TY - GEN
T1 - A METHOD FOR DETECTING CORONARY ARTERY DISEASE USING NOISY ULTRASHORT ELECTROCARDIOGRAM RECORDINGS
AU - Apostolou, Orestis
AU - Charisis, Vasileios
AU - Apostolidis, Georgios
AU - Hadjileontiadis, Leontios J.
N1 - 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:
© 2022 IEEE
PY - 2022
Y1 - 2022
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.
AB - 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.
KW - autoencoder
KW - coronary artery disease
KW - electrocardiogram
KW - smartwatch
KW - SVM
UR - http://www.scopus.com/inward/record.url?scp=85131266339&partnerID=8YFLogxK
U2 - 10.1109/ICASSP43922.2022.9746632
DO - 10.1109/ICASSP43922.2022.9746632
M3 - Conference contribution
AN - SCOPUS:85131266339
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 1336
EP - 1340
BT - 2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022
Y2 - 23 May 2022 through 27 May 2022
ER -