⚡ ECGLight: Compute-Light Framework for Paper ECG Digitization & Myocardial Infarction Screening

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📌 Abstract & Key Highlights

ECGLight is an end-to-end, compute-light framework and interactive web workstation for converting paper/photographed 12-lead ECG records into high-fidelity 500 Hz digitized signals and performing automated screening for Myocardial Infarction (MI) and Occlusive MI (OMI) pathologies.

💡 Designed for Low-Resource & Remote Settings: ECGLight runs completely on CPU-only hardware in <30 seconds per ECG without requiring cloud connectivity or expensive GPU infrastructure, democratizing AI-based decision support for clinics worldwide.

✨ Key Features & Capabilities

• 📷 High-Fidelity Signal Digitization: Multi-stage computer vision pipeline combining sequential YOLOv11 object detection, scale calibration via Hough lines, K-Means grid construction, and an anti-leakage connected-component filter to extract clean 500 Hz 12-lead signals from smartphone photos or scans.
• ❤️ High-Accuracy MI & OMI Screening:
  • 95.51% Accuracy ($F_1 = 0.9519$) for MI detection on the benchmark PTB-XL dataset (21,799 ECGs).
  • 88.89% Accuracy ($F_1 = 0.8862$) for Occlusive MI (OMI) screening on the hospital-acquired ECG-Matrix dataset.
• ⚡ On-Device & Resource-Efficient: Fully functional on standard consumer laptop CPUs or CUDA GPUs.
• 🖥️ Interactive Web Dashboard Workstation: Streamlit workstation featuring real-time signal viewing, step-by-step digitization previews, R-peak beat segmentation, and downloadable diagnostic prediction tables.

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📰 News & Updates

• [2026/07] 📄 Paper released on arXiv: ECGLight: Compute-Light Framework For Paper ECG Digitization and Myocardial Infarction Screening.
• [2026/06] 🚀 Open-source release of the ECGLight web dashboard workstation and pre-trained weights repository.

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📌 Table of Contents

• 📌 Abstract & Key Highlights
• 📰 News & Updates
• 🖥️ Web Dashboard Workstation Overview
• 🚀 Installation & Setup
• 🧠 Pre-Trained Classifiers & Tasks
• 🚀 Command Line Usage
• 📁 Repository Structure & Directory Organization
• 📷 How It Works: Signal Digitization
• 📈 How It Works: Signal Analysis & Visualization
• ⚡ How It Works: Heartbeat Segmentation
• 🧠 How It Works: Cardiac Classification
• 🤝 Collaborating Institutions
• 👥 Authors & Contact
• 📄 Citation
• 📄 License

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🖥️ Web Dashboard Workstation Overview

The ECGLight workstation provides a responsive user interface designed for clinical workflow exploration, research, and education. It unifies the computer vision digitization and diagnostic classification models into a seamless local web application.

Workstation Modules

1. 📷 ECG Image Digitizer:

   • Upload scanned or photographed ECG images (.png, .jpg, .jpeg).
   • Execute the sequential YOLOv11 detection pipeline with real-time visual progress indicators.
   • Summarizes detected leads, calibrated sampling rates, and total extracted samples.
   • Automatically exports the digitized time-series CSV to output/digitization/latest_digitized.csv.

2. 📈 ECG Signal Viewer:

   • Interactive multi-channel ECG signal visualization using native Streamlit/Vega-Lite charts with zoom, pan, and hover tooltips.
   • Supports stacked subplots (with distinct clinical lead colorings) and multi-lead overlay modes.
   • Computes statistical summaries (mean, SD, min/max, voltage range) and row-level previewing.

3. ❤️ ECG Classification Engine:

   • Evaluates cardiac pathologies using pre-trained time-series ensemble and deep learning classifiers.
   • Automatically segments raw signals into heartbeats around R-peaks using the Pan-Tompkins algorithm.
   • Inference Mode (Unlabeled Data): Generates downloadable Predictions Tables with predicted class labels and probability confidence scores.
   • Evaluation Mode (Ground-Truth Labeled Data): Displays interactive evaluation metrics (Accuracy, $F_1$-Score, Sensitivity, Specificity, Confusion Matrix).

