{"cells": [{"cell_type": "markdown", "metadata": {}, "source": "# PhysioNet - Digitization of ECG Images: ECG images (scanned/photographed paper printouts) to Time series data (12-lead ECG signals) using computer-vision signal-extraction\n\n**Dataset:** physionet-ecg-images\n**Generated by:** Alexandria Research Assistant\n**Date:** 2025-10-28\n\n---\n\nThis notebook was automatically generated by Alexandria with comprehensive research data.\n"}, {"cell_type": "markdown", "metadata": {}, "source": "## \ud83d\udcda Research Background & Literature Review\n\n**PhysioNet 2024 - Digitization of ECG Images: Recent Papers, SOTA Techniques, and Approaches**  \n*Task: Convert ECG images (scans/photos of 12-lead ECG printouts) into digital time series using computer vision signal-extraction.*\n\n---\n\n## Top Recent Papers (2023\u20132025) and Resources\n\n| Title & Link | Description | Relevance |\n|---|---|---|\n| **[Combining Hough Transform and Deep Learning Approaches to Reconstruct ECG Signals From Printouts (2024)](https://arxiv.org/abs/2410.14185)**<br>GitHub: [ECG-Digitiser](https://github.com/felixkrones/ECG-Digitiser) | SOTA PhysioNet 2024-winning approach. Deep segmentation (nnU-Net) + classical Hough transform for grid and curve extraction. Robust to artifacts; pretrained models available. | Directly addresses the exact challenge. End-to-end code for multi-lead ECG signal extraction from images. |\n| **[ECG-Image-Database: A Dataset of ECG Images with Real-World Imaging and Scanning Artifacts (2024)](https://arxiv.org/abs/2409.16612)** | Synthetic and real-world ECG image dataset (from PTB-XL, Emory) with paired ground truth. Contains artificial and physical artifacts, relevant for robust model development. | Foundational open dataset for training/benchmarking digitization models under realistic conditions. |\n| **Kaggle Baselines and Community Solutions**<br>[ECG Original Explained Baseline](https://www.kaggle.com/code/ambrosm/ecg-original-explained-baseline) | Practical, explainable baselines for the competition. Code for image correction, segmentation, and signal extraction. | Reproducible, interpretable starting points for custom model design. |\n| **PhysioNet Competition Discussions & Notebooks**<br>[V2 Notebook](https://www.kaggle.com/code/taylorsamarel/v2-physionet-digitization-of-ecg-images) | Community posts, code, and model ablations analyzing feature extraction, augmentation, and error modes. | Contains empirical insight into pitfalls, artifact handling, and feature engineering. |\n\n---\n\n## Key State-of-the-Art Techniques\n\n### 1. **Hybrid Classical + Deep Learning Pipelines**\n- **Segmentation (Deep Learning):**\n  - *nnU-Net* for robust multi-lead and grid segmentation. Pretrained on multi-vendor, artifact-rich datasets[1].\n  - Encoder-decoder U-Nets, transformer-based segmenters, or self-supervised vision models for poor-quality input and unseen layouts[1][2].\n- **Signal Path Extraction (Classical Vision):**\n  - *Hough Transform* (standard and probabilistic) to identify grid lines and axes[1].\n  - *Curve Extraction* via skeletonization or active contour models to trace ECG traces post-segmentation.\n\n### 2. **Synthetic Data and Robust Augmentation**\n- Generation of synthetic ECG printouts with controlled *geometry*, *contrast*, and *artifact* variation using ECG-Image-Kit[2][7].\n- Physical simulation: introducing stains, folds, lighting changes, noise, and even mold, then re-scanning for realism[2].\n- Ensures that models can generalize to the myriad of real-world conditions found in legacy, vendor-varied scans.\n\n### 3. **Domain-Specific Preprocessing**\n- **Perspective Correction**: Affine and homography transforms to counteract print/scan skew, variable alignment, and lens distortion (common in photographs).\n- **Grid Removal and Standardization**: Explicit identification and removal or compensation for ECG grid artifacts using morphological and frequency-domain filtering.\n- **Lead Localization**: Per-image detection of lead positions and bounding boxes to account for vendor (layout) variability[1][2].\n- **Dynamic Range and Contrast Normalization**: Histogram matching and local adaptive thresholding to compensate for inconsistent lighting and ink degradation.\n\n### 4. **Multi-Lead and Multi-Scale Signal Extraction**\n- **Lead Synchronization & Alignment**: Detection and mapping of each lead\u2019s time axis, adjusting for asynchrony/overlap in vendor layouts.\n- **Resolution-Adaptive Sampling**: Estimation of time axis scaling directly from image features\u2014or using deep-learning regression if grid detection fails\u2014ensuring consistent output even with variable printout resolutions and scan qualities.\n\n### 5. **Post-processing and Time Series Assembly**\n- Signal dewarping to remove perspective and scanning artifacts.\n- Denoising using signal processing filters (median, Savitzky-Golay, wavelet denoise) on extracted curve data.\n- Stitching and temporal alignment of partial lead segments into continuous signals[1].\n\n---\n\n## Competition-Specific Methods and Feature Engineering\n\n- **End-to-End Segmentation Models**: Custom-trained nnU-Net or similar architectures, with strong augmentations, for precise curve segmentation on 12-lead layouts[1].\n- **Geometry-Informed Losses**: Penalizing overlap/false splitting of adjacent (multi-lead) traces, enforcing smoothness and temporal consistency[1][2].\n- **Simulated Artifacts in Training**: Use of the ECG-Image-Database and synthetic artifact pipelines for better out-of-distribution robustness[2][7].\n- **Automatic Vendor/Layout Recognition**: Featurization (either via classifier or unsupervised clustering on layout metadata) to apply specialized extraction pipelines per vendor style[1][2].\n- **Uncertainty Quantification**: Bootstrapped extraction or MC dropout to quantify and suppress spurious predictions in unclear regions.\n\n---\n\n## References (as requested with links):\n\n1. **Krones et al. (2024). Combining Hough Transform and Deep Learning Approaches to Reconstruct ECG Signals From Printouts.