Key result
Deep-learning model integrating CNNs, LSTM, and Mamba blocks achieves ~99% accuracy for ECG signal analysis.
Why the study?
To address the high computational complexity and inference inefficiencies of transformer-based models processing long sequences in ECG classification.
Does a deep-learning model integrating CNNs, LSTMs, and Mamba blocks improve ECG classification accuracy and computational efficiency?
Does a deep-learning model integrating CNNs, LSTMs, and Mamba blocks improve ECG classification accuracy and computational efficiency?
A novel deep-learning model integrating CNNs, LSTMs, and Mamba blocks provides highly accurate and computationally efficient ECG signal classification, offering potential for real-time cardiovascular monitoring.
Electrocardiogram (ECG) classification is crucial for the timely detection of cardiac arrhythmias and related heart conditions. This study presents an innovative deep‐learning model that integrates convolutional neural networks (CNNs), long short‐term memory (LSTM) units, and Mamba blocks with state‐space models (SSMs) to enhance the precision of ECG classification. This model addresses the limitations of transformer‐based approaches, particularly their high computational complexity and inefficiencies during the inference phase, exacerbated by their self‐attention mechanisms when processing long sequences. Evaluations on the MIT‐BIH dataset reveal that our model achieves outstanding classification accuracy, with a computational efficiency of 99.26%. Integrating the multibranch convolutional approach with Mamba blocks provides a robust and efficient framework for ECG signal analysis, offering a promising solution for real‐time cardiovascular monitoring and diagnosis.
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Najia et al. (2025) studied Cardiac arrhythmias. Deep-learning model integrating CNNs, LSTM units, and Mamba blocks with SSMs vs. Transformer-based approaches was evaluated on Classification accuracy. A deep-learning model integrating CNNs, LSTM units, and Mamba blocks achieved an outstanding classification accuracy of 99.26% on the MIT-BIH dataset for ECG signal analysis.
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