Heart Disease Detection Using ECG Waveforms
Keywords:
ADAM optimizer, Atrial fibrillation (AFib), Cardiovascular diseases, Convolutional Neural Network (CNN), Dart with Flutter, Disease probability detection, Electrocardiogram (ECG), ECG waveform, Healthcare service system, Heart’s electrical activity, Irregular heart rhythms, Kaggle figure, MySQL database, Softmax tensor, Ventricular tachycardia (VT)Abstract
Cardiovascular diseases, also known as CVDs, are still the main reason for sickness and death worldwide, with around 17.5 million deaths linked to them in 2012. The Electrocardiogram (ECG) signal shows the Heart's electrical activity on the body's surface, providing critical information about heart function. It is often used to spot any irregularities in heart rhythm and structure. Over the years, many techniques have been created and researched to classify and detect abnormalities in ECG signals, showing potential for use in medical settings. Current research frequently needs to provide thorough comparisons of different heart abnormalities. Some studies focus on specific conditions, such as atrial fibrillation, while others look at ST changes. This study introduces a new method using deep convolutional neural networks to classify heartbeats and accurately detect five types of arrhythmias. Our technique involves training a Convolutional Neural Network (CNN) on a meticulously selected dataset, thoroughly validating it, and fine-tuning it with specific parameters and epochs. By feeding ECG images into the model, users can quickly determine whether the cardiac condition is normal or abnormal.