** [arXiv:2410.14185][1] \u00b7 [GitHub (ECG-Digitiser)][1]\n2. **Reyna et al. (2024). ECG-Image-Database: A Dataset of ECG Images with Real-World Imaging and Scanning Artifacts.** [arXiv:2409.16612][2]  \n3. **Kaggle Baselines:** [ECG Original explained baseline][6], [V2 PhysioNet Notebook][3], [Competition Page][8]\n\n---\n\n### Practical Takeaways\n\n- Use **hybrid approaches** (classical + deep learning) for maximum robustness.\n- Train with **synthetic + real, artifact-rich datasets**, such as ECG-Image-Database.\n- Employ **layout-specific** pipelines: explicit grid, axis, lead detection, and adaptive scaling.\n- Deep models alone are brittle against novel artifacts; combine with classical knowledge-driven filters and pre/post-processing.\n\n---\n\n#### If you need code templates, reproducible pipelines, or further SOTA references, see the [ECG-Digitiser repository][1] and [ECG-Image-Database][2] for ready-to-use data, models, and augmentation scripts.\n\n[1]: https://github.com/felixkrones/ECG-Digitiser\n[2]: https://arxiv.org/abs/2409.16612\n[3]: https://www.kaggle.com/code/taylorsamarel/v2-physionet-digitization-of-ecg-images\n[6]: https://www.kaggle.com/code/ambrosm/ecg-original-explained-baseline\n[8]: https://www.kaggle.com/competitions/physionet-ecg-image-digitization"}, {"cell_type": "markdown", "metadata": {}, "source": "## \ud83d\udca1 Research Gaps & Opportunities\n\nCurrent approaches for digitizing ECG images into 12-lead time-series data\u2014such as those from the 2024 PhysioNet Challenge\u2014have advanced the field but still exhibit significant limitations and present multiple unexplored research opportunities. The following analysis identifies present gaps, challenges, and prospective directions relevant to this computer-vision signal-extraction task.\n\n---\n\n## 1. Current Limitations in Existing Approaches\n\n- **Sensitivity to Image Artifacts**  \n  Many leading solutions, including the winning ECG-Digitiser (which combines Hough Transform for grid/line detection with deep learning for segmentation), still struggle with severe artifacts such as strong rotations, motion blur, stains, wrinkles, poor lighting, and perspective distortion. Artifacts can break grid detection, cause missed or spurious segments, and impede accurate time coordinate mapping, especially where physical degradation intersects with low-contrast waveforms[1][2].\n  \n- **Dependence on Synthetic Data and Low-Diversity Training**  \n  While data such as the ECG-Image-Database offers unprecedented realism from programmatic and physical distortions, most models are still trained largely on synthetic or limited real-world examples, potentially missing rare or complex combinations of artifacts, and suffering from overfitting on certain layouts[2].\n  \n- **Vendor- and Site-specific Layout Variations**  \n  Many extraction pipelines struggle with idiosyncratic vendor markings, variable grid scales, missing or nonstandard calibration signals, and differences in label/lead ordering and axis scaling\u2014particularly problematic in global health contexts where device heterogeneity is high[1][5][7].\n  \n- **Temporal and Spatial De-synchronization**  \n  Alignment between leads, especially when leads are printed with non-synchronous offsets or cut/separated in the image by folds or occlusion, remains a problem for multi-lead signal assembly[1][3].\n  \n- **Loss of Fidelity in Amplitude and Timing**  \n  Minor inaccuracies in pixel-to-mV or pixel-to-ms conversion (due to geometric distortions, variable DPI, non-rectangular grids, etc.) can cause significant signal degradation\u2014potentially masking diagnostic features like arrhythmia onset times, subtle ST changes, or low-amplitude events[2].\n  \n- **Inadequate Handling of Variable Sampling Frequencies and Grid Parameters**  \n  Current tools often assume fixed or easily estimated grid sizes/frequencies, but may fail when these are missing, inconsistent, or non-orthogonal, further limiting generalizability[1][2].\n\n---\n\n## 2. Unexplored Research Directions in Computer Vision\n\n- **Self-Supervised Pretraining on Document Images**  \n  Pretraining models on large corpora of non-ECG but related document and chart images (e.g., finance, oscilloscope, scientific plots, handwritten graphs) may yield representations more robust to out-of-domain distortions prior to ECG-specific fine-tuning.\n\n- **Graph Neural Networks on Extracted Curve Skeletons**  \n  Using GNNs to model multi-lead structures where both topology (e.g., bifurcations, overlaps) and graph spatial features (curve adjacency, continuity) are encoded, enabling better handling of lead crossing, interruptions, or occlusions.\n\n- **End-to-End Vision Transformers (ViTs) for Multi-Lead Coordination**  \n  Applying Transformer architectures capable of global attention over the entire printed page could aid in simultaneous extraction, lead disambiguation, and layout understanding versus treating leads independently.\n\n- **Domain Adaptation for Device-Specific Layouts**  \n  Few-shot adaptation, domain adversarial training, or meta-learning approaches could tailor extraction pipelines to unseen or rare device formats and global vendor variants using minimal annotations.\n\n---\n\n## 3. Opportunities for Improvement Specific to Signal-Extraction\n\n- **Robust Grid and Waveform Decoupling under Heavy Artifacts**  \n  Separating the graphical ECG grid from signal traces even when grid lines overlap signals closely or grid visibility is partially lost, possibly via multi-stage segmentation or attention-driven refinement.\n\n- **Dynamic Time Warping (DTW) and Shape-Matching for Trace Correction**  \n  Using DTW, optimal path finding, or curve-regularization routines during vectorization to align noisy extracted paths with plausible ECG morphologies and medical priors, correcting for small extraction errors.\n\n- **Uncertainty Quantification and Quality Control**  \n  Integrating uncertainty-aware extraction pipelines that flag dubious segments, signal interruptions, or suspect calibration\u2014enabling automated or semi-automated human review.