ECG Real-time Monitoring and Heart Anomaly Detection Reimagined
Bibliographic record
Abstract
The electrocardiogram (ECG) test is developed to monitor the functionality of the cardiovascular system. Nowadays, numerous attentions have been given to the accurate and early detection of heartbeat anomalies in real-time to prevent complications and take necessary measures. This paper proposes a robust real-time binary classification for ECG signals to detect possible anomalies. We implement an initial detection phase right where ECG data is collected through lightweight deep learning analysis. We evaluate the system on two widely used datasets, PTB and MIT-BIH datasets from PhysioNet. Our experiments suggest using artificial neural network (ANN) algorithms for their superior performance over other machine learning algorithms with an accuracy up to 99.3%. Furthermore, we implemented our system on a Raspberry Pi B+ representing an ECG patch to collect and process ECG signals and detect any abnormalities using the proposed ANN model. To create a scalable system, we stream the data in real-time using Apache Kafka and MQTT to keep records of patients’ ECG data and use it for further analysis to identify causes and support medical diagnosis. The system notifies healthcare providers when abnormalities are detected.
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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".