Journal of Artificial Intelligence and Metaheuristics JAIM 2833-5597 10.54216/JAIM https://www.americaspg.com/journals/show/2373 2022 2022 Department of ECE, I.E.T., Dr. Rammanohar Lohia Avadh University, Ayodhya, UP, India Ashutosh Kumar Singh Department of CSE-AI & ML, St. Martin's Engineering College, Secunderabad, Telangana, India R. Karthikeyan Department of ECE, Malla Reddy Engineering College, Secunderabad, Telangana, India P. Joel Josephson Department of ECE, Hindustan Institute of Technology and Science, Chennai, TN, India Pallavi Singh Advanced methods are needed for fast and reliable detection of cardiovascular illnesses, which continue to be a primary source of morbidity and death globally. Using deep learning, this research presents a new method, dubbed "DeepLearnCardia," for analyzing electrophysiological data in cardiac bioengineering. To improve the analysis of cardiac electrophysiological data and provide a complete solution for arrhythmia prediction, the proposed technique combines wavelet transformations, attention processes, and multimodal fusion. Data preprocessing, feature extraction using wavelets, temporal encoding using Long Short-Term Memory (LSTM) networks, an attention mechanism, multimodal fusion, and spatial analysis with Convolutional Neural Networks (CNNs) are all components of this technique. In order to train the model, we use an adaptive optimizer and binary cross entropy as the loss function. Key performance metrics such as accuracy, sensitivity, specificity, precision, F1 score, and area under the ROC curve (AUC-ROC) are used to compare the proposed method's performance to that of six established methods: Signal Pro Analyzer, Electro Cardio Suite, Bio Signal Master, Cardio Wave Analyzer, EKG Precision Pro, and Heart Stat Analyzer. The results suggest that the proposed technique is superior to the state-of-the-art in cardiac signal analysis across all criteria. The suggested technique not only requires less resources, but also trains and infers more quickly and uses less of them. 2024 2024 08 18 10.54216/JAIM.070101 https://www.americaspg.com/articleinfo/28/show/2373