# Personalized Automatic Sleep Staging with Single-Night Data: a Pilot Study with KL-Divergence Regularization

arXiv: https://arxiv.org/abs/2004.11349 · HTML: https://mlsanity.com/paper/2004.11349/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Apr 2020.

## Built on by (most cited first, 5 of 5)

- [Automatic Sleep Staging of EEG Signals: Recent Development, Challenges, and Future Directions](https://mlsanity.com/paper/2111.08446/) (2021)
- [A multi-level interpretable sleep stage scoring system by infusing experts' knowledge into a deep network architecture](https://mlsanity.com/paper/2207.04585/) (2022)
- [ResWCAE: Biometric Pattern Image Denoising Using Residual Wavelet-Conditioned Autoencoder](https://mlsanity.com/paper/2307.12255/) (2023)
- [Feature matching as improved transfer learning technique for wearable EEG](https://mlsanity.com/paper/2201.00644/) (2021)
- [Unsupervised domain transfer: Overcoming signal degradation in sleep monitoring by increasing scoring realism](https://mlsanity.com/paper/2604.13988/) (2026)

## Abstract (arXiv)

Brain waves vary between people. An obvious way to improve automatic sleep staging for longitudinal sleep monitoring is personalization of algorithms based on individual characteristics extracted from the first night of data. As a single night is a very small amount of data to train a sleep staging model, we propose a Kullback-Leibler (KL) divergence regularized transfer learning approach to address this problem. We employ the pretrained SeqSleepNet (i.e. the subject independent model) as a starting point and finetune it with the single-night personalization data to derive the personalized model. This is done by adding the KL divergence between the output of the subject independent model and the output of the personalized model to the loss function during finetuning. In effect, KL-divergence regularization prevents the personalized model from overfitting to the single-night data and straying too far away from the subject independent model. Experimental results on the Sleep-EDF Expanded database with 75 subjects show that sleep staging personalization with a single-night data is possible with help of the proposed KL-divergence regularization. On average, we achieve a personalized sleep staging accuracy of 79.6%, a Cohen's kappa of 0.706, a macro F1-score of 73.0%, a sensitivity of 71.8%, and a specificity of 94.2%. We find both that the approach is robust against overfitting and that it improves the accuracy by 4.5 percentage points compared to non-personalization and 2.2 percentage points compared to personalization without regularization.