Classification of Cardiac Arrhythmias Based on Artificial Neural Networks and Continuous-in-Time Discrete-in-Amplitude Signal Flow
Bibliographic record
Abstract
Conventional Artificial Neural Networks (ANNs) for classification of cardiac arrhythmias are based on Nyquist sampled electrocardiogram (ECG) signals. The uniform sampling scheme introduces large redundancy in the ANN, which results high power and large silicon area. To address these issues, we propose to use continuous-in-time discrete-in-amplitude (CTDA) sampling scheme as the input of the network. The CTDA sampling scheme significantly reduces the sample points on the baseline part while provides more detail on useful features in the ECG signal. It is shown that the CTDA sampling scheme achieves significant savings on arithmetic operations in the ANN while maintains the similar performance as Nyquist sampling in the classification. The proposed method is evaluated by MIT-BIH arrhythmia database following AAMI recommended practice.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".