🔄 End-to-End Workflow

The pipeline operates in four coordinated phases: Digitization $ ightarrow$ Analysis $ ightarrow$ Segmentation $ ightarrow$ Classification. Architectural flowcharts for each phase are provided in the How It Works sections below.

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🚀 Installation & Setup

Prerequisites

• Python: 3.9+ (tested on Python 3.9 and 3.11)
• Hardware: Standard CPU (CUDA-capable GPU optional, automatically detected)

Step-by-Step Installation

1. Clone the Repository:

   git clone https://github.com/scai-lab/ECG-Digitization-Classification.git
   cd ECG-Digitization-Classification

2. Create and Activate Conda Environment:

   conda env create -f environment.yml
   conda activate infer

   [!IMPORTANT] Windows Compatibility & TensorFlow Setup: If running on Windows and encountering DLL loading errors (ImportError: DLL load failed while importing _pywrap_tensorflow_internal), install the pinned TensorFlow and Protobuf pairing:

   pip install tensorflow==2.15.0 protobuf==4.25.3

3. Download Pre-Trained Model Weights: The YOLOv11 detection checkpoints and pre-trained time-series classifiers are hosted on Polybox due to file size constraints:

   👉 Download Pre-Trained Models Directory (ETH Zürich Polybox)

   Extract the downloaded archive and place the models/ directory directly into the root of the project workspace:

   models/
   ├── digitization_models/
   │   ├── yolo11_full/weights/best.pt
   │   ├── yolo11_lead/weights/best.pt
   │   ├── yolo11_pulse/weights/best.pt
   │   └── yolo11_patch/weights/best.pt
   └── classifier_models/
       ├── mi_vs_normal_segmented/
       ├── omi_vs_nonomi/
       └── ecg_surgery/
   

4. Launch the Web Dashboard Workstation:

   streamlit run app.py

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🧠 Pre-Trained Classifiers & Tasks

The classification engine supports three distinct diagnostic tasks using the pre-trained weights in models/classifier_models/:

Diagnostic Task	Model Architecture	Expected Input Tensor Shape	Test Accuracy	Target Positive Class
Normal vs Myocardial Infarction (MI)	Arsenal Ensemble	12 leads $ imes$ 140 timesteps	92.3%	MYOCARDIAL_INFARCTION
Occlusive MI (OMI) vs Non-OMI	Rocket Classifier	12 leads $ imes$ 141 timesteps	88.9%	OMI
Pre-Procedural vs Post-Procedural MI	InceptionTime Deep Net	12 leads $ imes$ 140 timesteps	91.4%	pre-procedural MI

• Arsenal: An ensemble of ROCKET classifiers utilizing random convolutional kernels combined with ridge regression feature classification.
• Rocket: Random Omni-directional Kernel Extraction classifier computing high-dimensional time-series representations.
• InceptionTime: A deep 1D convolutional neural network ensemble leveraging multi-scale temporal kernel convolutions.

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🚀 Command Line Usage

Batch Signal Digitization (run_org.py)

Process structured directories of paper ECG scans in batch mode:

1. Configure dataset directory paths in run_org.py:

   ORGANIZED_DIR = "../ecg_files/ECG_organized_all"   # Input dataset root
   OUTPUT_DIR    = "../ecg_files/ECG_digitized"       # Destination for CSVs
   CATEGORIES    = ["pre", "index", "post"]           # Sub-directories

2. Run the batch digitization script:

   python run_org.py

Command-Line Model Inference (run_inference.py)

Run standalone inference on pre-digitized CSV datasets:

# 1. Normal vs MI Classification (Arsenal Model)
python archive/classification/run_inference.py --model mi_vs_normal_segmented --input data/ptb_xl/segmented_heartbeats.csv

# 2. Occlusive MI (OMI) vs Non-OMI Classification (Rocket Model)
python archive/classification/run_inference.py --model omi_vs_nonomi --input data/ecg_matrix_omi_segmented_50_150_90.csv

# 3. Custom Output Destination
python archive/classification/run_inference.py --model ecg_surgery --input data/ecg_surgery_segmented_50_150_70.csv --output results/surgery_preds.csv

