Arrhythmia Classification Using Noise Filtering and 1D CNN
Bibliographic record
Abstract
Identifying arrhythmias in electrocardiogram (ECG) data is critical for diagnosing and managing heart disorders.However, various types of noise in ECG data frequently pose a challenge to proper arrhythmia classification.To overcome this challenge, this study suggests a three-step process to make it more accurate to classify arrhythmias as normal (N), supraventricular (S), ventricular (V), fusion (F), or unknown (Q).In the first step of the three-step process, we add Gaussian noise to the MIT-BIH ECG data to make the classification model more reliable.Second, ECG signals are notch-filtered to eliminate noise artifacts and preserve cardiac information after Gaussian noise injection.Retaining important cardiac information while reducing noise distortion.Third, the 1D CNN receives denoised ECG data for arrhythmia classification.Five-class arrhythmias can be used to examine the ECG signals, according to the results of the suggested modeling.With a 1% error rate, the 1D-CNN-based classification system can identify N, S, V, F, and Q with 99%, 86%, 96%, 80%, and 99% accuracy.The results suggest that the three-step ECG arrhythmia categorization method improves diagnostic accuracy, enabling early treatment by healthcare experts.Its real-world applications improve cardiovascular diagnosis and patient outcomes.
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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".