A Classification Scheme for ECG Signals Based on Bidirectional LSTM Model
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Bibliographic record
Abstract
The application of ECG to diagnose cardiovascular diseases is a common method in clinical medicine, so the use of deep learning tools to achieve automatic analysis and classification of ECG has been a research direction for a wide range of researchers. This paper proposes a classification model for ECG signals based on a bidirectional LSTM model which is trained and tested using the dataset used for the PhysioNet 2017 computational cardiology challenge. The data are normalized and then processed by feature extraction. After passing a bidirectional LSTM layer, a fully connected layer, a softmax layer, and a classification layer in the model, and finally achieve the binary classification of normal signals and atrial fibrillation signals. In this process, the feature of bidirectional LSTM that can integrate contextual information is fully utilized. The experiments show that the classification accuracy of the model reaches 94.1%, demonstrating a good classification result.
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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 | 0.000 | 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