计算机科学
人工智能
模式识别(心理学)
特征提取
睡眠阶段
脑电图
分类器(UML)
小波
特征选择
机器学习
语音识别
多导睡眠图
心理学
精神科
作者
José Benavente Herrera,Carlos M. Fernandes,Antonio M. Mora,Daria Migotina,R. Largo,Alberto Guillén,Agostinho Rosa
标识
DOI:10.1142/s0129065713500123
摘要
This work proposes a methodology for sleep stage classification based on two main approaches: the combination of features extracted from electroencephalogram (EEG) signal by different extraction methods, and the use of stacked sequential learning to incorporate predicted information from nearby sleep stages in the final classifier. The feature extraction methods used in this work include three representative ways of extracting information from EEG signals: Hjorth features, wavelet transformation and symbolic representation. Feature selection was then used to evaluate the relevance of individual features from this set of methods. Stacked sequential learning uses a second-layer classifier to improve the classification by using previous and posterior first-layer predicted stages as additional features providing information to the model. Results show that both approaches enhance the sleep stage classification accuracy rate, thus leading to a closer approximation to the experts' opinion.
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