AI Article Synopsis
- Sleep patterns differ significantly among individuals, and this study investigates automated ways to identify groups with similar sleep behaviors using polysomnography data.
- A new method utilizing unsupervised machine learning on hypnodensity graphs from a pre-trained neural network revealed two stable clusters in a sample of 100 subjects, which were visualized with total sleep time estimates.
- The analysis showed that hypnodensity representations of sleep stages resulted in more reliable clustering compared to traditional hypnograms.
Article Abstract
Sleep patterns vary widely between individuals. We explore methods for identifying populations exhibiting similar sleep patterns in an automated fashion using polysomnography data. Our novel approach applies unsupervised machine learning algorithms to hypnodensities graphs generated by a pre-trained neural network. In a population of 100 subjects we identify two stable clusters whose characteristics we visualize graphically and through estimates of total sleep time. We also find that the hypnodensity representation of the sleep stages produces more robust clustering results than the same methods applied to traditional hypnograms.
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| http://dx.doi.org/10.1109/EMBC40787.2023.10340905 | DOI Listing |
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