Monitoring of Cardiovascular Diseases with Deep Learning and ECG Data

Abstract

The heart is a remarkable organ that operates continuously throughout a person s life, playing a vital role in sustaining life. Cardiovascular diseases (CVDs), commonly known as heart diseases, represent the leading cause of death worldwide and pose a significant public health challenge, accounting for nearly 18 million deaths annually. Understanding and effectively managing CVDs are essential for improving overall health and well-being. Early detection enables timely intervention, which can greatly enhance patient outcomes. Adopting lifestyle changes such as regular exercise, a balanced diet, and routine medical check-ups can significantly reduce CVD risk. Various diagnostic tools including electrocardiograms (ECGs), echocardiograms, cardiac CT and MRI scans, blood pressure monitoring, cholesterol profiling, and diabetes evaluations are crucial in assessing heart health. Continued research is vital for improving prevention, diagnosis, and treatment strategies for CVDs. newlineAmong diagnostic tools, ECGs are widely used because they are non-invasive, cost-effective, and capable of recording the heart s electrical activity to provide visual representations of its impulses. However, manual analysis of ECG data is time-consuming and susceptible to human error. Automated detection of CVDs using artificial intelligence applied to ECG signal analysis offers a transformative approach to early diagnosis and risk assessment. This process involves several stages, including data pre-processing to remove noise, signal normalization, and feature extraction. newlineTraditional approaches often rely on handcrafted features and rule-based algorithms, which limit their ability to capture the complex patterns present in multi-source ECG data. The introduction of deep learning (DL) architectures has revolutionized automated CVD detection, achieving performance levels comparable to those of expert cardiologists. Techniques such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) have significantly improved the accuracy a