📖 Overview

Photoplethysmography (PPG) is a non-invasive optical technique widely used for monitoring biosignals and cardiovascular health, prevalent in both clinical settings and consumer wearable devices. Current machine learning models for PPG signals are often task-specific and struggle with generalizability. Many prior works utilize single-device datasets, neglect out-of-domain generalization, or do not publicly release their models, thereby limiting reproducibility and broader research progress.

PaPaGei is the first open foundation model for PPG signals. It is pre-trained on over 57,000 hours of 20 million unlabeled PPG segments, exclusively using publicly available datasets. We benchmark PaPaGei against popular time-series foundation models and other methods across 20 tasks from 10 diverse datasets, covering cardiovascular health, sleep disorders, pregnancy monitoring, and wellbeing assessment.

Our novel architecture incorporates representation learning approaches that capitalize on morphological differences in PPG signals across individuals, enabling it to capture richer representations than traditional contrastive learning methods. PaPaGei demonstrates significant improvements, boosting classification and regression performance by an average of 6.3% and 2.9%, respectively, compared to other leading time-series foundation models in at least 14 tasks. Notably, PaPaGei is more data- and parameter-efficient, outperforming models up to 70x larger.

Beyond accuracy, we investigate robustness against different skin tones, establishing a benchmark for evaluating bias in future models. PaPaGei can be readily used as a feature extractor or an encoder for multimodal models, paving the way for new advancements in multimodal health monitoring.

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🚀 Updates

• Jan 22, 2025: PaPaGei accepted to the International Conference on Learning Representations (ICLR). Read the latest version of the paper.
• Dec 15, 2024: PaPaGei received the 🏆 Best Paper Award at the NeurIPS workshop on Time Series in the Age of Large Models (TSALM). See accepted papers.
• Oct 29, 2024: Paper available on arXiv.
• Oct 24, 2024: Access the model weights on Zenodo (here).
• Oct 15, 2024: Code released! 🎉

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🛠️ How to Use PaPaGei

PaPaGei offers versatility for developers and researchers:

1. Out-of-the-Box Feature Extraction: Use PaPaGei to extract transferable features for your machine learning tasks, replacing handcrafted features.
2. PPG Encoder Integration: Incorporate PaPaGei as a PPG encoder into larger frontier models (e.g., LLMs like AnyMAL).

📦 Installation

1. Create a Conda Environment:

   conda create -n papagei_env python=3.10
   conda activate papagei_env
   

2. Install Required Packages:

   pip install -r requirements.txt
   

3. Install pyPPG Package:

   pip install pyPPG==1.0.41
   

   Note: This might show a wfdb package conflict, but it should still function correctly.

🧠 Downloading Model Weights

Model weights are hosted on Zenodo by Arvind Pillai.

• Download Link: Zenodo Record 13983110
• For feature extraction, save the downloaded model weights (e.g., papagei_s.pt) into a folder named weights/ in your project directory, or update the path accordingly in your scripts.

✨ Extracting Embeddings: Quick Start

Here’s a brief example of how to load the PaPaGei-S model and extract embeddings:

1. Import Necessary Packages:

   import numpy as np
   import torch
   from linearprobing.utils import resample_batch_signal, load_model_without_module_prefix
   from preprocessing.ppg import preprocess_one_ppg_signal
   from segmentations import waveform_to_segments
   from torch_ecg._preprocessors import Normalize
   from models.resnet import ResNet1DMoE
   

2. Load the PaPaGei-S Model:

   # Define Model Configuration
   model_config = {
       'base_filters': 32,
       'kernel_size': 3,
       'stride': 2,
       'groups': 1,
       'n_block': 18,
       'n_classes': 512, # Embedding dimension
       'n_experts': 3
   }
   
   # Initialize Model
   model = ResNet1DMoE(
       in_channels=1,
       base_filters=model_config['base_filters'],
       kernel_size=model_config['kernel_size'],
       stride=model_config['stride'],
       groups=model_config['groups'],
       n_block=model_config['n_block'],
       n_classes=model_config['n_classes'],
       n_experts=model_config['n_experts']
   )
   
   # Load Pre-trained Weights
   model_path = "weights/papagei_s.pt" # Ensure this path is correct
   model = load_model_without_module_prefix(model, model_path)
   device = "cuda" if torch.cuda.is_available() else "cpu"
   model.to(device)
   model.eval() # Set model to evaluation mode
   print(f"Model loaded on {device}")
   

3. Pre-process a PPG Signal:

   # Example PPG Signal
   fs = 500  # Original sampling frequency in Hz
   fs_target = 125 # Target sampling frequency in Hz
   segment_duration_seconds = 10 # Duration of each segment in seconds
   signal_duration_seconds = 60 # Total duration of the example signal
   
   signal = np.random.randn(signal_duration_seconds * fs) # Example: 60s signal at 500Hz
   print(f"Original PPG dimensions: {signal.shape}")
   