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📁 Repository Structure & Directory Organization

.
├── app.py                          # Streamlit application main router
├── config.py                       # Centralized configuration and model registry
├── digitization.py                 # Core ECGImage extraction pipeline class
├── environment.yml                 # Conda environment dependency specification
├── LICENSE                         # Non-commercial academic license agreement
├── README.md                       # Repository documentation
│
├── backend/                        # Dashboard background execution adapters
│   ├── __init__.py                 # Package initializer
│   ├── digitization_runner.py      # YOLO loader and single-image processor
│   └── classification_runner.py    # Model loader and inference adapter
│
├── utils/                          # Streamlit front-end UI view components
│   ├── __init__.py                 # Package initializer
│   ├── branding.py                 # Header titles and institutional footer logos
│   ├── css.py                      # Custom clinical theme and styling utilities
│   ├── hardware.py                 # Hardware detector (CPU/GPU)
│   ├── page_digitizer.py           # ECG Digitizer page view
│   ├── page_csv_viewer.py          # Interactive Signal Viewer page view
│   └── page_classifier.py          # Classification workstation page view
│
├── models/                         # Relocated YOLO checkpoints and classifiers (external)
│   ├── digitization_models/        # YOLOv11 weights (full, lead, pulse, patch)
│   └── classifier_models/          # Pre-trained classifier weights (Arsenal, Rocket, InceptionTime)
│
└── archive/                        # Archived research, training, and CLI scripts (local)
    └── classification/             # Baseline training and evaluation scripts

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📷 How It Works: Signal Digitization

The core engine in digitization.py executes a multi-stage computer vision workflow to convert image pixels into calibrated voltage waveforms:

graph TD
    A[ECG Image Upload] --> B[Preprocessing: Otsu & Blurring]
    B --> C[YOLOv11 Detection & Segmentation]
    subgraph YOLOv11 Models
        C1[yolo11_full: Lead Boundaries]
        C2[yolo11_lead: Text Name Labels]
        C3[yolo11_pulse: Calibration Pulses]
        C4[yolo11_patch: Waveform Segments]
    end
    C --> C1 & C2 & C3 & C4
    C1 & C2 & C3 & C4 --> D[Hough Lines Calibration]
    D --> E[K-Means Row & Column Grid Construction]
    E --> F[Anti-Leakage Connected Components Filter]
    F --> G[Centroid Trace & Resampling to 500Hz]
    G --> H[Export latest_digitized.csv]

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1. Image Preprocessing: Cleans input scans using shadow removal, Otsu binarization, and Gaussian blurring to separate ink traces from paper texture.
2. YOLO Segmentation: Applies patched YOLO segmentation models across multiple crop scales (4×, 4.5×, 5×) to bound individual lead regions.
3. Multi-Model Object Detection: Runs YOLO detectors in parallel:
   • yolo11_full: Bounding boxes for the 12 lead channels.
   • yolo11_lead: Text label identification (I, II, aVR, V1-V6).
   • yolo11_pulse: Bounding boxes for 1 mV calibration reference pulses.
4. Scale Calibration: Fits Hough lines to calibration pulse boundaries to compute exact voltage scaling ($ ext{V}/ ext{px}$) and time scaling ($ ext{s}/ ext{px}$).
5. Grid Construction: Employs K-Means clustering on lead coordinates to construct regular grid layouts (3×4, 4×3, 6×2, 12×1) and resolve standard Cabrera lead ordering.
6. Contour Tracing & Anti-Leakage Filtering: Traces pixel centroids, baseline-corrects waveforms, and applies an anti-leakage connected-component filter per crop to isolate primary waveform traces from adjacent lead leakage. Signals are resampled to 500 Hz calibrated in mV.

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📈 How It Works: Signal Analysis & Visualization

graph TD
    A[Upload Digitized CSV] --> B[Parse Lead Voltages & Timestamps]
    B --> C[Vega-Lite Interactive Visualizer]
    C --> C1[Render Stacked Leads]
    C --> C2[Render Overlaid Signals]
    B --> D[Compute Signal Statistics: Mean, SD, Min/Max]
    D --> E[Display Summary Dataframes & Row Previews]

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1. Signal Parsing: Validates multi-lead CSV headers, timestamps, and sampling consistency.
2. Interactive Rendering: Native Vega-Lite charts enable client-side pan, zoom, and multi-channel hover tooltips.
3. Statistical Profiling: Calculates lead-level statistical metrics (mean, standard deviation, voltage range, min/max).

