A METHOD FOR DETECTING CORONARY ARTERY DISEASE USING NOISY ULTRASHORT ELECTROCARDIOGRAM RECORDINGS

Orestis Apostolou; Vasileios Charisis; Georgios Apostolidis; Leontios J. Hadjileontiadis

doi:10.1109/ICASSP43922.2022.9746632

A METHOD FOR DETECTING CORONARY ARTERY DISEASE USING NOISY ULTRASHORT ELECTROCARDIOGRAM RECORDINGS

Orestis Apostolou
, Vasileios Charisis
, Georgios Apostolidis
, Leontios J. Hadjileontiadis

Biomedical Engineering & Biotechnology

Aristotle University of Thessaloniki

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Scopus citations

Abstract

The current study aims at creating an algorithm able to detect Coronary Artery Disease (CAD), using ultrashort (duration of 30 seconds) one-lead ECG recordings. The presented method is designed to allow both electrode and noisy recordings (deriving from a smartwatch) as input. This is achieved by using an autoencoder neural network, which inspects the quality of each recording. The algorithm's core is a Support Vector Machine (SVM) model, which evaluates each patient's recordings and predicts whether they indicate CAD. Using statistics and combining the models mentioned above, a light, reliable, easy to use predicting system is created, suitable for deployment in a mobile application, which uses a smartwatch as its recording tool.

Original language	British English
Title of host publication	2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1336-1340
Number of pages	5
ISBN (Electronic)	9781665405409
DOIs	https://doi.org/10.1109/ICASSP43922.2022.9746632
State	Published - 2022
Event	47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Virtual, Online, Singapore Duration: 23 May 2022 → 27 May 2022

Publication series

Name	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume	2022-May
ISSN (Print)	1520-6149

Conference

Conference	47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022
Country/Territory	Singapore
City	Virtual, Online
Period	23/05/22 → 27/05/22

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

autoencoder
coronary artery disease
electrocardiogram
smartwatch
SVM

Access to Document

10.1109/ICASSP43922.2022.9746632

OpenUrl availability

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Apostolou, O., Charisis, V., Apostolidis, G., & Hadjileontiadis, L. J. (2022). A METHOD FOR DETECTING CORONARY ARTERY DISEASE USING NOISY ULTRASHORT ELECTROCARDIOGRAM RECORDINGS. In 2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings (pp. 1336-1340). (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2022-May). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP43922.2022.9746632

Apostolou, Orestis ; Charisis, Vasileios ; Apostolidis, Georgios et al. / A METHOD FOR DETECTING CORONARY ARTERY DISEASE USING NOISY ULTRASHORT ELECTROCARDIOGRAM RECORDINGS. 2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 1336-1340 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings).

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abstract = "The current study aims at creating an algorithm able to detect Coronary Artery Disease (CAD), using ultrashort (duration of 30 seconds) one-lead ECG recordings. The presented method is designed to allow both electrode and noisy recordings (deriving from a smartwatch) as input. This is achieved by using an autoencoder neural network, which inspects the quality of each recording. The algorithm's core is a Support Vector Machine (SVM) model, which evaluates each patient's recordings and predicts whether they indicate CAD. Using statistics and combining the models mentioned above, a light, reliable, easy to use predicting system is created, suitable for deployment in a mobile application, which uses a smartwatch as its recording tool.",

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author = "Orestis Apostolou and Vasileios Charisis and Georgios Apostolidis and Hadjileontiadis, \{Leontios J.\}",

note = "Funding Information: This research project was funded by the Abu Dhabi Department of Education and Knowledge (ADEK), UAE, under the Award for Research Excellence (AARE) 2018, ref. no: 29934 Publisher Copyright: {\textcopyright} 2022 IEEE; 47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 ; Conference date: 23-05-2022 Through 27-05-2022",

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Apostolou, O, Charisis, V, Apostolidis, G & Hadjileontiadis, LJ 2022, A METHOD FOR DETECTING CORONARY ARTERY DISEASE USING NOISY ULTRASHORT ELECTROCARDIOGRAM RECORDINGS. in 2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, vol. 2022-May, Institute of Electrical and Electronics Engineers Inc., pp. 1336-1340, 47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022, Virtual, Online, Singapore, 23/05/22. https://doi.org/10.1109/ICASSP43922.2022.9746632

A METHOD FOR DETECTING CORONARY ARTERY DISEASE USING NOISY ULTRASHORT ELECTROCARDIOGRAM RECORDINGS. / Apostolou, Orestis; Charisis, Vasileios; Apostolidis, Georgios et al.
2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2022. p. 1336-1340 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2022-May).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - The current study aims at creating an algorithm able to detect Coronary Artery Disease (CAD), using ultrashort (duration of 30 seconds) one-lead ECG recordings. The presented method is designed to allow both electrode and noisy recordings (deriving from a smartwatch) as input. This is achieved by using an autoencoder neural network, which inspects the quality of each recording. The algorithm's core is a Support Vector Machine (SVM) model, which evaluates each patient's recordings and predicts whether they indicate CAD. Using statistics and combining the models mentioned above, a light, reliable, easy to use predicting system is created, suitable for deployment in a mobile application, which uses a smartwatch as its recording tool.

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Apostolou O, Charisis V, Apostolidis G, Hadjileontiadis LJ. A METHOD FOR DETECTING CORONARY ARTERY DISEASE USING NOISY ULTRASHORT ELECTROCARDIOGRAM RECORDINGS. In 2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2022. p. 1336-1340. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). doi: 10.1109/ICASSP43922.2022.9746632

A METHOD FOR DETECTING CORONARY ARTERY DISEASE USING NOISY ULTRASHORT ELECTROCARDIOGRAM RECORDINGS

Abstract

Publication series

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UN SDGs

Keywords

Access to Document

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