CardioGuard: AI-driven ECG authentication hybrid neural network for predictive health monitoring in telehealth systems
Bibliographic record
Abstract
The increasing integration of telehealth systems underscores the importance of robust and secure methods for patient data management. Traditional authentication methods, such as passwords and PINs, are prone to breaches, underscoring the need for more secure alternatives. Therefore, there is a need for alternative approaches that provide enhanced security and user convenience. Biometric-based authentication systems uses individuals unique physical or behavioral characteristics for identification, have emerged as a promising solution. Specifically, Electrocardiogram (ECG) signals have gained attention among various biometric modalities due to their uniqueness, stability, and non-invasiveness. This paper presents CardioGaurd, a deep learning-based authentication system that leverages ECG signals-unique, stable, and non-invasive biometric markers. The proposed system uses a hybrid Convolution and Long short-term memory based model to obtain rich characteristics from the ECG signal and classify it as authentic or fake. CardioGaurd not only ensures secure access but also serves as a predictive tool by analyzing ECG patterns that could indicate early signs of cardiovascular abnormalities. This dual functionality enhances patient security and contributes to AI-driven disease prevention and early detection. Our results demonstrate that CardioGaurd offers superior performance in both security and potential predictive health insights compared to traditional models, thus supporting a shift towards more proactive and personalized telehealth solutions.
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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) | 0.000 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".