Automatic recognition of arrhythmia using a CNN-based broad learning system
Bibliographic record
Abstract
An accurate electrocardiogram (ECG) beat classification can benefit the diagnosis of the cardiovascular disease. Currently, automatic ECG classification systems based on deep neural network are useful in the field of ECG recognition. However, most of them are time-consuming in training, less robustness to noise, and need to retrain the entire model when new data are added. To address these problems, we propose an ECG classification algorithm by using a CNN-based broad learning system (CNNBLS) for recognition of arrhythmia. We performed two experiments to evaluate the robustness and incremental learning features of the proposed classification system. In noise robustness experiment, we selected five types of original and denoising abnormal ECGs in the MIT-BIH arrhythmia database, and overall accuracy of the five arrhythmia classifications achieved 98.5% and 98%. In incremental learning experiment, we selected 6 types of abnormal ECGs data in the MIT-BIH arrhythmia database. The accuracy and training time before incremental learning were 97.94% and 21.61 s, and the accuracy and training time after incremental learning with additional 12929 new data were 98.45% and 47.23 s. Experimental results show that our model is a practical ECG recognition method with suitable noise robustness and has superiority in training time while the accuracy is guaranteed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".