Project / 002
ECG Arrhythmia Classification & TinyML Deployment
A thesis project connecting rigorous ECG model evaluation with constrained on-device inference.
A CNN-based ECG arrhythmia classifier evaluated across patients and deployed for on-device inference on ESP32.
PROJECT / 002
PUBLIC RECORD · REV / 01
01The challenge
ECG classifiers can look convincing without proving that they generalize to patients the model has not seen. The second constraint was practical: carrying the trained model from a research environment onto an ESP32-class device.
02The approach
How the pieces connect
The work uses a CNN with an inter-patient evaluation framework, then converts the trained network with TensorFlow Lite for TinyML deployment. This keeps evaluation discipline and deployment constraints in the same research pipeline.
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03Build scope
Implemented capabilities
- BSc thesis research
- Inter-patient evaluation framework
- TensorFlow Lite conversion
- On-device TinyML inference
04The result
The thesis links model development, inter-patient testing, TensorFlow Lite conversion, and ESP32 inference instead of stopping at an offline accuracy result.
05Technology
- 01Python
- 02TensorFlow Lite
- 03CNN
- 04TinyML
- 05ESP32
06Related work
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