Article

State-of-the-art Sleep Arousal Detection Evaluated on a Comprehensive Clinical Dataset

2024-03-22

Abstract excerpt

<h4>Aim: </h4> ing to apply automatic arousal detection to support sleep laboratories, we evaluated an optimized, state-of-the-art approach using data from daily work in our university hospital sleep laboratory. Therefore, a machine learning algorithm was trained and evaluated on 3423 polysomnograms of people suffering from various sleep disorders. The model architecture is a U-net that accepts 50 Hz signals as in...

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Literature Corpus work: 13f73247-bc71-5fdd-9092-4fb0dee1a9f4
DOI: 10.21203/rs.3.rs-4040917/v1

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