DEEP LEARNING BASED ARRHYTHMIA DISEASE DETECTION USING ELECTROCARDIOGRAM SIGNALS

Abstract

The primary objective of this work is to use the patient’s electrocardiogram (ECG) to develop a method for early diagnosis of Arrhythmia disease. Traditional methods of diagnosing Ar- rhythmia often require extensive tests and a laborious workup in a hospital setting. Improved diagnostic technologies are needed to enable early and precise detection of cardiovascular diseases, particularly Arrhythmias. In this work, deep learning is integrated with signal processing techniques to detect Arrhythmia disease, using a dataset from the well-known MIT-BIH Arrhythmia Database. The first step is to convert the raw ECG information into visual representations using Wavelet transforms. These transforms capture precise temporal and frequency properties, unveiling detailed information about the underlying complexities of the heart rhythm. A Convolutional Neural Network (CNN) model specifically designed for Arrhythmia detection is then trained using this transformed data. Deep learning uti- lizes pre-existing knowledge from established architectures, enhancing the model’s ability to detect patterns indicative of Arrhythmia diseases. The proposed methodology is carefully evaluated on a testing set using performance metrics such as accuracy, sensitivity, and speci- ficity. These performance metrics are crucial indicators of the model’s ability to accurately distinguish between normal, web, and even cardiac rhythms. Overall, this research aims to develop a novel approach combining deep learning and signal processing techniques to enhance the detection of Arrhythmia diseases using ECG data.