diagnostics Article Potential of Rule-Based Methods and Deep Learning Architectures for ECG Diagnostics Giovanni Bortolan 1 , Ivaylo Christov 2, * and Iana Simova 3   Citation: Bortolan, G.; Christov, I.; Simova, I. Potential of Rule-Based Methods and Deep Learning Architectures for ECG Diagnostics. Diagnostics 2021, 11, 1678. https:// doi.org/10.3390/diagnostics11091678 Academic Editor: Henk A. Marquering Received: 17 August 2021 Accepted: 10 September 2021 Published: 14 September 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). 1 Institute of Neuroscience IN-CNR, Corso Stati Uniti 4, 35127 Padova, Italy; giovanni.bortolan@cnr.it 2 Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Acad. G. Bonchev Str. Bl 105, 1113 Sofia, Bulgaria 3 Heart and Brain Center of Excellence, University Hospital Pleven, Pierre Curie 2 Str, 5804 Pleven, Bulgaria; ianasimova@gmail.com * Correspondence: Ivaylo.Christov@biomed.bas.bg Abstract: The main objective of this study is to propose relatively simple techniques for the auto- matic diagnosis of electrocardiogram (ECG) signals based on a classical rule-based method and a convolutional deep learning architecture. The validation task was performed in the framework of the PhysioNet/Computing in Cardiology Challenge 2020, where seven databases consisting of 66,361 recordings with 12-lead ECGs were considered for training, validation and test sets. A total of 24 different diagnostic classes are considered in the entire training set. The rule-based method uses morphological and time-frequency ECG descriptors that are defined for each diagnostic la- bel. These rules are extracted from the knowledge base of a cardiologist or from a textbook, with no direct learning procedure in the first phase, whereas a refinement was tested in the second phase. The deep learning method considers both raw ECG and median beat signals. These data are processed via continuous wavelet transform analysis, obtaining a time-frequency domain represen- tation, with the generation of specific images (ECG scalograms). These images are then used for the training of a convolutional neural network based on GoogLeNet topology for ECG diagnostic classification. Cross-validation evaluation was performed for testing purposes. A total of 217 teams submitted 1395 algorithms during the Challenge. The diagnostic accuracy of our algorithm produced a challenge validation score of 0.325 (CPU time = 35 min) for the rule-based method, and a 0.426 (CPU time = 1664 min) for the deep learning method, which resulted in our team attaining 12th place in the competition. Keywords: ECG; arrhythmia; features; rule-based method; convolutional neural network; GoogLeNet network; wavelet transform; scalogram; PhysioNet/Computing in Cardiology Challenge 2020 1. Introduction The automatic detection and classification of cardiac abnormalities from 12-lead ECG signals has been an area of research interest for a long time [1]. Methods have ranged from medical decision-support systems to statistical approaches, from simple neural network architectures to more sophisticated methods based on deep neural networks [1–3]. There has been much focus on research employing the use of deep learning with medical images [4], time series classification [5], and object detection [6]. In [7], a deep recurrent neural network approach was developed and tested for the classification of four types of the severity of atrial fibrillation (AF) based on 21 features. The use of continuous wavelet transforms (CWTs) for ECG signal processing is present in several studies; for example, in [8] the CWT was considered for multiscale parameter estimation for delineation of the fiducial points of P-QRS-T waves. Recent examples of diagnostic 12-lead ECG classification have been reported. They come from the use of a deep neural network for the classification of six diagnostic classes [3], whereas the study in [9] considered the analysis of 12-lead ECG signals based on deep Diagnostics 2021, 11, 1678. https://doi.org/10.3390/diagnostics11091678 https://www.mdpi.com/journal/diagnostics