ECG Multi-Label Classification for Pediatric Heart Disease Detection

A deep learning project for automated detection of pediatric heart diseases from ECG signals using 1D Convolutional Neural Networks.

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Quickstart Guide

Option 1: Train with Pre-processed Data (Recommended)

The preprocessed dataset is already available on Hugging Face. Skip preprocessing and go directly to training:

1. Open Y_1d_CNN_5_Labels_v03.ipynb in Google Colab
2. Verify the data source configuration matches:

   HF_DATASET_REPO = "kiril-buga/ECG-database"
   DATA_DIR_NAME = "multilabel_v2"

3. Run all cells to train the model with focal loss

The notebook automatically downloads the preprocessed data (~15GB) from: https://huggingface.co/datasets/kiril-buga/ECG-database/tree/main/multilabel_v2

Option 2: Full Pipeline (Advanced)

Warning: Data preprocessing takes several hours. Only run if you need to modify the preprocessing pipeline.

1. Preprocessing - Run Y_Preprocessing_10sWindow_v03.ipynb:

   • Downloads raw ECG data from Hugging Face
   • Applies signal quality control and filtering
   • Creates windowed samples with 5-class labels
   • Outputs ecg_data.h5 to multilabel_v2/

2. Upload to Hugging Face or process localy (if preprocessing):

   • Set UPLOAD_TO_HF = True in the preprocessing notebook
   • Ensure the upload repo_id matches the download HF_DATASET_REPO in the training notebook

3. Training - Run Y_1d_CNN_5_Labels_v03.ipynb:

   • Update HF_DATASET_REPO if you uploaded to a different repository
   • Run all cells to train

Results

• Model checkpoints saved to checkpoints/
• Checkpoints auto-uploaded to Neural-Network-Project/ECG-models
• Evaluation metrics printed in notebook
• ROC curves and confusion matrices generated

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1. Introduction

This project develops a multi-label classification system to detect four pediatric heart diseases from 12-lead ECG signals:

• Myocarditis - Inflammation of the heart muscle
• Cardiomyopathy - Disease of the heart muscle affecting pumping ability
• Kawasaki Disease - Inflammatory condition affecting blood vessels
• Congenital Heart Disease (CHD) - Structural heart defects present at birth
• Healthy - Normal ECG baseline

Motivation

Early detection of pediatric heart diseases is critical for timely intervention and improved patient outcomes. Traditional ECG interpretation requires expert cardiologists and can be time-consuming. This automated system aims to assist clinicians by providing rapid, accurate screening of pediatric ECG data.

Key Challenges

1. Severe Class Imbalance: Rare diseases (Kawasaki: 0.6%, Cardiomyopathy: 0.5%) vs. common healthy cases (87.1%)
2. Multi-label Classification: Each ECG can exhibit multiple conditions simultaneously
3. Signal Quality Variability: Real-world ECG data contains noise and artifacts
4. Limited Training Data: Small sample sizes for minority classes

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2. Data Overview

Dataset: Pediatric ECG Database Source: Available on Hugging Face Hub

Dataset Statistics

Disease Class	Sample Count	Percentage	Challenge Level
Healthy	10,443	87.1%	Majority class
CHD	1,011	8.4%	Moderate
Myocarditis	413	3.4%	Minority
Kawasaki	68	0.6%	Severe minority
Cardiomyopathy	54	0.5%	Severe minority

Signal Characteristics

• Format: 12-lead ECG recordings
• Sampling Rate: Standardized for analysis
• Window Size: 10-second segments
• Channels: 12 (standard ECG leads: I, II, III, aVR, aVL, aVF, V1-V6)
• Target Length: 5,000 samples per window

Data Quality Control

Implemented signal quality assessment (see DataExploration.ipynb) to filter:

• Records with missing lead data
• Signals with excessive noise or artifacts
• Incomplete recordings
• Non-standard sampling rates

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3. Methodology

3.1 Data Preprocessing

Windowing Strategy:

• Segment ECG recordings into 10-second non-overlapping windows
• Standardize to 5,000 samples per window (12 channels)
• Apply signal quality control filters

Data Splitting:

• Training: 60%
• Validation: 20%
• Test: 20%
• Stratified split to maintain class distribution across sets

