conference
Conference | Asilomar Conference on Signals, Systems and Computers |
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Country/Territory | United States |
City | Virtual, Pacific Grove |
Period | 31/10/21 → 3/11/21 |
Funding
This work was funded by project PID2019-104881RB-I00, and PID2019-105674RB-I00 funded by Spanish Ministry of Science and Innovation (MICINN) and FEDER, by Gobierno de Aragón (Reference Group Biomedical Signal Interpretation and Computational Simulation (BSICoS) T39-20R) cofunded by FEDER 2014-2020 “Building Europe from Aragón”, and by European Research Council (ERC) through project ERC-StG 638284. The computation was performed at the High
Keywords
- biomedical marker
- Periodic component analysis
- QT interval
Publication forum classification
- Publication forum level 1
ASJC Scopus subject areas
- Signal Processing
- Computer Networks and Communications