Enhancing ECG-Based Cardiac Anomaly Detection Using Deep Learning for Improved Diagnostic Efficiency and Clinical Precision

Abstract

Cardiovascular problems cause a lot of illness and death around the world. To improve early diagnosis and treatment accuracy, testing methods need to be improved. Focus of this work is on using deep learning methods to make electrocardiogram (ECG)-based heart abnormality identification more accurate and time-effective. In this study variety of deep learning architectures, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory networks with Gated Recurrent Units (LSTM-GRU), Generative Adversarial Networks (GANs), and Visual Geometry Group networks (VGG), to create models that could detect subtle patterns in ECG signals that could point to possible heart problems. An extensive collection of thousands of labelled ECG records, ranging from normal to those with a variety of heart problems, was used to train these models. The suggested approach model architecture's performance was improved by fine-tuning many hyperparameters and confirmed using a different set of tests. According to our results, the LSTM-GRU model was more accurate and useful in clinical settings than other models. Accuracy of 97.5% was achieved by this model in catching time relationships and subtleties in ECG data. Furthermore, the VGG and CNN models did a good job of extracting features from raw ECG readings, and the GAN model was especially good at adding to the training dataset, which made the model more stable overall. The LSTM-GRU model was the best at finding heart abnormalities based on ECGs. This suggests that combining deep learning with complicated sequence modelling can greatly improve diagnostic accuracy and patient results. This method could be used in real life in automatic ECG analysis systems, giving doctors a useful way to check on heart health quickly and accurately.