Inter-Patient CNN-LSTM for QRS Complex Detection in Noisy ECG Signals
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, a convolutional neural network (CNN) with long short-term memory (LSTM) is designed to detect QRS complexes in noisy electrocardiogram (ECG) signals. The CNN performs feature extraction while the LSTM determines the QRS complex timings. A multi-layer perception (MLP) after the LSTM is added to format the QRS complex detection predictions. With a unique data preparation procedure that includes proper design of training dataset, the proposed CNN-LSTM can achieve superior inter-patient testing performance, which means the testing and training datasets do not share any same patient ECG records. This generalization ability characteristic is critical to automated ECG analysis in an age of big data collected from noisy wearable ECG devices. The MIT-BIH and the European ST-T noise stress test databases are used to validate the effectiveness of the proposed algorithm in terms of sensitivity (recall), positive predictive value (precision), F1 score and timing root mean square error of R peak positions.
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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