metadata
tags:
- atrial-fibrillation
- ppg
- pytorch
Model Overview
This model is designed for Atrial Fibrillation (AFib) detection and signal quality assessment from Photoplethysmography (PPG) waveforms.
Input Specifications
- Signal Type: PPG
- Sampling Rate: 32 Hz
- Input Shape:
(batch_size, 1, 800)
Required Preprocessing
To ensure optimal performance, input signals must be preprocessed using the following pipeline:
- Bandpass Filter: 0.3–7 Hz (2nd-order Butterworth, zero-phase).
- Normalization: Min-max normalization to scale the signal between [0, 1].
Model Outputs
The model provides three primary outputs:
| Output | Description |
|---|---|
afib_prob |
Raw Probabilities: |
afib_pred |
Classification: Binary prediction (1: Atrial Fibrillation, 0: Normal Sinus Rhythm). Thresholds have been calibrated based on quality assessment to optimize detection sensitivity and specificity. |
qa_pred |
Quality Assessment: Signal reliability score (2: Good, 1: Acceptable, 0: Poor). |
Details
Detailed training, architecture diagrams, and the preprocessing pipeline are available in the repository