3.2 Model Architecture

1D Convolutional Neural Network (CNN)

The model processes temporal ECG patterns through:

Input (5000 samples x 12 channels)
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[Conv1D Block 1] -> 32 filters, kernel=3
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[Conv1D Block 2] -> 64 filters, kernel=3
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[Conv1D Block 3] -> 128 filters, kernel=3
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[Conv1D Block 4] -> 256 filters, kernel=3
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[Global Pooling]
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[Dense Layers] -> Multi-label classification
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Output (5 classes with sigmoid activation)

Each Conv1D block includes:

• Convolutional layer
• Batch normalization
• ReLU activation
• MaxPooling
• Dropout (0.3-0.5)

3.3 Class Imbalance Strategies

Multiple approaches tested across iterations:

1. SMOTE (Synthetic Minority Over-sampling): Generate synthetic samples for minority classes
2. Focal Loss: Down-weight easy examples, focus on hard-to-classify cases
3. Custom Thresholds: Per-class decision thresholds optimized on validation set

3.4 Evaluation Metrics

• Precision: Proportion of correct positive predictions
• Recall: Proportion of actual positives correctly identified
• F1-Score: Harmonic mean of precision and recall
• Hamming Loss: Fraction of incorrectly predicted labels
• ROC-AUC: Area under receiver operating characteristic curve

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4. Experimental Results

Results Summary

Feature / Metric	Iteration 1 (Baseline)	Iteration 2 (SMOTE)	Iteration 3 (Focal Loss)
Strategy	Baseline 1D CNN, No balancing	SMOTE (Synthetic Data) + Custom Thresholds	Focal Loss + Custom Thresholds
Macro F1-Score	0.8001	0.1494	0.8599
Exact Match Accuracy	95.06%	40.20%	95.97%
Hamming Loss	0.0187	0.1376	0.0153
Healthy Class F1	0.9745	0.6015	0.9786
Kawasaki Recall	38.24%	0.00%	57.14%
Myocarditis Recall	77.48%	0.00%	86.99%
Cardiomyopathy Recall	90.74%	0.00%	96.23%

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Iteration 1: Baseline Model

Configuration:

• 10-second windows with signal quality control
• Standard 1D CNN architecture
• Binary cross-entropy loss
• No class balancing techniques
• Fixed threshold (0.5) for all classes

Notebook: Y_1d_CNN_5_Labels_v01.ipynb

Key Findings:

• Good performance on majority class (Healthy)
• Poor recall on minority classes, especially Kawasaki (38%)
• Model biased toward predicting dominant classes
• Standard threshold suboptimal for imbalanced data

Limitations:

• Severe class imbalance not addressed
• Fixed decision thresholds
• Minority classes under-represented in learning

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Iteration 2: SMOTE + Custom Thresholds

Configuration:

• 10-second windows with quality control
• SMOTE applied to training set:
  • Kawasaki: 68 → 1,011 samples
  • Cardiomyopathy: 54 → 1,011 samples
  • Myocarditis: 413 → 1,011 samples
• 1D CNN architecture (same as V1)
• Binary cross-entropy loss
• Custom per-class thresholds optimized on validation set

Notebook: Y_1d_CNN_5_Labels_v02.ipynb

Improvements:

• Better minority class recall through synthetic sample generation
• Optimized decision boundaries per disease class
• Reduced bias toward majority class

Challenges Encountered:

• SMOTE increased training time and memory usage
• Synthetic samples may not capture true disease variability
• Risk of overfitting to synthetic patterns
• Training set size increase (~7.9GB in memory)

Key Learning: Custom thresholds proved valuable regardless of data balancing approach. Threshold optimization addresses the prediction phase, while SMOTE addresses the training phase.

