Inter-Patient CNN-LSTM for QRS Complex Detection in Noisy ECG Signals
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".