A Deep Neuro-Fuzzy Method for ECG Big Data Analysis via Exploring Multimodal Feature Fusion

In the realm of medical data processing, particularly in the diagnosis and monitoring of cardiac diseases, the analysis of electrocardiogram (ECG) signals represents a critical challenge, especially with the burgeoning volume of ECG Big Data. Traditional methods and existing research often fall short in effectively analyzing this data, limited by their inability to fully capture the complex and nonlinear patterns inherent in ECG signals. Addressing these limitations, in this article, we introduce a novel deep neuro-fuzzy model augmented with multimodal feature fusion. Our method ingeniously combines the power of neuro-fuzzy systems with the robust feature extraction capabilities of deep learning, specifically leveraging a Transformer-based architecture, to analyze both ECG signals and their corresponding spectral images. This multimodal fusion not only enriches the model's input data, providing a comprehensive understanding of cardiac signals, but also enhances the adaptability and accuracy of cardiac arrhythmia detection. We rigorously validate our approach on the MIT-BIH arrhythmia database, conducting a series of experiments, including performance evaluations and ablation studies, to highlight the significant contributions of the multimodal feature fusion and neuro-fuzzy module. The results achieve significant improvements in classification metrics: an accuracy of 98.46% and an F1 score of 99.1%. Moreover, we benchmark the Transformer's feature extraction performance against other architectures, such as ResNet. The results unequivocally demonstrate our model's superiority and illustrate the potential of integrated neuro-fuzzy and deep learning approaches in overcoming the current limitations of ECG signal analysis.