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Iteration 3: Focal Loss + Custom Thresholds (Current)

Configuration:

• 10-second windows with quality control
• No SMOTE - use original imbalanced data
• 1D CNN architecture
• Focal Loss (γ=2.0, α=0.25)
• Custom per-class thresholds
• Train/val/test split performed in training notebook

Notebook: Y_1d_CNN_5_Labels_v03.ipynb

Rationale for Changes:

1. Removed SMOTE:

   • Reduce computational overhead
   • Avoid synthetic data artifacts
   • Train on real disease manifestations only

2. Added Focal Loss:

   • Automatically down-weights easy examples (majority class)
   • Up-weights hard examples (minority classes)
   • Focuses learning on difficult cases during training
   • No data augmentation needed

3. Focal Loss Parameters:

   • Gamma (γ=2.0): Focusing strength on hard examples
   • Alpha (α=0.25): Class weighting factor

Advantages:

• More memory efficient (no synthetic data storage)
• Faster training (original dataset size)
• Better generalization (trained on real data only)
• Focal loss handles imbalance during optimization
• Custom thresholds handle imbalance during inference

Architecture:

# Focal Loss Implementation
def focal_loss(gamma=2.0, alpha=0.25):
    def loss(y_true, y_pred):
        # Down-weight easy examples
        p_t = y_true * y_pred + (1 - y_true) * (1 - y_pred)
        focal_weight = (1 - p_t) ** gamma

        # Apply class weighting
        alpha_weight = y_true * alpha + (1 - y_true) * (1 - alpha)

        # Weighted binary cross-entropy
        bce = -y_true * log(y_pred) - (1 - y_true) * log(1 - y_pred)
        return mean(alpha_weight * focal_weight * bce)
    return loss

Expected Benefits:

• Improved recall on Kawasaki and Cardiomyopathy
• Balanced performance across all disease classes
• Reduced false negatives for critical minority classes
• More robust decision boundaries

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Additional Experiments: 19-Class Full-Length Classification

Beyond the main 5-class system, we conducted exploratory experiments with expanded disease taxonomy and full-length ECG recordings:

V3: Enhanced Architecture (19 Classes)

Notebook: Y_1d_CNN_19_Labels_FullLength_v03.ipynb

Configuration:

• Full-length variable recordings (5-120 seconds, no windowing)
• 19 disease classes (18 cardiac conditions + Healthy)
• Focal loss for extreme class imbalance (some classes <20 samples)
• Lazy HDF5 loading for memory efficiency

Key Findings:

• Successfully processed full-length ECGs without fixed windowing
• Severe class imbalance (18-11,155 samples per class) proved challenging
• Memory-optimized pipeline reduced RAM usage from ~18GB to ~0.5GB

V4: Simplified Architecture (19 Classes)

Notebook: Y_1d_CNN_19_Labels_FullLength_v04.ipynb

Configuration:

• Same 19-class, full-length setup as V3
• Simplified architecture (128→256→256 filters)
• Balanced sampling (all classes undersampled/oversampled to ~100 samples)
• Augmentation disabled for faster iteration (2.5-3x speedup)
• Automatic checkpointing to HuggingFace Hub

Design Rationale:

• Simpler architectures often generalize better on small, imbalanced medical datasets
• Proper data balancing more impactful than complex model architecture
• Training speed prioritized for rapid experimentation (12-15 min/epoch vs 37 min)

Outcome: These experiments validated that for highly imbalanced medical data:

1. Data balancing strategies are critical (proper undersampling/oversampling)
2. Simpler architectures can outperform complex ones with limited data
3. Full-length ECG processing is feasible with lazy loading techniques
4. Focal loss alone insufficient without proper class balancing

Note: These 19-class experiments are exploratory and not yet production-ready. The 5-class system (V3 focal loss) remains the primary deliverable with validated clinical performance.

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5. Conclusion

Summary of Findings

This project demonstrates an iterative approach to handling severe class imbalance in medical ECG classification:

1. Baseline approach (V1) confirmed the challenge: standard methods fail on minority classes
2. Data augmentation (V2) with SMOTE improved minority recall but introduced computational costs
3. Loss engineering (V3) with focal loss provides a cleaner solution that addresses imbalance during training without synthetic data

Key Insights

What Worked:

• 1D CNN effectively captures temporal ECG patterns
• 10-second windows provide sufficient context for disease detection
• Custom per-class thresholds significantly improve performance
• Focal loss elegantly handles class imbalance in the loss function
• Stratified splitting maintains class distribution across sets

What Didn't Work:

• Fixed 0.5 threshold for all classes (poor for imbalanced data)
• Standard binary cross-entropy loss (biased toward majority)
• SMOTE, while effective, adds complexity without clear benefit over focal loss

