Ιδρυματικό Αποθετήριο
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| URI | http://purl.tuc.gr/dl/dias/BA9DF3E7-F375-4E1C-B47D-06A904575ED2 | - |
| Αναγνωριστικό | https://doi.org/10.1109/BIBE52308.2021.9635462 | - |
| Αναγνωριστικό | https://ieeexplore.ieee.org/document/9635462 | - |
| Γλώσσα | en | - |
| Μέγεθος | 6 pages | en |
| Τίτλος | Heart rate classification using ECG signal processing and machine learning methods | en |
| Δημιουργός | Papadogiorgaki Maria | en |
| Δημιουργός | Παπαδογιωργακη Μαρια | el |
| Δημιουργός | Venianaki Maria | en |
| Δημιουργός | Charonyktakis Paulos | en |
| Δημιουργός | Antonakakis Marios | en |
| Δημιουργός | Αντωνακακης Μαριος | el |
| Δημιουργός | Tsamardinos, Ioannis | en |
| Δημιουργός | Zervakis Michail | en |
| Δημιουργός | Ζερβακης Μιχαηλ | el |
| Δημιουργός | Sakkalis, Vangelis | en |
| Εκδότης | Institute of Electrical and Electronics Engineers | en |
| Περίληψη | 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%. | en |
| Τύπος | Πλήρης Δημοσίευση σε Συνέδριο | el |
| Τύπος | Conference Full Paper | en |
| Άδεια Χρήσης | http://creativecommons.org/licenses/by/4.0/ | en |
| Ημερομηνία | 2023-05-12 | - |
| Ημερομηνία Δημοσίευσης | 2021 | - |
| Θεματική Κατηγορία | ECG | en |
| Θεματική Κατηγορία | Heart rate | en |
| Θεματική Κατηγορία | Signal processing | el |
| Θεματική Κατηγορία | Feature extraction | en |
| Θεματική Κατηγορία | Machine learning | en |
| Θεματική Κατηγορία | Convolutional Neural Networks | en |
| Βιβλιογραφική Αναφορά | M. 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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