Hybrid CNN-BLSTM architecture for classification and detection of arrhythmia in ECG signals
Why this work is in the frame
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Bibliographic record
Abstract
This study introduces a robust and efficient hybrid deep learning framework that integrates Convolutional Neural Networks (CNN) with Bidirectional Long Short-Term Memory (BLSTM) networks for the automated detection and classification of cardiac arrhythmias from electrocardiogram (ECG) signals. The proposed architecture leverages the complementary strengths of both components: the CNN layers autonomously learn and extract salient morphological features from raw ECG waveforms, while the BLSTM layers effectively model the sequential and temporal dependencies inherent in ECG signals, thereby improving diagnostic accuracy. To further enhance training stability and non-linear representation capability, the Mish activation function is incorporated throughout the network. The model was trained and evaluated using a combination of the widely recognized MIT-BIH Arrhythmia Database and de-identified clinical ECG recordings sourced from collaborating healthcare institutions, ensuring both diversity and clinical relevance of the dataset. Notably, the framework operates with minimal preprocessing, underscoring its practical viability for real-time implementation. Experimental results demonstrate the model's exceptional performance, achieving an overall classification accuracy of 99.52%, sensitivity of 99.48%, and specificity of 99.85%. These outcomes highlight the model's robustness, generalizability, and strong potential for integration into clinical decision support systems, particularly in high-throughput or resource-constrained healthcare environments.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it