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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

Απλή Εγγραφή


URIhttp://purl.tuc.gr/dl/dias/BA9DF3E7-F375-4E1C-B47D-06A904575ED2-
Αναγνωριστικόhttps://doi.org/10.1109/BIBE52308.2021.9635462-
Αναγνωριστικόhttps://ieeexplore.ieee.org/document/9635462-
Γλώσσαen-
Μέγεθος6 pagesen
ΤίτλοςHeart rate classification using ECG signal processing and machine learning methodsen
ΔημιουργόςPapadogiorgaki Mariaen
ΔημιουργόςΠαπαδογιωργακη Μαριαel
ΔημιουργόςVenianaki Mariaen
ΔημιουργόςCharonyktakis Paulosen
ΔημιουργόςAntonakakis Mariosen
ΔημιουργόςΑντωνακακης Μαριοςel
ΔημιουργόςTsamardinos, Ioannisen
ΔημιουργόςZervakis Michailen
ΔημιουργόςΖερβακης Μιχαηλel
ΔημιουργόςSakkalis, Vangelisen
ΕκδότηςInstitute of Electrical and Electronics Engineersen
ΠερίληψηElectrocardiogram (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
ΤύποςΠλήρης Δημοσίευση σε Συνέδριοel
ΤύποςConference Full Paperen
Άδεια Χρήσηςhttp://creativecommons.org/licenses/by/4.0/en
Ημερομηνία2023-05-12-
Ημερομηνία Δημοσίευσης2021-
Θεματική ΚατηγορίαECGen
Θεματική ΚατηγορίαHeart rateen
Θεματική ΚατηγορίαSignal processingel
Θεματική ΚατηγορίαFeature extractionen
Θεματική ΚατηγορίαMachine learningen
Θεματική ΚατηγορίαConvolutional Neural Networksen
Βιβλιογραφική ΑναφοράM. 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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