Medical AIAdvancedSignal classificationData leakage1D CNNECG

ECG Arrhythmia: Beat-by-Beat Classification

A 1D CNN with RR-interval features labels every heartbeat as normal, supraventricular, ventricular or fusion (AAMI classes) on the MIT-BIH Arrhythmia Database, with the inter-patient split, so no patient is in both training and test. The demo finds the beats in an ECG strip and labels each one.

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ECG arrhythmia kit demo: a 30-second ECG strip with a predicted class letter above every heartbeat and the cardiologist's label below

Our honest results

Macro-F1 - 22 unseen patients (49,661 beats)	0.498
Ventricular beats found (sensitivity)	0.959
Supraventricular beats found (sensitivity)	0.150
Same model, random beat split: macro-F1	0.963

Same network, two test sets: 0.96 on beats of people it has seen and 0.50 on new people. The guide explains the gap and why most of the missed S beats come from one patient. Trains in 14 s on an RTX 5070 Laptop GPU and in 3 min on its CPU.

What's included

Readable source codeSmall, commented modules you can explain line by line in your viva.
Demo appA local web app to show your examiners - runs offline on your laptop.
Trained model + reference resultsSo you can compare your own runs fairly.
Learning guide with viva Q&ABackground, design choices explained, limitations and extension ideas.
One-click setup & testsWindows setup script and tests that prove it works on your PC.
Dataset download scriptFetches the public data from its original source, with the citation.

Dataset credit

Moody G.B., Mark R.G. (2005). MIT-BIH Arrhythmia Database (version 1.0.0). PhysioNet. doi:10.13026/C2F305 - Open Data Commons Attribution License v1.0 (opendatacommons.org/licenses/by/1-0). Also: Moody G.B., Mark R.G. (2001). The impact of the MIT-BIH Arrhythmia Database. IEEE Eng Med Biol 20(3):45-50.

Academic integrity

We sell code and teaching, not finished projects. You must understand, extend and write up the work yourself, acknowledge the kit and cite the dataset. No ready-made reports.

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