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Summary of Wav2sleep: a Unified Multi-modal Approach to Sleep Stage Classification From Physiological Signals, by Jonathan F. Carter and Lionel Tarassenko


wav2sleep: A Unified Multi-Modal Approach to Sleep Stage Classification from Physiological Signals

by Jonathan F. Carter, Lionel Tarassenko

First submitted to arxiv on: 7 Nov 2024

Categories

  • Main: Machine Learning (cs.LG)
  • Secondary: Artificial Intelligence (cs.AI)

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Summary difficulty Written by Summary
High Paper authors High Difficulty Summary
Read the original abstract here
Medium GrooveSquid.com (original content) Medium Difficulty Summary
A new deep learning model called wav2sleep is introduced for accurate classification of sleep stages from various sensor measurements. Unlike previous approaches that focused on specific input signals like electrocardiogram (ECG) or photoplethysmogram (PPG), wav2sleep is designed to operate on variable sets of input signals during training and inference. The model is trained on over 10,000 overnight recordings from six publicly available polysomnography datasets, including SHHS and MESA. As a result, wav2sleep outperforms existing sleep stage classification models across test-time input combinations, including ECG, PPG, and respiratory signals.
Low GrooveSquid.com (original content) Low Difficulty Summary
Wav2sleep is a new way to classify sleep stages using sensors like heart rate or blood flow. It’s different from old approaches that only worked with one type of sensor data. Wav2sleep can work with many types of sensor data at the same time. This makes it better at recognizing sleep stages than other models.

Keywords

* Artificial intelligence  * Classification  * Deep learning  * Inference