ECG Arrhythmia Classification based on Convolutional Autoencoders and Transfer Learning - Rasool Muayad Obaidi; Riam Abdul Sattar; Mayada Abd; Inas Amjed Almani; Tawfeeq Alghazali; Saad Ghazi Talib; Muneam Hussein Ali; Mohammed Q. Mohammed; Tuqaa Abid Mohammad; Mariam Raheem Abdul-Sahib (Majlesi Journal of Electrical Engineering, 2022)
An Electrocardiogram (ECG) is a test that is done with the objective of monitoring the heartâs rhythm and electrical activity. It is conducted by attaching a specific type of sensor to the subjectâs skin to detect the signals generated by the heartbeats. These signals can reveal significant information about the wellness of the subjectsâ 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.