Advanced Cardiac Monitoring via IoT: A CNN-TCN Hybrid Model for Accurate Clinical Decision Making
Bibliographic record
Abstract
Cardiovascular disease continues to be the primary global cause of mortality, yet existing remote monitoring solutions remain prohibitively expensive for widespread adoption.Addressing this critical gap, we present an affordable, real-time cardiac monitoring system that integrates biomedical sensing with cloud-based deep learning analytics.Our solution employs an AD8232 ECG module for capturing cardiac electrical activity alongside a SIM808 module for simultaneous GPS tracking, with data processed through a WeMos microcontroller and transmitted to a cloud database.For advanced ECG interpretation, we developed a novel hybrid CNN-TCN deep learning architecture that classifies heartbeats into five diagnostic categories: normal (N), supraventricular ectopic (S), ventricular ectopic (V), fusion (F), and unknown (Q) beats.This integrated hardware-software platform demonstrates three key innovations: (1) cost-effective real-time data acquisition, (2) robust cloud-based storage and accessibility, and (3) state-of-the-art arrhythmia detection through our optimized deep learning model.According to the results, the proposed method outperforms previous methods in cardiac rhythm classification, achieving competitive performance with an overall accuracy of 98.55%, sensitivity of 91.2%, and specificity of 99.4%.The combination of portable hardware with accurate algorithmic classification offers significant value for telemedicine applications and decentralized patient management, particularly in resource-constrained healthcare environments.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".