\n\n- **Real-Time or On-Device Extraction**  \n  Optimizing models for speed and efficiency (e.g., pruned or quantized CNNs) to permit rapid digitization in healthcare environments with limited compute resources.\n\n---\n\n## 4. Novel Techniques Applicable to Key Challenges\n\n| Challenge                                   | Novel Technique                        | Description                                                                                              |\n|---------------------------------------------|----------------------------------------|----------------------------------------------------------------------------------------------------------|\n| Severe Artifacts / Distortion               | Diffusion Models for Inpainting        | Use diffusion or GAN-based inpainting to restore obscured or missing signal regions, improving recovery. |\n| Layout Variability                          | Layout-aware Multi-task Networks       | Simultaneous extraction of grid, lead boxes, text, and signals with strong spatial priors.               |\n| Lead Synchronization and Overlap            | Spatiotemporal Multi-lead Fusion       | Combine per-lead extraction with global context, leveraging inter-lead relationships for timing recovery. |\n| Amplitude/Time Conversion                   | Bayesian Calibration Estimation        | Model calibration parameters as probabilistic variables to propagate uncertainty in mm/mV or ms mapping.  |\n| Variable Sampling                           | Adaptive Interpolation/Super-Resolution| Employ learning-based interpolation to harmonize sampling rates across leads and grid sizes.              |\n| OCR and Metadata Extraction                 | Large Language Model (LLM) OCR Fusion  | Pair text (labels, calibration) extraction with image features to improve overall context.                |\n\n---\n\n### Additional Research Gaps\n\n- **Multi-modal Learning**: Jointly leveraging text (labels, calibration marks) and signal traces for improved lead identification.\n- **Human-in-the-Loop Correction**: Tools for rapid manual correction of extraction failures, feeding back corrections to the model for active learning.\n- **Standardization**: Construction of robust, widely-accepted benchmarks and error metrics that reflect clinical (not just pixelwise) accuracy, to evaluate downstream diagnostic impact[2][3][7].\n\n---\n\nImproving ECG signal digitization from images is an active field, with substantial room for advances in robustness, generalizability, and clinical fidelity\u2014particularly by leveraging advances in computer vision, probabilistic modeling, and human-computer interaction."}, {"cell_type": "markdown", "metadata": {}, "source": "## \ud83d\udcca Dataset Information\n\nThe **PhysioNet - Digitization of ECG Images** initiative on Kaggle provides the most relevant datasets for developing and benchmarking computer-vision solutions converting **ECG images (paper printouts and photographs) into time-series ECG signals**. Here\u2019s a detailed analysis of the core datasets and others suitable for **signal-extraction, transfer learning, and data augmentation** in this domain:\n\n---\n\n## 1. PhysioNet - Digitization of ECG Images (Competition Dataset)\n\n**Kaggle ID:** physionet/physionet-ecg-image-digitization  \n**Competition Link:** [PhysioNet - Digitization of ECG Images](https://www.kaggle.com/competitions/physionet-ecg-image-digitization)[8]\n\n### Characteristics\n- **Content:** 12-lead ECG images (scanned or photographed printouts) paired with ground truth time-series signals.\n- **Scale:** Thousands of images, reflecting real-world artifacts (noise, wrinkles, stains, angle shifts) and a variety of acquisition qualities[2][7].\n- **Formats:** \n    - **Images:** PNG, varied resolution/scanning conditions\n    - **Signals:** CSV or similar, with standardized time-series tabular data[5].\n- **Label Quality:** High; signals programmatically generated from PTB-XL and Emory Healthcare sources, ensuring exact alignment between image and timeseries[2][7].\n\n### Access\n- Requires Kaggle login and competition rules acceptance.\n- Data can be accessed via Kaggle's API or web download[5][8].\n- **Direct Kaggle Dataset Link:** [physionet-ecg-image-digitization/data](https://www.kaggle.com/competitions/physionet-ecg-image-digitization/data)[5]\n\n---\n\n## 2. ECG-Image-Database: Synthetic ECG Image Dataset\n\n**Not a Kaggle Dataset**, but *core source dataset* for the above competition, also cited in related repositories and papers[2][7].\n\n### Characteristics\n- **Content:** 35,595 ECG images with *paired ground-truth time series*, made with simulated real-world printing/scanning/photographing artifacts[2].\n- **Image Sources:** Raw signals from PTB-XL (977 ECGs) and Emory Healthcare (1,000 ECGs)\n- **Artifacts:** Covers digital distortions (e.g., noise, stains) and physical artifacts (e.g., paper folds, mold)[2].\n- **Use Case:** Ideal for developing and validating algorithms robust to real-world image degradations.\n\n### Access\n- Typically accessed via PhysioNet, not directly via Kaggle, but used to compose the primary Kaggle competition dataset[2][5].  \n- Documentation and scripts: [ECG-Image-Database Paper](https://arxiv.org/abs/2409.16612)[2]\n\n---\n\n## 3. PTB-XL: Large Public 12\u2011Lead ECG Database\n\n**Kaggle ID:** philipperemy/ptb-xl-ecg-dataset  \n[PTB-XL Dataset on Kaggle](https://www.kaggle.com/datasets/philipperemy/ptb-xl-ecg-dataset)\n\n### Characteristics\n- **Content:** >20,000 12-lead ECG recordings as time-series, not images.\n- **Purpose:** Upstream *source data* for ECG image generators (i.e., ECG-Image-Kit).\n- **Formats:** WFDB, CSV (signals).\n\n### Application\n- Use to synthesize large numbers of *paired* image-signal samples for **transfer learning** or **data augmentation** by generating images with toolkits like **ECG-Image-Kit** (open source)[1][2][7].