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https://hdl.handle.net/20.500.14365/2885
Title: | Ecg Arrhythmia Detection With Deep Learning | Authors: | Izci, Elif Degirmenci, Murside Ozdemir, Mehmet Akif Akan, Aydin |
Keywords: | Arrhythmia Deep Learning ECG Images Classification |
Publisher: | IEEE | Abstract: | Arrhythmia is any irregularity of heart rate that cause an abnormality in your heart rhythm. Manual analysis of Electrocardiogram (ECG) signal is not enough for quickly identify abnormalities in the heart rhythm. This paper proposes a deep learning approach for detection of five different arrhythmia types based on 2D convolutional neural networks (CNN) architecture. ECG signals were obtained from MIT-BIll arrhythmia database. For CNN architecture, each ECG signal was segmented into heartbeats, then each heartbeat was transformed into 2D grayscale heartbeat image. 2D CNN model was used due to success of image recognition. The proposed model result demonstrate that CNN and ECG image formation give highest result when classified different types of ECG arrhythmic signals. | Description: | 28th Signal Processing and Communications Applications Conference (SIU) -- OCT 05-07, 2020 -- ELECTR NETWORK | URI: | https://hdl.handle.net/20.500.14365/2885 | ISBN: | 978-1-7281-7206-4 | ISSN: | 2165-0608 |
Appears in Collections: | WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection |
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