Affiliations: [a] Instituto de Ingeniería Biomédica, Fac. de Ingeniería, Universidad de Buenos Aires (FIUBA), Argentina | [b] Departamento de Electrónica, Tecnología de Computadoras y Proyectos, Universidad Politécnica de Cartagena, Cartagena, Spain | [c] Instituto Argentino de Matemática Alberto P. Calderón (IAM, CONICET), Argentina
Correspondence:
[*]
Corresponding author: María Paula Bonomini, Departamento de Electrónica, Tecnología de Computadoras y Proyectos, Universidad Politécnica de Cartagena, Cartagena, Spain. E-mail: [email protected].
Abstract: Left bundle branch block is a cardiac conduction disorder that occurs when the electrical impulses that control the heartbeat are blocked or delayed as they travel through the left bundle branch of the cardiac conduction system providing a characteristic electrocardiogram (ECG) pattern. A reduced set of biologically inspired features extracted from ECG data is proposed and used to train a variety of machine learning models for the LBBB classification task. Then, different methods are used to evaluate the importance of the features in the classification process of each model and to further reduce the feature set while maintaining the classification performance. The performances obtained by the models using different metrics improve those obtained by other authors in the literature on the same dataset. Finally, XAI techniques are used to verify that the predictions made by the models are consistent with the existing relationships between the data. This increases the reliability of the models and their usefulness in the diagnostic support process. These explanations can help clinicians to better understand the reasoning behind diagnostic decisions.
