ECG Arrhythmia Classification based on Convolutional Autoencoders and Transfer Learning

Majlesi Journal of Electrical Engineering

مقاله 6، دوره 16، شماره 3، آذر 2022، صفحه 41-46   

نوع مقاله: Reseach Article

شناسه دیجیتال (DOI): 10.30486/mjee.2022.696505

نویسندگان

Rasool Muayad Obaidi^{* 1}؛ Riam Abdul Sattar²؛ Mayada Abd³؛ Inas Amjed Almani⁴؛ Tawfeeq Alghazali⁵؛ Saad Ghazi Talib⁶؛ Muneam Hussein Ali⁷؛ Mohammed Q. Mohammed⁸؛ Tuqaa Abid Mohammad⁹؛ Mariam Raheem Abdul-Sahib¹⁰

¹College of MLT, Ahl Al Bayt University, Kerbala, Iraq

²Al Farahidi University / College of Law/ Iraq

³Al-Manara College For Medical Sciences, Maysan, Iraq

⁴Department of Computer Technology Engineering, Al-Hadba University College, Iraq

⁵College of Media, Department of Journalism, The Islamic University in Najaf, Najaf, Iraq

⁶Law Department, Al-Mustaqbal University College, Babylon, Iraq

⁷Al-Nisour University College, Iraq

⁸Al-Esraa University College, Baghdad, Iraq

⁹Department of Dentistry, Al-Zahrawi University College, Karbala, Iraq

¹⁰Medical device engineering, Ashur University College, Baghdad, Iraq

چکیده

An Electrocardiogram (ECG) is a test that is done with the objective of monitoring the heart’s rhythm and electrical activity. It is conducted by attaching a specific type of sensor to the subject’s skin to detect the signals generated by the heartbeats. These signals can reveal significant information about the wellness of the subjects’ heart state, and cardiologists use them to detect abnormalities. Due to the prevalence of heart diseases amongst individuals around the globe, there is an urgent need to design computer-aided approaches to automatically analyze ECG signals. Recently, computer vision-based techniques have demonstrated remarkable performance in medical image analysis in a variety of applications and use cases. This paper proposes an approach based on Convolutional Autoencoders (CAEs) and Transfer Learning (TL). Our approach is an ensemble way of learning, the most useful features from both the signal itself, which is the input of the CAE, and the spectrogram version of the same signal, which is fed to a convolutional feature extractor named MobileNetV1. Based on the experiments conducted on a dataset collected from 3 well-known hospitals in Baghdad, Iraq, the proposed method claims good performance in classifying four types of problems in the ECG signals. Achieving an accuracy of 97.3% proves that our approach can be remarkably fruitful in situations where access to expert human resources is scarce.

کلیدواژه‌ها

Electrocardiogram (ECG)؛ Deep Learning؛ Transfer Learning؛ Convolutional AutoEncoders؛ EfficientNet؛ Heart Arrhythmia Classification

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