Heart Disease Classification using FusionNet to Extract SpatioTemporal Features from ECG Signal

An electrocardiogram (ECG) is a foundational tool for diagnosing cardiovascular diseases. However, the assessment of ECG data often depends on the subjective expertise of medical professionals, leading to challenges in accurately identifying subtle and ambiguous abnormalities. This study presents an advanced model that automates the classification of five specific cardiac diseases using AI-driven ECG analysis while validating its performance. Previous approaches have leveraged AI to classify time series data by converting these signals into image representations for analysis with convolutional neural networks (CNNs). The 1D ECG segment utilizes a bidirectional Long Short-Term Memory (bi-LSTM) network supplemented by an attention mechanism to enhance temporal information extraction. In parallel, the 2D ECG segment generates image representations through Gramian Angular Field (GAF), Recurrence Plot (RP), and Markov Transition Field (MTF) techniques. The late fusion strategy aggregates the maximum probabilities from the distinct classification outputs of the 1D and 2D analyses. Our framework has been rigorously evaluated on both 3-leads and 12-leads ECG datasets from the PTBXL database, instilling confidence in the robustness of our model. The proposed model demonstrates a significant performance improvement over state-of-the-art (SOTA) techniques, achieving enhancements in classification accuracy ranging from 0.8% to 12%. The source codes are available at: https://github.com/timeseriessignal/FusionNet