# TabNet Deep Model for Tachycardia Detection (MIMIC-IV ECG)

This repository hosts a TabNet deep learning model trained to classify episodes of **tachycardia** based on ECG-derived measurements from the MIMIC-IV ECG dataset.

## 🔍 Problem
Tachycardia is defined here as a **RR interval < 600ms**, derived directly from the `rr_interval` column. The goal is to predict the presence of tachycardia using temporal, positional and axis-based ECG features.

## 📊 Dataset
- Original Source: [MIMIC-IV ECG (v1.0)](https://physionet.org/content/mimic-iv-ecg/1.0/)
- Processed via pandas and sklearn (see notebook).
- Train/Val/Test split:
  - **Train**: 560,003 samples
  - **Val**: 120,026 samples
  - **Test**: 120,006 samples
- Tachycardia distribution:  
  ~17% Positive class (imbalanced)

## 🧠 Model
- Architecture: [PyTorch TabNet](https://dreamquark-ai.github.io/tabnet/)
- Type: Deep Tabular Neural Network
- Framework: PyTorch
- Training details:
  - Early Stopping: 27 epochs
  - Best Validation Accuracy: **98.67%**
  - Final Test Accuracy: **99.0%**
  - Precision (tachycardia): **1.00**
  - Recall (tachycardia): **0.90**
  - F1 Score (tachycardia): **0.95**

## 🧪 Results
| Metric        | Value    |
|---------------|----------|
| Accuracy      | 99.0%    |
| F1 Score      | 0.95     |
| Precision     | 1.00     |
| Recall        | 0.90     |


## 📁 Files Included
- `tabnet_deep_model.zip` → Trained TabNet model
- `MIMIC_ECG_Tachycardia_Analysis.ipynb` → Full preprocessing + metrics

## 🛠 How to Use
```python
from pytorch_tabnet.tab_model import TabNetClassifier

model = TabNetClassifier()
model.load_model("tabnet_deep_model.zip")
preds = model.predict(X_test)
```

## ⚠️ License & Data Use
This model was trained on the MIMIC-IV ECG dataset, which requires credentialed access via PhysioNet. Please ensure compliance with [PhysioNet's data use policy](https://physionet.org/about/policies/).

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Trained and released by [Mic52](https://huggingface.co/Mic52).