Deep Neural Networks for Automated Detection of Arrhythmia and Coronary Artery Disease
Bibliographic record
Abstract
Medical services are under pressure to provide prompt access to accurate diagnostic techniques due to the significant number of cardiovascular disease-related deaths that occur globally. Clinical practitioners use electrocardiography (ECG) for cardiac abnormality detection. However, clinician interpretation leads to inconsistent results and delayed medical decisions. This paper introduces an Artificial Intelligence (AI) system which analyzes ECG data to identify arrhythmias and diagnose Coronary Artery Disease (CAD). A one-dimensional Convolutional Neural Network (CNN) was developed to identify arrhythmias while a fully connected network served for CAD prediction. The models reached 96.2 % accuracy in arrhythmia detection and 92 % accuracy in CAD binary prediction after training on multiple benchmark datasets with advanced preprocessing and augmentation methods. The models were integrated into a cloud-based E-hospital platform to provide real-time analysis, electronic medical record (EMR) connectivity, and remote access capabilities. The proposed system demonstrated that Deep Learning (DL) improves medical accuracy while decreasing physician workloads and making cardiac care available to more patients in areas with limited resources.
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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".