Cardiac Arrhythmia Classification from 12-lead Electrocardiogram Using a Combination of Deep Learning Approaches (CROSBI ID 718360)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Barišić, Marko ; Jović, Alan
engleski
Cardiac Arrhythmia Classification from 12-lead Electrocardiogram Using a Combination of Deep Learning Approaches
Traditionally, electrocardiogram (ECG) signals are recorded and monitored over a period of time and finally analyzed by an expert. Automatic classification of cardiac arrhythmias has the potential to improve diagnostics. In this work, we explore the use of representation learning from ECG signals for cardiac arrhythmia classification. The dataset consisting of five cardiac rhythm types was created from the CPSC, CPSC-Extra, and The Georgia 12-lead ECG Challenge databases. We use a sophisticated deep learning approach for representation learning and classification, namely a combination of a Convolutional Auto-Encoder (CAE) and a Long Short-Term Memory (LSTM) classifier. CAE was used to compress the input signal that serves as input to the LSTM classifier. We also implemented a CAE-based data augmentation approach to balance the data distribution. The classification results reaching above 90% accuracy show that the use of the complex deep learning approach is suitable for addressing the problem.
arrhythmia classification ; ECG ; deep learning ; convolutional autoencoder ; LSTM ; data augmentation
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Podaci o prilogu
1707-1712.
2022.
objavljeno
Podaci o matičnoj publikaciji
Proceedings of MIPRO 2022 45th Jubilee International Convention
Skala, Karolj
Rijeka: Hrvatska udruga za informacijsku i komunikacijsku tehnologiju, elektroniku i mikroelektroniku - MIPRO
1847-3938
1847-3946
Podaci o skupu
MIPRO 2022
predavanje
23.05.2022-27.05.2022
Opatija, Hrvatska