\n\n---\n\n## 4. Synthetic and Related Datasets for Transfer/Augmentation\n\n| Dataset                                                              | Kaggle ID (if available)             | Characteristics                       | Best Use                               |\n|----------------------------------------------------------------------|--------------------------------------|---------------------------------------|----------------------------------------|\n| **PhysioNet - Digitization of ECG Images**                           | physionet/physionet-ecg-image-digitization | Real-world, paired images/signals      | Direct signal extraction/benchmarking  |\n| **ECG-Image-Database**                                               | \u2013                                    | Synthetic + real artifact images, paired signals | Data augmentation, robustness testing  |\n| **PTB-XL large ECG dataset**                                         | philipperemy/ptb-xl-ecg-dataset      | Only time-series, for synthetic image gen | Transfer learning/data synthesis       |\n| **Chapman-Shaoxing ECG Dataset**                                     | icefireq/heartecg                    | Large time-series, multilead           | Synthetic image generation, diversity  |\n\n---\n\n## 5. Data Availability & Access Methods\n\n- **Kaggle Data API:** Use `kaggle competitions download -c physionet-ecg-image-digitization` for the competition dataset[5][8].\n- **PTB-XL:** `kaggle datasets download -d philipperemy/ptb-xl-ecg-dataset`\n- **Chapman-Shaoxing:** `kaggle datasets download -d icefireq/heartecg`\n- **ECG-Image-Database:** Accessible via [arXiv:2409.16612](https://arxiv.org/abs/2409.16612)[2] or PhysioNet.\n\n---\n\n## 6. Data for Transfer Learning/Augmentation\n\n- *Transfer learning* can use the **PhysioNet competition dataset** or generate additional synthetic data from base signal datasets (PTB-XL, Chapman-Shaoxing) using ECG-Image-Kit[1][2][7].\n- *Data augmentation* is feasible via synthetic noise/distortions, simulated artifacts (using findings from ECG-Image-Database and community scripts).\n\n---\n\n## 7. Additional Notebooks and Baselines\n\n- **Baselines:**  \n  - [taylorsamarel/v2-physionet-digitization-of-ecg-images](https://www.kaggle.com/code/taylorsamarel/v2-physionet-digitization-of-ecg-images)[3]\n  - [ambrosm/ecg-original-explained-baseline](https://www.kaggle.com/code/ambrosm/ecg-original-explained-baseline)[6]\n- These provide starter code and reproducible pipelines for signal extraction benchmarking.\n\n---\n\n### **Summary Table: Key Relevant Kaggle Datasets for ECG Image Digitization**\n\n| Dataset Name                                    | Kaggle ID                                   | Size/Format               | Quality/Notes              | Access              |\n|-------------------------------------------------|---------------------------------------------|--------------------------|----------------------------|---------------------|\n| PhysioNet - Digitization of ECG Images          | competitions/physionet-ecg-image-digitization | PNG, CSV, ~30k samples   | High; real/simulated images, signals | Competition data tab |\n| PTB-XL ECG Dataset                              | philipperemy/ptb-xl-ecg-dataset             | WFDB, CSV, 21k+ signals  | Source for synth images    | Public Kaggle Datasets |\n| Chapman-Shaoxing ECG Dataset                    | icefireq/heartecg                           | CSV                      | Multi-lead, diverse        | Public Kaggle Datasets |\n\n---\n\n**Strongest recommendation**:  \n- Use [physionet/physionet-ecg-image-digitization](https://www.kaggle.com/competitions/physionet-ecg-image-digitization)[5] and [PTB-XL](https://www.kaggle.com/datasets/philipperemy/ptb-xl-ecg-dataset) as your primary and augmentation sources.  \n- Leverage the recent [ECG-Image-Database (arXiv:2409.16612)](https://arxiv.org/abs/2409.16612)[2] for robust, artifact-rich, paired data.\n\nYou can integrate additional synthetic images using ECG-Image-Kit on PTB-XL or other ECG datasets to expand your training set for transfer or robustification purposes[1][2][7]."}, {"cell_type": "markdown", "metadata": {}, "source": "## \u2699\ufe0f Implementation Strategy\n\nTo digitize ECG images into multichannel time series, a robust, modular pipeline must address both the computer vision (CV) challenges of artifact-laden medical images and the domain specifics of multilead ECG signal structure. Below is a detailed implementation strategy incorporating established best practices and state-of-the-art solutions, grounded in PhysioNet 2024 Challenge insights and recent open research.\n\n---\n\n## 1. Concrete Code Approach & Overall Architecture\n\n**Recommended high-level pipeline:**\n\n1. **Preprocessing**: Clean, deskew, and normalize ECG images.\n2. **Segmentation**: Isolate each ECG waveform region (per lead).\n3. **Coordinate Mapping**: Precisely identify axes and gridlines for coordinate transformation.\n4. **Signal Trace Extraction**: For each segmented lead, convert the pixel trace into a 1D signal.\n5. **Postprocessing & Signal Harmonization**: Align, resample, and scale all leads to recover the 12-channel time series.\n\nThis closely matches the [ECG-Digitiser](https://github.com/felixkrones/ECG-Digitiser) state-of-the-art pipeline, which combines deep learning-based segmentation (nnU-Net) with geometric postprocessing (e.g., Hough Transform) for signal extraction[1].\n\n---\n\n### Example Structure (Python pseudo-code)\n\n```python\ndef digitize_ecg_image(image_path, model_path):\n    image = preprocess_image(image_path)\n    mask = segment_waveforms(image, model_path)\n    axes_info = detect_axes_and_grid(image)\n    lead_traces = extract_lead_traces(image, mask, axes_info)\n    signals = postprocess_and_resample(lead_traces, axes_info)\n    return signals  # shape: (12, N)\n```\n\n---\n\n## 2. Data Preprocessing Pipeline\n\n**Key Steps:**\n\n- **Denoising & Contrast Enhancement**: Adaptive histogram equalization or CLAHE.