Wavelet-Based EEG Analysis and Automatic Classification System of Long-Term Polysomnography
Date Issued
2006
Date
2006
Author(s)
Chao, Chih-Feng
DOI
zh-TW
Abstract
An automatic analysis method for 「extracting primary rhythms」, 「detecting and eliminating electrocardiograph (ECG) artifacts of electroencephalography (EEG)」, and「automatic detection and recognition of sleep spindles」are proposed in this paper. The idea is to decompose the EEG into quasi-stationary states based on wavelet Multi-Resolution Analysis. Considering the properties of wavelet filters and the relationship between wavelet basis and EEG characteristics, this paper presents a wavelet basis selection criterion to choose suitable basis and optimal scale for decomposition and detection without time-shift by wavelet transform. Unlike previously investigations, the proposed method separates pure rhythms (alpha, beta and slow wave) to reduce the complexity of reviewing EEG and also conforms to medical requirements. Furthermore, an automatic and adaptive method with high reliability to detect and eliminate ECG artifacts from EEGs is also developed without an additional synchronous ECG channel. By using the method, the total detection rate is above 93% for MIT/ BIH database. To the aspect of 「automatic detection and recognition of sleep spindles」, the new wavelet basis 「Spindlet」is created to extract sleep spindles, the total detection rate is above 87.97% for Sleep-EDF database of Physiobank.
Based on the above-mentioned research and methodology, the proposed approach of Automatic Classification System of long-term Polysomnography, ACSP, contains 4 primary procedures: (1) segmentation; (2) feature extraction; (3) classification; (4) presentation. Nonlinear energy operator method is used to divide the prolonged polysomnography into moderate segments, which are utilized to extract the features. All the segment features are used to classify the segments into groups of like patterns by self organization strategy. The analyzed data of the long-term polysomnography are presented in a compressed form in final procedure. This is completed by providing a representative sample from each group and a compressed time profile of the whole polysomnography. The performance evaluations indicate that the approaches are feasible and can be used as a new way for automatic biomedical signal analysis.
Subjects
小波轉換
小波基底選用準則
模糊C-means分群法
貝氏分類器
腦電訊號
心電訊號干擾
睡眠紡錘波
wavelet transform
wavelet basis selection criterion
Fuzzy C-Means clustering
Bayesian classifier
EEG
ECG artifact
sleep spindle
Type
thesis
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