Naslov
Cardiac Arrhythmia Classification from 12-lead Electrocardiogram Using a Combination of Deep Learning Approaches

Autori
Barišić, Marko ; Jović, Alan

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
Proceedings of MIPRO 2022 45th Jubilee International Convention / Skala, Karolj - Rijeka : Hrvatska udruga za informacijsku i komunikacijsku tehnologiju, elektroniku i mikroelektroniku - MIPRO, 2022, 1707-1712

Skup
45th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO 2022)

Mjesto i datum
Opatija, Hrvatska, 23.05.2022. - 27.05.2022

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
arrhythmia classification ; ECG ; deep learning ; convolutional autoencoder ; LSTM ; data augmentation

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo, Kliničke medicinske znanosti

POVEZANOST RADA