\n- **Rotation & Perspective Correction**: Use Hough Transform for grid line detection, then apply affine/perspective transformation[1][7].\n- **Cropping & Lead ROI Detection**: Automatic (deep-learning) segmentation to isolate leads and remove annotations[1].\n- **Artifact Removal**: Morphological operations for removing grid overdraw and print noise[2].\n\n**Example:**  \n```python\nimport cv2\n# 1. Read and enhance\nimg = cv2.imread('ecg.jpg', cv2.IMREAD_GRAYSCALE)\nimg = cv2.equalizeHist(img)\n\n# 2. Detect and correct orientation\nedges = cv2.Canny(img, 50, 150)\nlines = cv2.HoughLines(edges, 1, np.pi/180, 200)\n# ...determine rotation, apply cv2.warpAffine\n\n# 3. Mask non-ECG regions (using segmentation mask)\n# Apply mask to img\n```\n\n**Training Data Augmentation:**  \nInclude synthetic noise, variable brightness, rotations, stains, and grid variations, mimicking the *ECG-Image-Database* provided for the challenge[2][7].\n\n---\n\n## 3. Model Architecture Recommendations\n\n**Best results as of 2024:**\n\n- **Segmentation**: **nnU-Net** (deep encoder-decoder U-Net variant) for semantic segmentation of ECG lanes[1].\n    - Input: Preprocessed RGB or grayscale ECG image\n    - Output: Segmentation mask per ECG lead\n\n- **Postprocessing**:\n    - **Hough Transform** for grid/axes detection and trace alignment[1].\n    - **Classical Signal Extraction**: Skeletonization + pixel-to-signal mapping along time axis.\n    - Consider post-hoc ML regression for de-digitization refinement.\n\n**Alternatives:**\n- Lead-specific signal tracing with classical CV (e.g., OpenCV + connected component analysis), but deep learning segmentation is now state-of-the-art.\n\n**Block Diagram:**\n\n| Stage              | Method              | Rationale                                      |\n|--------------------|---------------------|------------------------------------------------|\n| Preprocessing      | OpenCV (classical)  | Robust, fast for artifact correction           |\n| Segmentation       | nnU-Net (PyTorch)   | Superior performance for biomedical images     |\n| Postprocessing     | Hough, Morphology   | Precise geometric correction and grid mapping  |\n| Trace Extraction   | Skeletonization     | Converts mask to 1D (per-lead) signal series   |\n\n---\n\n## 4. Training Strategy & Hyperparameters\n\n### nnU-Net Segmentation\n\n- **Data**: Augment with rotations, scale, intensity noise, and grid artifacts per ECG-Image-Database[2][7].\n- **Loss**: Combo of cross-entropy and Dice loss to balance mask accuracy.\n- **Optimizer**: AdamW or SGD.\n- **Learning Rate**: Start \\( 1 \\times 10^{-3} \\), cosine decay.\n- **Batch Size**: Max possible per GPU memory (e.g., 4\u201316).\n- **Epochs**: 100\u2013200, early stopping on validation mask IoU.\n- **Validation split**: 10%\u201320% images with diverse artifact combinations.\n\n### Signal Extraction Postprocessing\n\n- No training required, but validate parameter selection (e.g., for thresholding, grid calibration) on a held-out subset.\n\n---\n\n## 5. Evaluation Metrics\n\n**Primary metrics (from PhysioNet Challenge and literature):**\n\n- **Mean Absolute Error (MAE) per Lead**: Mean samplewise error between predicted and reference time series, averaged over all 12 leads and normalized by dynamic range[1][8].\n- **Correlation Coefficient**: Median or mean Pearson correlation between predicted and ground truth signals per lead[8].\n- **Multilead Synchrony**: Inter-lead correlation/consistency, to ensure simultaneous alignment.\n- **Lead Detection Rate**: % of leads correctly segmented and extracted from input images[1].\n\n**Additional:**\n\n- **Visual Inspection**: Overlay of true/predicted signals for spot checks on benchmarks with severe artifacts.\n- **Robustness Curves**: Metric performance across subsets stratified by artifact severity or vendor-specific layouts[2].\n\n---\n\n## Resources & Open Code\n\n- [ECG-Digitiser (1st place, 2024)](https://github.com/felixkrones/ECG-Digitiser) \u2014 full code, pretrained weights, pipeline examples, and synthetic data scripts[1].\n- [ECG-Image-Database](https://arxiv.org/abs/2409.16612) \u2014 diverse, annotated dataset for training and artifact robustness[2].\n- Kaggle Notebooks with starter code, baseline pipelines, and testing artifacts[3][4].\n\n---\n\n## References to Cited Approaches\n\n- *ECG-Digitiser* combines classical geometric transforms and deep learning to robustly handle mixed artifacts and lead arrangements[1].\n- *ECG-Image-Database* provides a foundational, diverse dataset for real-world generalization and robust model development[2].\n- Public Kaggle kernels supply baseline implementations and helpful code snippets for pipeline modules[3][4].\n\n---\n\n**Summary:**  \nAdopt a hybrid pipeline with deep-learning segmentation (nnU-Net), robust preprocessing, and geometric postprocessing for signal reconstruction. Validate with leadwise MAE and correlation; use heavily augmented, artifact-rich datasets for robustness and real-world performance[1][2][8]."}, {"cell_type": "markdown", "metadata": {}, "source": "## 1. Setup & Imports\n\nInstall and import required libraries."}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": "import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\nimport torch\nimport torch.nn as nn\nfrom torch.utils.data import Dataset, DataLoader\nimport torchvision.transforms as transforms\n\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import accuracy_score, classification_report\n\nimport warnings\nwarnings.filterwarnings('ignore')\n\n# Set random seeds\nnp.random.seed(42)\ntorch.manual_seed(42)\n\ndevice = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\nprint(f'Using device: {device}')"}, {"cell_type": "markdown", "metadata": {}, "source": "## 2. Load Dataset\n\nLoading dataset: **physionet-ecg-images**\n\nCompetition: `physionet-ecg-image-digitization`"}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": "from pathlib import Path\nimport pandas as pd\nimport os\n\n# Setup\nDATA_PATH = Path(f'/kaggle/input/physionet-ecg-image-digitization')\nprint(f'\ud83d\udcc1 Data path: {DATA_PATH}')\nprint(f'\ud83d\udcc1 Path exists: {DATA_PATH.exists()}')\n\n# List files\nif DATA_PATH.exists():\n    all_files = list(DATA_PATH.glob('**/*'))\n    print(f'\\n\ud83d\udcca Found {len(all_files)} total files/folders')\n\n    # Filter parquet files\n    parquet_files = [file for file in all_files if file.suffix == '.parquet']\n    print(f'\\n\ud83d\udcca Found {len(parquet_files)} parquet files')\n\n    # Load parquet files\n    for file in parquet_files:\n        print(f'\\nLoading {file.name}...')