Compelling new electrocardiographic markers for automatic diagnosis. (June 2022)

• Record Type: Journal Article
• Authors: Rueda, Cristina; Fernández, Itziar; Larriba, Yolanda; Rodríguez-Collado, Alejandro; Canedo, Christian
• Is Part Of: Computer methods and programs in biomedicine. Volume 221(2022)
• Publication Date: 2022-06
• Subjects: FMM model -- ECG waves -- Diagnostic rule -- Bundle branch block; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2022.106807
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: The automatic diagnosis of some heart diseases from the electrocardiogram (ECG) signals is considered to be 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. We propose two simple rules for the automatic diagnosis of Blundle Branch Blocks that use two markers derived from the so-called FMMecg delineator. The advantages of this approach include the simplicity, the good statistical properties and clear interpretation in clinically meaningful terms of the new markers, and the high sensitivity and specificity values of the rules obtained from several well-known benchmarking databases. The rules can be used universally, being available at https://fmmmodel.shinyapps.io/fmmEcg/ for any given ECG signal. Abstract: 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 e c g 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 35, 000 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.