Heart rate classification using ECG signal processing and machine learning methodsHeart rate classification using ECG signal processing and machine learning methods Πλήρης Δημοσίευση σε Συνέδριο Conference Full Paper 2023-05-122021enThis work was prepared in the framework of the x(epi)VLEPSIS project, which is co‐financed by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH – CREATE – INNOVATE (project code: T1EDK-04440).Electrocardiogram (ECG) signal constitutes a valuable technique that provides considerable information towards the early diagnosis of several cardiovascular diseases, especially regarding the detection of abnormal heart rate, namely arrhythmias. In this paper, innovative methodologies that allow for the efficient classification of cardiac rhythm are presented. The proposed methods are based on ECG signal analysis, extraction of significant features, as well as classification algorithms. Several clinical, time- and frequency-domain features are either calculated, or automatically extracted by means of a Convolutional Neural Network, while traditional machine learning algorithms, such as k-Nearest Neighbors and Random Forests are employed in order to classify the ECG signals among 7 different cases of abnormal and normal heart rate. The learning methods are carried out within the JADBio software tool, that also performs feature selection prior to classification. The experimental results demonstrate high performance of the deployed methods in terms of relevant statistical metrics, while they yielded an average validation Area Under the Curve (AUC) of 99.9%.http://creativecommons.org/licenses/by/4.0/2021 IEEE 21st International Conference on Bioinformatics and Bioengineering Papadogiorgaki Maria Παπαδογιωργακη Μαρια Venianaki Maria Charonyktakis Paulos Antonakakis Marios Αντωνακακης Μαριος Tsamardinos, Ioannis Zervakis Michail Ζερβακης Μιχαηλ Sakkalis, Vangelis Institute of Electrical and Electronics Engineers ECG Heart rate Signal processing Feature extraction Machine learning Convolutional Neural Networks