ENHANCING HEALTHCARE WITH EOG: A NOVEL APPROACH TO SLEEP STAGE CLASSIFICATION

This repository contains code, results, and dataset links for our ICASSP 2024 paper titled ENHANCING HEALTHCARE WITH EOG: A NOVEL APPROACH TO SLEEP STAGE CLASSIFICATION. 📝

Authors: ¹Shivam Kumar Sharma, ¹Suvadeep Maiti, Bapi Raju

¹Equal contribution

More details on the paper can be found here. Raise an issue for any query regarding the code, paper, or for any support.

Table of contents

• Introduction
• Highlights
• Results
• Dataset
• Getting started
• Getting the weights
• License and Citation

Introduction 🔥

We introduce an innovative approach to automated sleep stage classification using EOG signals, addressing the discomfort and impracticality associated with EEG data acquisition. In addition, it is important to note that this approach is untapped in the field, highlighting its potential for novel insights and contributions. Our proposed SE-Resnet-Transformer model provides an accurate classification of five distinct sleep stages from raw EOG signal. Extensive validation on publically available databases (SleepEDF-20, SleepEDF-78, and SHHS) reveals noteworthy performance, with macro-F1 scores of 74.72, 70.63, and 69.26, respectively. Our model excels in identifying REM sleep, a crucial aspect of sleep disorder investigations. We also provide insight into the internal mechanisms of our model using techniques such as 1D-GradCAM and t-SNE plots. Our method improves the accessibility of sleep stage classification while decreasing the need for EEG modalities. This development will have promising implications for healthcare and the incorporation of wearable technology into sleep studies, thereby advancing the field’s potential for enhanced diagnostics and patient comfort.

Highlights ✨

• A supervised model trained on Electrooculogram (EOG) data beating the current SOTA models 💥.
• Complete pre-processing pipeline, augmentation, and training scripts are available for experimentation.
• Pre-trained model weights are provided for reproducibility.

Results 🕺

Linear evaluation results on Sleep-EDF-20, Sleep-EDF-78 and SHHS datasets.

	Accuracy	κ	Macro F1-score
Sleep-EDF-20	79.3	0.72	74.7
Sleep-EDF-153	73.6	0.68	70.6
SHHS	79.0	0.71	69.3

1D-GradCAM visualization of raw EOG epochs along with sleep micro-structures shown in green boxes.

Confusion Matrics of Fold-0 on SleepEDF-20, SleepEDF-153 and SHHS datasets

Getting started 🥷

Setting up the environment

COMING SOON

What each file does

COMING SOON

Training the model

COMING SOON

Testing the model

COMING SOON

Logs and checkpoints

• The logs are saved in logs/ directory.
• The model checkpoints are saved in checkpoints/ directory.

Getting the weights 🏋️

COMING SOON

License and Citation 📰

Please cite the following paper if you have used this code:

@misc{deep2023sharma,
      title={ENHANCING HEALTHCARE WITH EOG: A NOVEL APPROACH TO SLEEP STAGE CLASSIFICATION}, 
      author={Suvadeep Maiti and Shivam Sharma and S. Mythirayee and Srijithesh Rajendran and Bapi Raju},
      year={2023},
      eprint={2310.03757},
      archivePrefix={arXiv},
      primaryClass={eess.SP}
}