An enhanced ResNet-50 deep learning model for arrhythmia detection using electrocardiogram biomedical indicators

Abstract

Electrocardiogram (ECG) is one among the most common detecting techniques in the analysis and detection of cardiac arrhythmia adopted due to its cost efficiency and simplicity. In a clinical routine, ECG database is collected on daily basis and these databases are reviewed manually. Along with other conventional methods, various approaches using machine learning has been proposed in the past few years. But these would require in-depth knowledge on several parameters and pre-processing techniques in the specific domain. This study is aimed at implementing a more reliable deep learning model that has the capacity to diagnose arrhythmia from a database with 109,446 samples in 5 different categories. In our proposed work, we have used deep learning methodologies for the diagnosis and detection of cardiac arrhythmia automatically. Balancing the biasedness in the waveforms from MIT-BIH arrhythmia database, model is developed. MIT-BIH arrhythmia database with the ECG waveforms promises good accuracy. This automated prediction of the disease using CNN and ResNet-18 architectures are compared in terms of accuracy. CNN has accuracy approximately 97.86% and 98.14% for improved ResNet-18. Also, a comparative analysis is done with the proposed model and already existing techniques. Several limitations and future opportunities are also reviewed. We believe it can be used considerably for cardiac arrhythmia prediction worldwide. Based on the results obtained, ResNet-18 architecture can be used as an efficient procedure, that reduces the burden of training a deep convolutional neural network from start, resulting in a technique that is simple to use.