RT info:eu-repo/semantics/article
T1 Compelling new electrocardiographic markers for automatic diagnosis
A1 Rueda Sabater, María Cristina
A1 Fernández Martínez, Itziar
A1 Larriba González, Yolanda
A1 Rodríguez Collado, Alejandro
A1 Canedo, Christian
K1 Electrocardiographic markers
K1 Marcadores electrocardiográficos
AB Background and Objective: The automatic diagnosis of heart diseases from the electrocardiogram (ECG) signal is crucial in clinical decision-making. However, the use of computer-based decision rules in clinical practice is still deficient, mainly due to their complexity and a lack of medical interpretation. The objetive of this research is to address these issues by providing valuable diagnostic rules that can be easily implemented in clinical practice. In this research, efficient diagnostic rules friendly in clinical practice are provided. Methods: In this paper, interesting parameters obtained from the ECG signals analysis are presented and two simple rules for automatic diagnosis of Bundle Branch Blocks are defined using new markers derived from the so-called FMM delineator. The main advantages of these markers are the good statistical properties and their clear interpretation in clinically meaningful terms. Results: High sensitivity and specificity values have been obtained using the proposed rules with data from more than 35000 patients from well known benchmarking databases. In particular, to identify Complete Left Bundle Branch Blocks and differentiate this condition from subjects without heart diseases, sensitivity and specificity values ranging from 93% to 99% and from 96% to 99%, respectively. The new markers and the automatic diagnosis are easily available at https://fmmmodel.shinyapps.io/fmmEcg/, an app specifically developed for any given ECG signal. Conclusions: The proposal is different from others in the literature and it is compelling for three main reasons. On the one hand, the markers have a concise electrocardiographic interpretation. On the other hand, the diagnosis rules have a very high accuracy. Finally, the markers can be provided by any device that registers the ECG signal and the automatic diagnosis is made straightforwardly, in contrast to the black-box and deep learning algorithms.
PB Elsevier
SN 0169-2607
YR 2022
FD 2022
LK https://uvadoc.uva.es/handle/10324/53029
UL https://uvadoc.uva.es/handle/10324/53029
LA eng
NO Computer Methods and Programs in Biomedicine, 2022, vol. 221, 106807
NO Producción Científica
DS UVaDOC
RD 06-ago-2024