\n        try:\n            df = pd.read_parquet(file)\n            print(f\"\ud83d\udcca Shape: {df.shape}\")\n            print(f\"\ud83d\udcca Columns: {df.columns.tolist()}\")\n            print(f\"\ud83d\udcca Sample Data:\\n{df.head()}\")\n        except Exception as e:\n            print(f\"Error loading {file.name}: {e}\")\n\n    # Handle train/test splits\n    train_files = [file for file in parquet_files if 'train' in file.name]\n    test_files = [file for file in parquet_files if 'test' in file.name]\n\n    if train_files:\n        print(\"\\nLoading train data...\")\n        train_dfs = []\n        for file in train_files:\n            try:\n                df = pd.read_parquet(file)\n                train_dfs.append(df)\n            except Exception as e:\n                print(f\"Error loading {file.name}: {e}\")\n        if train_dfs:\n            train_df = pd.concat(train_dfs, ignore_index=True)\n            print(f\"\ud83d\udcca Train Shape: {train_df.shape}\")\n            print(f\"\ud83d\udcca Train Columns: {train_df.columns.tolist()}\")\n            print(f\"\ud83d\udcca Train Sample Data:\\n{train_df.head()}\")\n\n    if test_files:\n        print(\"\\nLoading test data...\")\n        test_dfs = []\n        for file in test_files:\n            try:\n                df = pd.read_parquet(file)\n                test_dfs.append(df)\n            except Exception as e:\n                print(f\"Error loading {file.name}: {e}\")\n        if test_dfs:\n            test_df = pd.concat(test_dfs, ignore_index=True)\n            print(f\"\ud83d\udcca Test Shape: {test_df.shape}\")\n            print(f\"\ud83d\udcca Test Columns: {test_df.columns.tolist()}\")\n            print(f\"\ud83d\udcca Test Sample Data:\\n{test_df.head()}\")\nelse:\n    print(f'\u274c Data path does not exist')"}, {"cell_type": "markdown", "metadata": {}, "source": "## 3. Exploratory Data Analysis\n\n**Analyzing the competition data structure**"}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": "# Exploratory Data Analysis\ntry:\n    print('\ud83d\udd27 === EXPLORATORY DATA ANALYSIS ===\\n')\n\n    # Check if train_df and test_df exist\n    if 'train_df' not in globals() or 'test_df' not in globals():\n        raise ValueError(\"train_df and/or test_df not found. Please load data first.\")\n\n    # Basic info\n    print(\"\ud83d\udcca Train Data Info:\")\n    print(train_df.info())\n    print(\"\\n\ud83d\udcca Test Data Info:\")\n    print(test_df.info())\n\n    # Check for missing values\n    print(\"\\n\ud83d\udcca Train Missing Values:\")\n    print(train_df.isnull().sum())\n    print(\"\\n\ud83d\udcca Test Missing Values:\")\n    print(test_df.isnull().sum())\n\n    # Unique values in key columns\n    print(\"\\n\ud83d\udcca Unique Values in Train:\")\n    for col in train_df.columns:\n        print(f\"{col}: {train_df[col].nunique()} unique values\")\n    print(\"\\n\ud83d\udcca Unique Values in Test:\")\n    for col in test_df.columns:\n        print(f\"{col}: {test_df[col].nunique()} unique values\")\n\n    # Sample distributions\n    print(\"\\n\ud83d\udcca Train Data Describe:\")\n    print(train_df.describe())\n    print(\"\\n\ud83d\udcca Test Data Describe:\")\n    print(test_df.describe())\n\n    # Visualize distributions for numeric columns\n    import matplotlib.pyplot as plt\n    import seaborn as sns\n\n    numeric_cols = train_df.select_dtypes(include=['float64', 'int64']).columns\n    if len(numeric_cols) > 0:\n        print(\"\\n\ud83d\udcca Plotting numeric distributions...\")\n        for col in numeric_cols:\n            plt.figure(figsize=(8, 4))\n            sns.histplot(train_df[col], kde=True, color='blue', label='Train')\n            sns.histplot(test_df[col], kde=True, color='orange', label='Test')\n            plt.title(f'Distribution of {col}')\n            plt.legend()\n            plt.show()\n\n    # Visualize categorical distributions\n    cat_cols = train_df.select_dtypes(include=['object', 'category']).columns\n    if len(cat_cols) > 0:\n        print(\"\\n\ud83d\udcca Plotting categorical distributions...\")\n        for col in cat_cols:\n            plt.figure(figsize=(8, 4))\n            sns.countplot(data=train_df, x=col, color='blue', label='Train')\n            sns.countplot(data=test_df, x=col, color='orange', label='Test')\n            plt.title(f'Distribution of {col}')\n            plt.legend()\n            plt.xticks(rotation=45)\n            plt.show()\n\n    # Check for class imbalance (if applicable)\n    if 'label' in train_df.columns:\n        print(\"\\n\ud83d\udcca Label Distribution in Train:\")\n        print(train_df['label'].value_counts(normalize=True))\n        plt.figure(figsize=(8, 4))\n        sns.countplot(data=train_df, x='label')\n        plt.title('Label Distribution in Train')\n        plt.show()\n\n    if 'label' in test_df.columns:\n        print(\"\\n\ud83d\udcca Label Distribution in Test:\")\n        print(test_df['label'].value_counts(normalize=True))\n        plt.figure(figsize=(8, 4))\n        sns.countplot(data=test_df, x='label')\n        plt.title('Label Distribution in Test')\n        plt.show()\n\n    # Check for duplicate samples\n    print(\"\\n\ud83d\udcca Duplicate Rows in Train:\", train_df.duplicated().sum())\n    print(\"\ud83d\udcca Duplicate Rows in Test:\", test_df.duplicated().sum())\n\n    # Check for data leakage (if applicable)\n    if 'patient_id' in train_df.columns and 'patient_id' in test_df.columns:\n        train_patients = set(train_df['patient_id'].unique())\n        test_patients = set(test_df['patient_id'].unique())\n        overlap = train_patients.intersection(test_patients)\n        print(\"\\n\ud83d\udcca Overlapping Patient IDs between Train and Test:\", len(overlap))\n\n    # Print summary\n    print(\"\\n\u2705 Exploratory Data Analysis complete!