Cardiac arrhythmia detection from 2d ecg images by using deep learning technique
2019 Medical Technologies Congress, TIPTEKNO 2019, İzmir, Türkiye, 3 - 05 Ekim 2019, (Tam Metin Bildiri)
- Yayın Türü: Bildiri / Tam Metin Bildiri
- Doi Numarası: 10.1109/tiptekno.2019.8895011
- Basıldığı Şehir: İzmir
- Basıldığı Ülke: Türkiye
- Anahtar Kelimeler: Arrhythmia Detection, Convolutional Neural Networks, Deep Learning, ECG Images, Electrocardiogram
- Kütahya Sağlık Bilimleri Üniversitesi Adresli: Hayır
Özet
Arrhythmia is irregular changes of normal heart rhythm and effective manual identifying of them require a lot of time and depends on experience of clinicians. This paper proposes deep learning-based novel 2-D convolutional neural network (CNN) approach for accurate classification of five different arrhythmia types. The performance of the proposed architecture is tested on Electrocardiogram (ECG) signals that are taken from MIT-BIH arrhythmia benchmark database. ECG signals was segmented into heartbeats and each of the heartbeats was converted into 2-D grayscale images as an input data for CNN structure. The accuracy of the proposed architecture was found as 97.42% on the training results revealed that the proposed 2-D CNN architecture with transformed 2-D ECG images can achieve highest accuracy without any preprocessing and feature extraction and feature selection stages for ECG signals.