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Heart rate classification using ECG signal processing and machine learning methods

Papadogiorgaki Maria, Venianaki Maria, Charonyktakis Paulos, Antonakakis Marios, Tsamardinos, Ioannis, Zervakis Michail, Sakkalis, Vangelis

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URIhttp://purl.tuc.gr/dl/dias/BA9DF3E7-F375-4E1C-B47D-06A904575ED2-
Identifierhttps://doi.org/10.1109/BIBE52308.2021.9635462-
Identifierhttps://ieeexplore.ieee.org/document/9635462-
Languageen-
Extent6 pagesen
TitleHeart rate classification using ECG signal processing and machine learning methodsen
CreatorPapadogiorgaki Mariaen
CreatorΠαπαδογιωργακη Μαριαel
CreatorVenianaki Mariaen
CreatorCharonyktakis Paulosen
CreatorAntonakakis Mariosen
CreatorΑντωνακακης Μαριοςel
CreatorTsamardinos, Ioannisen
CreatorZervakis Michailen
CreatorΖερβακης Μιχαηλel
CreatorSakkalis, Vangelisen
PublisherInstitute of Electrical and Electronics Engineersen
Content SummaryElectrocardiogram (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%.en
Type of ItemΠλήρης Δημοσίευση σε Συνέδριοel
Type of ItemConference Full Paperen
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2023-05-12-
Date of Publication2021-
SubjectECGen
SubjectHeart rateen
SubjectSignal processingel
SubjectFeature extractionen
SubjectMachine learningen
SubjectConvolutional Neural Networksen
Bibliographic CitationM. Papadogiorgaki, M. Venianaki, P. Charonyktakis, M. Antonakakis, I. Tsamardinos, M. E. Zervakis and V. Sakkalis, "Heart rate classification using ECG signal processing and machine learning methods," presented at the 2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE), Kragujevac, Serbia, 2021, doi: 10.1109/BIBE52308.2021.9635462.en

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