\")\n\nexcept Exception as e:\n    print(f'\u2717 Error in Exploratory Data Analysis: {e}')\n    import traceback\n    traceback.print_exc()"}, {"cell_type": "markdown", "metadata": {}, "source": "## 4. Data Preprocessing\n\n**Competition:** physionet-ecg-image-digitization\n\n**Note:** Following research-based implementation strategy"}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": "# Data Preprocessing\ntry:\n    print('\ud83d\udd27 === DATA PREPROCESSING ===\\n')\n\n    # 1. Load and inspect data\n    print('\ud83d\udcc2 Loading data from:', DATA_PATH)\n    image_paths = list(pathlib.Path(DATA_PATH).glob('**/*.png')) + list(pathlib.Path(DATA_PATH).glob('**/*.jpg'))\n    print(f'Found {len(image_paths)} ECG images')\n\n    # 2. Visualize a sample image\n    sample_img_path = str(image_paths[0])\n    sample_img = plt.imread(sample_img_path)\n    plt.figure(figsize=(10, 6))\n    plt.imshow(sample_img, cmap='gray')\n    plt.title('Sample ECG Image (Before Preprocessing)')\n    plt.axis('off')\n    plt.show()\n\n    # 3. Define preprocessing transforms (adjust as needed for your data)\n    preprocess_transform = torchvision.transforms.Compose([\n        torchvision.transforms.ToPILImage(),\n        torchvision.transforms.Grayscale(num_output_channels=1),\n        torchvision.transforms.Resize((512, 512)),  # Adjust size based on EDA\n        torchvision.transforms.ToTensor(),\n        torchvision.transforms.Normalize(mean=[0.5], std=[0.5])  # Standardize\n    ])\n\n    # 4. Preprocess a sample image and visualize\n    sample_tensor = preprocess_transform(sample_img)\n    plt.figure(figsize=(10, 6))\n    plt.imshow(sample_tensor.permute(1, 2, 0), cmap='gray')\n    plt.title('Sample ECG Image (After Preprocessing)')\n    plt.axis('off')\n    plt.show()\n\n    # 5. Check for corrupted images\n    corrupted = []\n    for img_path in image_paths:\n        try:\n            img = plt.imread(str(img_path))\n            _ = preprocess_transform(img)\n        except Exception as e:\n            corrupted.append(str(img_path))\n    if corrupted:\n        print(f'\u26a0\ufe0f Found {len(corrupted)} corrupted images. Example:', corrupted[0])\n    else:\n        print('\u2705 No corrupted images detected.')\n\n    # 6. Print summary\n    print(f'\\n\ud83d\udcca Total images: {len(image_paths)}')\n    print(f'\ud83d\udcca Corrupted images: {len(corrupted)}')\n    print('\u2705 Data Preprocessing complete!')\n\nexcept Exception as e:\n    print(f'\u2717 Error in Data Preprocessing: {e}')\n    import traceback\n    traceback.print_exc()"}, {"cell_type": "markdown", "metadata": {}, "source": "## 5. Model Architecture\n\n**Task:** signal-extraction\n\n**Approach:** Based on research and implementation strategy above"}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": "# Model Architecture\ntry:\n    print('\ud83d\udd27 === MODEL ARCHITECTURE ===\\n')\n\n    import torch.nn as nn\n    import torch.nn.functional as F\n    from torchvision.models import resnet18\n    from torchvision.models.resnet import ResNet18_Weights\n\n    # --- 1. Define the Segmentation Model (U-Net style) ---\n    class DoubleConv(nn.Module):\n        def __init__(self, in_channels, out_channels):\n            super().__init__()\n            self.double_conv = nn.Sequential(\n                nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1),\n                nn.BatchNorm2d(out_channels),\n                nn.ReLU(inplace=True),\n                nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),\n                nn.BatchNorm2d(out_channels),\n                nn.ReLU(inplace=True)\n            )\n        def forward(self, x):\n            return self.double_conv(x)\n\n    class Down(nn.Module):\n        def __init__(self, in_channels, out_channels):\n            super().__init__()\n            self.maxpool_conv = nn.Sequential(\n                nn.MaxPool2d(2),\n                DoubleConv(in_channels, out_channels)\n            )\n        def forward(self, x):\n            return self.maxpool_conv(x)\n\n    class Up(nn.Module):\n        def __init__(self, in_channels, out_channels):\n            super().__init__()\n            self.up = nn.ConvTranspose2d(in_channels, in_channels // 2, kernel_size=2, stride=2)\n            self.conv = DoubleConv(in_channels, out_channels)\n        def forward(self, x1, x2):\n            x1 = self.up(x1)\n            diffY = x2.size()[2] - x1.size()[2]\n            diffX = x2.size()[3] - x1.size()[3]\n            x1 = F.pad(x1, [diffX // 2, diffX - diffX // 2, diffY // 2, diffY - diffY // 2])\n            x = torch.cat([x2, x1], dim=1)\n            return self.conv(x)\n\n    class OutConv(nn.Module):\n        def __init__(self, in_channels, out_channels):\n            super().__init__()\n            self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=1)\n        def forward(self, x):\n            return self.conv(x)\n\n    class UNet(nn.Module):\n        def __init__(self, n_channels=1, n_classes=1):\n            super().__init__()\n            self.inc = DoubleConv(n_channels, 64)\n            self.down1 = Down(64, 128)\n            self.down2 = Down(128, 256)\n            self.down3 = Down(256, 512)\n            self.down4 = Down(512, 1024)\n            self.up1 = Up(1024, 512)\n            self.up2 = Up(512, 256)\n            self.up3 = Up(256, 128)\n            self.up4 = Up(128, 64)\n            self.outc = OutConv(64, n_classes)\n        def forward(self, x):\n            x1 = self.inc(x)\n            x2 = self.down1(x1)\n            x3 = self.down2(x2)\n            x4 = self.down3(x3)\n            x5 = self.down4(x4)\n            x = self.up1(x5, x4)\n            x = self.up2(x, x3)\n            x = self.up3(x, x2)\n            x = self.up4(x, x1)\n            logits = self.outc(x)\n            return logits\n\n    # --- 2. Define the Coordinate Mapping Model (ResNet-based) ---\n    class CoordNet(nn.Module):\n        def __init__(self):\n            super().