Automatic Sleep Stage Scoring using Hilbert-Huang Transform with BP Neural Network
In this paper, a novel method based on Hilbert-Huang Transform (HHT) and backpropagation (BP) neural network is proposed to perform automatic sleep stages classification. Features extracted from 30-second epoch of EEG using HHT are good representations of EEG signal. A three-layer BP neural network is employed to classify these features to one appropriate stage. For a four-stage classification, consisting of Awake, Stage 1+REM, Stage 2 and slow wave stage (SWS), of one single Pz-Oz channel EEG signal alone from 7 human subjects, the average stage recognition rate of the proposed method can achieve Awake 95.2%, Stage 1+Rem 87.1%, Stage 2 82.0%, SWS 92.9%. The experiment results show the method is effective and promising in automatic sleep states classification. It can be a powerful tool in sleep quality monitoring and sleep-related diseases diagnosis.
Yuelei Liu Lanfeng Yan Bo Zeng Wei Wang
School of Information Science and Engineering, Lanzhou University 222 Southern Tianshui road, Lanzho Peoples’ Hospital of Gansu Province 222 Southern Tianshui road, Lanzhou, Gansu Province, China, 7300
国际会议
成都
英文
1-4
2010-06-18（万方平台首次上网日期，不代表论文的发表时间）