Key result
Novel deep learning model achieves ~98% accuracy in ECG arrhythmia classification.
Why the study?
Traditional ECG classification methods have limitations in feature extraction precision and discrimination capability for arrhythmia classification.
Does a deep learning AMH–BiLSTM model based on 16-dimensional Gaussian mixture model waveform features improve ECG arrhythmia heartbeat classification accuracy in recordings from the MIT–BIH Arrhythmia Database?
Does a deep learning AMH–BiLSTM model based on 16-dimensional Gaussian mixture model waveform features improve ECG arrhythmia heartbeat classification accuracy in recordings from the MIT–BIH Arrhythmia Database?
Combining visibility graph-based waveform segmentation and Gaussian mixture model feature extraction with an attention-based BiLSTM deep learning network achieves high accuracy (97.93%) and reproducibility in automated ECG arrhythmia classification.
Caution against immediate clinical adoption; extends benchmark performance of Gaussian-BiLSTM models on MIT-BIH data.
Electrocardiography (ECG) is a vital tool for diagnosing and screening cardiovascular diseases. Accurately extracting ECG signal features is key to improving the accuracy of arrhythmia classification. To address the limitations of traditional methods in terms of feature extraction precision and discrimination capability, this study proposes an ECG signal classification method based on Gaussian features. First, the R-peak is identified via the visibility graph algorithm, and its prominence is computed to locate the onset and offset of the P, Q, R, S, and T waves across the heartbeat waveform. Five Gaussian components are then fitted to the segmented signal, yielding 16 highly discriminative features for classification. Furthermore, an AMH–BiLSTM architecture is developed by integrating convolutional neural networks, bidirectional long short-term memory, and multi-head attention mechanisms, with two fully connected layers performing the final classification task. To address class imbalance in the MIT–BIH Arrhythmia Database, the SMOTE algorithm is employed to oversample minority classes. Moreover, over 10 independent random seeds, the proposed method achieves a mean classification accuracy of 97.93%, precision of 98.10%, recall of 97.92%, and F1-score of 97.99%, with a mean AUC of 99.90%. Compared to the baseline model under the same experimental setup, our proposed method has shown some improvements. In addition, we also provide reference comparisons with recent representative methods, and our method demonstrates competitive classification performance under comparable evaluation protocols.
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Li et al. (2026) studied Cardiac arrhythmia (n=48). AMH-BiLSTM model with Gaussian features vs. Baseline and representative deep learning models was evaluated on Mean ECG heartbeat classification accuracy. The AMH-BiLSTM deep learning model using Gaussian ECG features achieved a mean classification accuracy of 97.93% and an F1-score of 97.99% on the MIT-BIH Arrhythmia Database.
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