Prediction Model of Automatic Sleep Staging Based on Ensemble Learning
Authors: 
Kai Liu, Hongjun Zhang, Chenxi XiaAuthors Info & Claims
Pages 514 - 518
Abstract
A kind of sleep staging prediction model based on signal data preprocessing and ensemble learning method is constructed to solve the low accuracy rate of traditional automatic sleep staging. First, the preprocessing of 3,000 samples aims to eliminate the non-characteristic signal data and cement the distribution difference of data at different sleep stages; Then the soft-voting classifier prediction model is constructed by applying the ensemble learning thought that integrating the Adaboost and KNN model. The average classification accuracy rate of the method reaches 87.6% after the comparison and analysis of experimental results. Compared with other machine learning models and signal processing methods, accuracy rate of classification prediction improves greatly.
References
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Index Terms
- Prediction Model of Automatic Sleep Staging Based on Ensemble Learning
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Published In
October 2021
593 pages
ISBN:9781450395588
DOI:10.1145/3500931
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Association for Computing Machinery
New York, NY, United States
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Published: 22 December 2021
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ISAIMS 2021
ISAIMS 2021: 2nd International Symposium on Artificial Intelligence for Medicine Sciences
October 29 - 31, 2021
Beijing, China
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Overall Acceptance Rate 53 of 112 submissions, 47%
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