LABEL_DISCOVERY_AND_FIX_SUMMARY.md

🏷️ 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
• GitHub: ECG-FM Repository
• 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:

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

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

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