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⚡ How It Works: Heartbeat Segmentation

Prepares continuous 12-lead signals for classification models via Pan-Tompkins R-peak detection:

graph TD
    A[Digitized 500Hz Signal] --> B[Bandpass Filter 5-15Hz]
    B --> C[Derivative Filter]
    C --> D[Squaring Operation]
    D --> E[Moving Window Integration]
    E --> F[Adaptive Thresholding & R-Peak Search]
    F --> G[Extract 140-sample Beats: 50ms pre-R, 150ms post-R]
    G --> H[Max-Absolute Voltage Normalization]

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1. Bandpass Filtering: 5–15 Hz bandpass filter isolates QRS energy while suppressing muscle artifact and baseline wander.
2. Differentiation & Squaring: Highlights steep QRS slopes and attenuates P/T waves.
3. Moving Window Integration: Integrates energy across a 150 ms window to delineate QRS complexes.
4. Adaptive Thresholding: Dynamically searches for R-peak maxima.
5. Beat Windowing & Normalization: Extracts beat windows centered around R-peaks (50 ms pre-R, 150 ms post-R), normalizes max-absolute voltage, and formats output tensors (140/141 timesteps).

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🧠 How It Works: Cardiac Classification

graph TD
    A[Segmented Heartbeats] --> B[Numpy3D Reshaping: N_instances × 12_leads × N_timesteps]
    B --> C[Select Classification Task]
    subgraph Model Registry
        C1[Normal vs MI: Arsenal]
        C2[OMI vs non-OMI: Rocket]
        C3[Pre vs Post-Procedural MI: InceptionTime]
    end
    C --> C1 & C2 & C3
    C1 & C2 & C3 --> D[Load Pre-Trained Pickled Estimator]
    D --> E[Predict Class Labels & Probabilities]
    E --> F[Generate Downloadable Predictions CSV]

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1. Tensor Formatting: Formats heartbeat segments into 3D NumPy arrays (N_samples, 12_leads, N_timesteps).
2. Model Evaluation: Invokes the pre-trained pickled estimator for the selected diagnostic endpoint.
3. Report Generation: Formats class predictions, confidence probabilities, and diagnostic metrics for export.

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🤝 Collaborating Institutions

This research project was developed in multi-center academic and clinical collaboration:

• ETH Zürich
• Istituto Cardiocentro Ticino (EOC)
• Università della Svizzera italiana (USI)
• Università della Campania Luigi Vanvitelli

                          

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👥 Authors & Contact

• Shreyasvi Natraj — snatraj@ethz.ch
• Cyrus Achtari
• Felice Gragnano
• Andrea Milzi
• Marco Valgimigli
• Diego Paez-Granados

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📄 Citation

If you use ECGLight or its pre-trained models in your research, please cite our arXiv preprint:

@article{natraj2026ecglight,
  title={ECGLight: Compute-Light Framework For Paper ECG Digitization and Myocardial Infarction Screening},
  author={Natraj, Shreyasvi and Achtari, Cyrus and Gragnano, Felice and Milzi, Andrea and Valgimigli, Marco and Paez-Granados, Diego},
  journal={arXiv preprint arXiv:2607.07683},
  year={2026},
  url={https://arxiv.org/abs/2607.07683},
  doi={10.48550/arXiv.2607.07683}
}

APA Citation: Natraj, S., Achtari, C., Gragnano, F., Milzi, A., Valgimigli, M., & Paez-Granados, D. (2026). ECGLight: Compute-Light Framework For Paper ECG Digitization and Myocardial Infarction Screening. arXiv preprint arXiv:2607.07683. https://arxiv.org/abs/2607.07683

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📄 License

This repository and model weights are released under the Non-Commercial Academic and Research License Agreement. Please refer to the LICENSE file for full terms. Free for non-profit academic and research use. Commercial use is strictly prohibited.