Title Deep-learning-based estimation of the spatial QRS-T angle from reduced-lead ECGs /
Authors Santos Rodrigues, Ana ; Augustauskas, Rytis ; Lukoševičius, Mantas ; Laguna, Pablo ; Marozas, Vaidotas
DOI 10.3390/s22145414
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Is Part of Sensors.. Basel : MDPI. 2022, vol. 22, iss. 14, art. no. 5414, p. 1-22.. ISSN 1424-8220
Keywords [eng] cardiovascular heath assessment ; composite loss function ; consumer healthcare devices ; machine learning ; regression ; unobtrusive monitoring ; wearable devices
Abstract [eng] The spatial QRS-T angle is a promising health indicator for risk stratification of sudden cardiac death (SCD). Thus far, the angle is estimated solely from 12-lead electrocardiogram (ECG) systems uncomfortable for ambulatory monitoring. Methods to estimate QRS-T angles from reduced-lead ECGs registered with consumer healthcare devices would, therefore, facilitate ambulatory monitoring. (1) Objective: Develop a method to estimate spatial QRS-T angles from reduced-lead ECGs. (2) Approach: We designed a deep learning model to locate the QRS and T wave vectors necessary for computing the QRS-T angle. We implemented an original loss function to guide the model in the 3D space to search for each vector's coordinates. A gradual reduction of ECG leads from the largest publicly available dataset of clinical 12-lead ECG recordings (PTB-XL) is used for training and validation. (3) Results: The spatial QRS-T angle can be estimated from leads {I, II, aVF, V2} with sufficient accuracy (absolute mean and median errors of 11.4° and 7.3°) for detecting abnormal angles without sacrificing patient comfortability. (4) Significance: Our model could enable ambulatory monitoring of spatial QRS-T angles using patch- or textile-based ECG devices. Populations at risk of SCD, like chronic cardiac and kidney disease patients, might benefit from this technology.
Published Basel : MDPI
Type Journal article
Language English
Publication date 2022
CC license CC license description