Automated ECG Analysis for Cardiovascular Disease Prediction Using Deep Learning

Global health is seriously threatened by cardiovascular diseases (CVD), which calls for precise and effective diagnostic instruments. While qualitative and quantitative studies have been the mainstay of traditional approaches for assessing heart problems, current developments in machine learning and big data provide new possibilities for better diagnoses. By combining signal processing methods, hybrid machine learning models, and cardiology, this work suggests a unique method for predicting ECG diseases. Wavelet transformation is used to convert the ECG signals into a time-frequency representation, enabling more thorough analysis. While convolutional neural networks (CNNs) examine spatial aspects in the converted time-frequency images, recurrent neural networks (RNNs) are used to recognise sequential patterns in ECG data. Furthermore, K-Nearest Neighbours (KNN) offers a reliable categorisation model for prediction validation. Our results show that in healthcare settings, combining machine learning and signal processing methods may greatly increase classification accuracy while lowering staffing costs and diagnostic errors.