---
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:
1.  **Bandpass Filter:** 0.3–7 Hz (2nd-order Butterworth, zero-phase).
2.  **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](https://github.com/blublunalnal/afib_detection#project-1-revised-deepbeat)