Technical Detail

Training and Evaluation Methodology

This page summarizes the implemented training pipeline, model architecture strategy, and deployment setup used for the ECG arrhythmia classification system.

Data and Label Construction

  • Dataset source: PTB-XL public ECG corpus with 12-lead waveforms and metadata.
  • SCP code aggregation transforms raw statements into five diagnostic superclasses.
  • Signals are loaded at configured sampling rates and standardized per training workflow.
  • Metadata features include age, sex, height, weight, infarction staging, and pacemaker status.

Modeling Strategy

  • Model01: metadata-only dense network for baseline comparisons.
  • Model02: dual-input architecture (metadata branch + Conv1D ECG branch).
  • Model03: augmentation-enhanced variant using windowing and additive noise.
  • All models are trained as multi-label classifiers with sigmoid outputs.

Pipeline Components

01

Preprocessing

Metadata engineering, target creation, standardized scaling, and split preparation.

02

Training

TensorFlow/Keras training with early stopping and model checkpoint tracking.

03

Evaluation

Held-out set performance measurement using shared post-processing logic.

04

Deployment

Streamlit inference interface with embedded web presentation for review sessions.

Reproducible Training Commands

Representative commands from the project workflow:

# prepare and train all models
python train.py --data-path /path/to/ptbxl --model all

# train the best-performing architecture
python train.py --data-path /path/to/ptbxl --model model02

# evaluate trained checkpoints
python evaluate.py --model all

Use Boundary

This platform is presented as a computational cardiology research demonstrator.

It does not replace clinical interpretation, formal diagnosis, or physician-led decision workflows.