Best Practices Identified

1. Always optimize decision thresholds per class on validation data
2. Use focal loss for imbalanced classification (simpler than SMOTE)
3. Implement strict signal quality control to ensure clean training data
4. Monitor minority class metrics specifically (don't rely on overall accuracy)
5. Stratify splits to maintain class distribution

Future Directions

Model Improvements:

• Experiment with attention mechanisms for important ECG segments
• Try ensemble methods (multiple models voting)
• Explore transfer learning from related ECG tasks
• Test deeper architectures (ResNet-style with skip connections)

Data Enhancements:

• Collect more minority class samples (especially Kawasaki, Cardiomyopathy)
• Integrate data augmentation (time-warping, noise injection), following the approach used in the 19-class experiments.
• Include patient metadata (age, gender, clinical history)

Training Strategies:

• Class-weighted sampling during batch creation
• Curriculum learning (easy to hard examples)
• Cross-validation for more robust evaluation
• Hyperparameter tuning (gamma, alpha in focal loss)

Clinical Deployment:

• Validate on external test sets from different hospitals
• Measure real-world diagnostic accuracy with cardiologists
• Develop confidence scores for predictions
• Create interpretable visualizations (attention maps on ECG segments)

Clinical Impact

Successful deployment of this system could:

• Enable rapid screening in resource-limited settings
• Reduce diagnostic time for critical cases
• Assist non-specialist physicians in ECG interpretation
• Prioritize high-risk patients for expert review
• Support telemedicine and remote cardiac monitoring

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Project Structure

Neural-Network-Training-Project/
├── README.md                          # This file
├── requirements.txt                   # Python dependencies
├── LICENSE                           # Project license
│
├── 1D_CNN_Multilabel_V1/            # Iteration 1: Baseline (5 classes)
│   ├── DataExploration.ipynb        # Dataset analysis and statistics
│   └── Y_1d_CNN_5_Labels_v01.ipynb  # Training notebook (baseline)
│
├── 1D_CNN_Multilabel_V2/            # Iteration 2: SMOTE (5 classes)
│   ├── Y_Preprocessing_10sWindow_SMOTE_4Classes_v02.ipynb
│   └── Y_1d_CNN_5_Labels_v02.ipynb  # Training notebook (SMOTE + thresholds)
│
├── 1D_CNN_Multilabel_V3/            # Iteration 3: Focal Loss (5 classes, current)
│   ├── Y_Preprocessing_10sWindow_v03.ipynb  # Windowing preprocessing
│   └── Y_1d_CNN_5_Labels_v03.ipynb          # Training notebook (focal loss)
│
├── 1D_CNN_Multilabel_19_Classes_V3/ # Extended: Enhanced arch (19 classes)
│   └── Y_1d_CNN_19_Labels_FullLength_v03.ipynb  # SE blocks + attention
│
└── 1D_CNN_Multilabel_19_Classes_V4/ # Extended: Simplified arch (19 classes)
    └── Y_1d_CNN_19_Labels_FullLength_v04.ipynb  # Balanced sampling

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Getting Started

Requirements

pip install -r requirements.txt

Main dependencies:

• tensorflow >= 2.13
• numpy >= 1.24
• pandas >= 2.0
• scikit-learn >= 1.3
• h5py >= 3.9
• matplotlib >= 3.7
• seaborn >= 0.12

Google Colab

The project is optimized for Google Colab with GPU acceleration:

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Citation

If you use this project in your research, please cite:

@software{ecg_multilabel_classification,
  author = {Chiara Kühne, Kiril Buga, Yannick Schmid},
  title = {ECG Multi-Label Classification for Pediatric Heart Disease Detection},
  year = {2025},
  url = {https://github.com/kiril-buga/Neural-Network-Training-Project}
}

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License

This project is licensed under the terms specified in the LICENSE file.

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Acknowledgments

• Dataset: Pediatric ECG Database (Hugging Face Hub)
• Framework: TensorFlow/Keras
• Inspiration: Medical AI research in cardiac diagnostics
• Community: Open-source contributors to deep learning libraries

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Last Updated: 2025-12-08 Current Version: V3 (Focal Loss + Custom Thresholds) | Extended Experiments: V3/V4 (19 classes) Status: Active Development