Detection of Heart Failure Using a Convolutional Neural Network (CNN) via ECG Signals

Heart failure is a common cardiovascular disease that millions of people experience globally which is also called as Chronic heart failure (CHF). Early detection and prompt intervention are essential for heart failure management to improve patient outcomes and save healthcare costs. Electrocardiogram (ECG) signals are frequently used to diagnose cardiac problems, including heart failure. They offer essential information about the electrical activity of the heart. Convolutional Neural Networks (CNNs), in particular, are machine learning approaches that have demonstrated promise in automating the identification of heart failure using ECG readings in recent years. This work suggests a unique method for heart failure detection based on pre-trained architecture trained on ECG signal data, namely an ECG-UNET. The CNN model automatically identifies pertinent characteristics from unprocessed ECG signals and categorizes them as standard or cardiac failure. The proposed CNN model is trained and evaluated on a large dataset that includes ECG recordings from patients with heart failure diagnoses and healthy controls. The outcomes show how well the CNN-based method can identify heart failure from ECG readings. With its high sensitivity and specificity, the model shows promise for use in clinical settings as a screening tool for the early identification of heart failure. Furthermore, the suggested CNN design is scalable and computationally efficient, qualifying it for real-time deployment in various healthcare environments.