https://www.selleckchem.com/pr....oducts/azd0156-azd-0
The classification was carried out with a multi-class linear discriminant analysis classifier. Performance of the proposed model showed that our automatic system can achieve an overall accuracy of 65% for determining the predominant site-of-collapse for all site-of-collapse classes and an accuracy of 80% for classifying tongue/non-tongue related collapse. Our results indicate that the audio signal recorded during sleep can be helpful in identifying the site-of-collapse and therefore could potentially be used as a new tool for d
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