[Feature]: Develop End-to-End ECG Classification & Alert Pipeline

[gaurav12301010]

So, what is it about?

The current workflow handles model training and evaluation in isolation, with no integrated real-time inference or user interface.
Implement an end-to-end pipeline where incoming ECG signals are pre-processed, passed through the trained model, and used for live arrhythmia detection and alerting.

Technical Notes:

Build a unified inference pipeline connecting signal acquisition → pre-processing → model inference → output handling.

Load the best saved model automatically from checkpoints for real-time predictions.

Integrate the existing pre-processing module to ensure consistent data formatting.

Implement logic to track 3–4 consecutive abnormal predictions and trigger a notification or warning event.

Use a lightweight interface framework such as Streamlit to visualize input signals, predicted classes, and alerts in real time.

Expected Outcome:

Fully automated signal-to-prediction workflow.

Dynamic loading of the best available model.

Real-time notification on persistent arrhythmia patterns.

User-friendly monitoring dashboard for live signal and prediction visualization.

Code of Conduct

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