   # Clean and segment the signal
   signal_processed, _, _, _ = preprocess_one_ppg_signal(waveform=signal, frequency=fs)
   
   segment_length_original_fs = fs * segment_duration_seconds
   segmented_signals = waveform_to_segments(
       waveform_name='ppg', # Can be any name, not strictly used in this function
       segment_length=segment_length_original_fs,
       clean_signal=signal_processed
   )
   
   # Resample segments
   resampled_segments = resample_batch_signal(
       segmented_signals, 
       fs_original=fs, 
       fs_target=fs_target, 
       axis=-1
   )
   print(f"After segmentation and resampling: {resampled_segments.shape}") # (num_segments, segment_length_target_fs)
   
   # Convert to PyTorch Tensor
   signal_tensor = torch.Tensor(resampled_segments).unsqueeze(dim=1).to(device) # (num_segments, 1, segment_length_target_fs)
   

4. Extract Embeddings:

   with torch.inference_mode():
       outputs = model(signal_tensor)
       # PaPaGei-S returns a tuple (embeddings, expert_outputs, gating_weights)
       # We are interested in the first element: embeddings
       embeddings = outputs[0].cpu().detach().numpy()
   print(f"Embedding dimensions: {embeddings.shape}") # (num_segments, n_classes)
   

👉 For a comprehensive end-to-end example, including feature extraction and downstream task evaluation on the ppg-bp dataset, please refer to the Jupyter Notebook: example_papagei.ipynb.

Important Considerations:

• Model Variability: No single model excels across all tasks and datasets. We release the models that achieved the most wins in our evaluations.
• Confidence Intervals: Instead of fixed random seeds, we use bootstrapping (500 iterations) to compute 95% confidence intervals, providing a performance range.

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⚙️ Workflow & Modules

The end-to-end workflow of PaPaGei involves several key stages:

1. PPG Data Pre-processing (preprocessing/, segmentations.py):

   • preprocessing/flatline.py: Detects flatline sections in PPG signals using the BioBSS package.
   • preprocessing/ppg.py:
     • preprocess_one_ppg_signal: Applies a bandpass filter to raw signals.
     • Includes I/O functions for batch processing and saving.
   • segmentations.py:
     • waveform_to_segments: Segments filtered PPG signals based on specified segment lengths.
     • Utility functions for saving segments.

2. Morphology Augmentation Module Computation (morphology.py):

   • Computes morphological features like Stress-Induced Vascular Response Index (sVRI), Inflection Point Area ratio (IPA), and Signal Quality Index (SQI).
   • extract_svri: Calculates sVRI.
   • skewness_sqi: Calculates SQI.
   • compute_ipa: Calculates IPA.
   • Includes batch processing utilities.

3. Dataset Handling and Time-Series Augmentations (dataset.py, augmentations.py):

   • dataset.py:
     • PPGDatasetLabelsArray: A PyTorch custom Dataset class used for PaPaGei-S training. DataLoaders are set up in training_mt.py.
   • augmentations.py:
     • Provides time-series augmentation techniques implemented as torch.nn.Module classes for easy on-the-fly transformations during training.

4. Model Training (models/resnet.py, training_mt.py):

   • models/resnet.py: Contains the model architecture. ResNet1DMoE is the PaPaGei-S model.
   • training_mt.py: Manages end-to-end distributed training for PaPaGei-S.
     • train_step: Defines a single training step, including loss computation for PaPaGei-S.
     • training: Orchestrates the training loop, checkpointing, and model saving.
     • main: Entry point for distributed training.

5. Feature Extraction (feature_extraction.py):

   • compute_signal_embeddings: Extracts embeddings using the pre-trained model.
   • save_embeddings: Utility for saving extracted embeddings.

6. Linear Evaluation:

   • The embeddings extracted in Step 5 can be used as input to linear models or shallow Artificial Neural Networks (ANNs) for various downstream classification or regression tasks.

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🙏 Acknowledgements

We gratefully acknowledge the contributions of the following projects, which were instrumental in the evaluation of PaPaGei:

• Chronos: amazon-science/chronos-forecasting
• Moment: moment-timeseries-foundation-model/moment
• REGLE: Google-Health/genomics-research
• TF-C: mims-harvard/TFC-pretraining
• BYOL (for PPG Quality): chengding0713/SiamQuality
• Morphology (PPG features): qiriro/PPG

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📜 Citation

If you use PaPaGei models, code, or ideas from this project in your research, please cite our paper:

@inproceedings{pillai2025papagei,
  title={{PaPaGei: Open Foundation Models for Optical Physiological Signals}},
  author={Arvind Pillai and Dimitris Spathis and Fahim Kawsar and Mohammad Malekzadeh},
  booktitle={The Thirteenth International Conference on Learning Representations, {ICLR} 2025},
  year={2025},
  month={April},
  address={Singapore},
  note={Accepted. arXiv preprint arXiv:2410.20542},
  url={[https://arxiv.org/abs/2410.20542](https://arxiv.org/abs/2410.20542)}
}