__init__()\n            self.resnet = resnet18(weights=ResNet18_Weights.DEFAULT)\n            self.resnet.conv1 = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3, bias=False)\n            self.resnet.fc = nn.Linear(512, 8)  # Predict 4 corners (x,y) each\n        def forward(self, x):\n            return self.resnet(x)\n\n    # --- 3. Define the Signal Extraction Model (1D CNN + MLP) ---\n    class SignalNet(nn.Module):\n        def __init__(self, input_length=512, output_length=4000, n_leads=12):\n            super().__init__()\n            self.conv1 = nn.Conv1d(1, 32, kernel_size=7, padding=3)\n            self.conv2 = nn.Conv1d(32, 64, kernel_size=5, padding=2)\n            self.conv3 = nn.Conv1d(64, 128, kernel_size=3, padding=1)\n            self.pool = nn.MaxPool1d(2)\n            self.fc1 = nn.Linear(128 * (input_length // 8), 512)\n            self.fc2 = nn.Linear(512, output_length * n_leads)\n            self.output_length = output_length\n            self.n_leads = n_leads\n        def forward(self, x):\n            x = F.relu(self.conv1(x))\n            x = self.pool(x)\n            x = F.relu(self.conv2(x))\n            x = self.pool(x)\n            x = F.relu(self.conv3(x))\n            x = self.pool(x)\n            x = x.view(x.size(0), -1)\n            x = F.relu(self.fc1(x))\n            x = self.fc2(x)\n            x = x.view(-1, self.n_leads, self.output_length)\n            return x\n\n    # --- 4. Combine into a Full Pipeline ---\n    class ECGDigitizer(nn.Module):\n        def __init__(self):\n            super().__init__()\n            self.segmenter = UNet()\n            self.coordnet = CoordNet()\n            self.signalnet = SignalNet()\n        def forward(self, x):\n            # x: (batch, 1, H, W) preprocessed image\n            mask = torch.sigmoid(self.segmenter(x))  # (batch, 1, H, W)\n            corners = self.coordnet(x).view(-1, 4, 2)  # (batch, 4, 2)\n            # For demo, just pass through; in practice, implement geometric transform here\n            # Extract per-lead traces (simplified for demo)\n            # In a real pipeline, use the mask and corners to extract each lead ROI\n            # Then, for each ROI, run signal extraction\n            # Here, we just pass the whole image through signalnet for illustration\n            # This is a placeholder; replace with your lead-wise extraction logic\n            x_signal = x.mean(dim=(2, 3)).unsqueeze(1)  # (batch, 1, 1)\n            x_signal = x_signal.expand(-1, 1, 512)  # (batch, 1, 512)\n            signals = self.signalnet(x_signal)  # (batch, 12, 4000)\n            return signals\n\n    # --- 5. Instantiate and Move to Device ---\n    model = ECGDigitizer().to(device)\n    print('Model architecture:')\n    print(model)\n    print(f'Model moved to {device}')\n\n    # --- 6. Example Forward Pass ---\n    sample_tensor = sample_tensor.unsqueeze(0).to(device)  # (1, 1, H, W)\n    with torch.no_grad():\n        output = model(sample_tensor)\n    print('Sample output shape:', output.shape)  # (1, 12, 4000)\n\n    # --- 7. Visualization ---\n    plt.figure(figsize=(12, 6))\n    for i in range(12):\n        plt.subplot(3, 4, i+1)\n        plt.plot(output[0, i].cpu().numpy())\n        plt.title(f'Lead {i+1}')\n    plt.suptitle('Extracted ECG Signals (Placeholder)')\n    plt.tight_layout()\n    plt.show()\n\n    print('\u2705 Model Architecture complete!')\n\nexcept Exception as e:\n    print(f'\u2717 Error in Model Architecture: {e}')\n    import traceback\n    traceback.print_exc()"}, {"cell_type": "markdown", "metadata": {}, "source": "## 6. Implementation & Next Steps\n\n**Note:** This section provides guidance, not complete code. Actual implementation depends on competition task."}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": "print('\ud83d\udccb === IMPLEMENTATION GUIDE ===\\n')\n\nprint('Competition Type: computer-vision - signal-extraction\\n')\nprint('Task: ECG images (scanned/photographed paper printouts) \u2192 Time series data (12-lead ECG signals)\\n')\nprint('\ud83d\udca1 Implementation Process:')\nprint('1. Load and explore the competition data')\nprint('2. Preprocess according to data type')\nprint('3. Build baseline model')\nprint('4. Train and validate')\nprint('5. Generate predictions')\nprint('6. Format submission file')\n\nprint('\\n\u26a0\ufe0f TODO:')\nprint('  [ ] Implement data preprocessing')\nprint('  [ ] Build and train model')\nprint('  [ ] Generate test predictions')\nprint('  [ ] Format submission')\n\nprint('\\n\ud83d\udca1 TIP: Check research gaps and implementation strategy above!')\n"}, {"cell_type": "markdown", "metadata": {}, "source": "## 7. Submission\n\n**Generate submission file in competition format**"}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": "print('\ud83d\udce4 === SUBMISSION GENERATION ===\\n')\n\nprint('PhysioNet - Digitization of ECG Images Submission Format:')\nprint('  Metric: SNR (Signal-to-Noise Ratio)')\nprint('  Format: Check sample_submission file for exact format')\n\nprint('\\n\u26a0\ufe0f TODO:')\nprint('  1. Generate predictions on test set')\nprint('  2. Format according to sample_submission')\nprint('  3. Validate submission format')\nprint('  4. Save submission file')\n\n# Load sample submission to see format\n# sample_sub = pd.read_csv(DATA_PATH / 'sample_submission.csv')  # or .parquet\n# print(sample_sub.head())\n#\n# Create your submission matching the format:\n# submission = sample_sub.copy()\n# submission['target'] = your_predictions  # Replace 'target' with actual column name\n# submission.to_csv('submission.csv', index=False)\n# print('\u2705 Submission created!')\n"}], "metadata": {"kernelspec": {"display_name": "Python 3", "language": "python", "name": "python3"}, "language_info": {"name": "python", "version": "3.8.0"}}, "nbformat": 4, "nbformat_minor": 4}