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Article

Electrocardiographic Fragmented Activity (II): A Machine Learning Approach to Detection

1
Unidad de Arritmias, Hospital Clínico Universitario Virgen de la Arrixaca, 30120 El Palmar, Spain
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Departamento de Medicina Interna, Universidad de Murcia, 30001 Murcia, Spain
3
Instituto Murciano de Investigación Biosanitaria Virgen de la Arrixaca (IMIB), 30120 El Palmar, Spain
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Departamento de Ingeniería de Comunicaciones, Universidad Miguel Hernández, 03202 Elche, Spain
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Departamento de Teoría de la Señal y Comunicaciones y Sistemas Telemáticos y Computación, Universidad Rey Juan Carlos, 28943 Fuenlabrada, Spain
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Center for Computational Simulation, Universidad Politécnica de Madrid, 28223 Pozuelo de Alarcón, Spain
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(17), 3565; https://doi.org/10.3390/app9173565
Received: 15 July 2019 / Revised: 19 August 2019 / Accepted: 27 August 2019 / Published: 31 August 2019
Hypertrophic cardiomyopathy, according to its prevalence, is a comparatively common disease related to the risk of suffering sudden cardiac death, heart failure and stroke. This illness is characterized by the excessive deposition of collagen among healthy myocardium cells. This situation, which is medically known as fibrosis, constitutes effective conduction obstacles in the myocardium electrical path, and when severe enough, it can be outlined as additional peaks or notches in the QRS, clinically entitled as fragmentation. Nowadays, the fragmentation detection is performed by visual inspection, but the fragmented QRS can be confused with the noise present in the electrocardiogram (ECG). On the other hand, fibrosis detection is performed by magnetic resonance imaging with late gadolinium enhancement, the main drawback of this technique being its cost in terms of time and money. In this work, we propose two automatic algorithms, one for fragmented QRS detection and another for fibrosis detection. For this purpose, we used four different databases, including the subrogated database described in the companion paper and incorporating three additional ones, one compounded by more accurate subrogated ECG signals and two compounded by real and affected subjects as labeled by expert clinicians. The first real-world database contains QRS fragmented records and the second one contains records with fibrosis and both were recorded in Hospital Clínico Universitario Virgen de la Arrixaca (Spain). To deeply analyze the scope of these datasets, we benchmarked several classifiers such as Neural Networks, Support Vector Machines (SVM), Decision Trees and Gaussian Naïve Bayes (NB). For the fragmentation dataset, the best results were 0.94 sensitivity, 0.88 specificity, 0.89 positive predictive value, 0.93 negative predictive value and 0.91 accuracy when using SVM with Gaussian kernel. For the fibrosis databases, more limited accuracy was reached, with 0.47 sensitivity, 0.91 specificity, 0.82 predictive positive value, 0.66 negative predictive value and 0.70 accuracy when using Gaussian NB. Nevertheless, this is the first time that fibrosis detection is attempted automatically from ECG postprocessing, paving the way towards improved algorithms and methods for it. Therefore, we can conclude that the proposed techniques could offer a valuable tool to clinicians for both fragmentation and fibrosis diagnoses support. View Full-Text
Keywords: ECG; fragmentation detection; multivariate techniques; fibrosis detection; machine learning ECG; fragmentation detection; multivariate techniques; fibrosis detection; machine learning
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MDPI and ACS Style

Melgarejo-Meseguer, F.-M.; Gimeno-Blanes, F.-J.; Salar-Alcaraz, M.-E.; Gimeno-Blanes, J.-R.; Martínez-Sánchez, J.; García-Alberola, A.; Rojo-Álvarez, J.L. Electrocardiographic Fragmented Activity (II): A Machine Learning Approach to Detection. Appl. Sci. 2019, 9, 3565. https://doi.org/10.3390/app9173565

AMA Style

Melgarejo-Meseguer F-M, Gimeno-Blanes F-J, Salar-Alcaraz M-E, Gimeno-Blanes J-R, Martínez-Sánchez J, García-Alberola A, Rojo-Álvarez JL. Electrocardiographic Fragmented Activity (II): A Machine Learning Approach to Detection. Applied Sciences. 2019; 9(17):3565. https://doi.org/10.3390/app9173565

Chicago/Turabian Style

Melgarejo-Meseguer, Francisco-Manuel; Gimeno-Blanes, Francisco-Javier; Salar-Alcaraz, María-Eladia; Gimeno-Blanes, Juan-Ramón; Martínez-Sánchez, Juan; García-Alberola, Arcadi; Rojo-Álvarez, José L. 2019. "Electrocardiographic Fragmented Activity (II): A Machine Learning Approach to Detection" Appl. Sci. 9, no. 17: 3565. https://doi.org/10.3390/app9173565

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