Automatic Detection of the K-Complex Using Matching Pursuit Algorithmfor NREM-2 Stage Scoring
Date Issued
2014
Date
2014
Author(s)
Chen, Szu-Yu
Abstract
The purpose of this research is to develop an identification method of the K-Complex wave (KC) in NREM-2 stages (N2) based on AASM rules. In order to mimic the procedure of the sleep stage scoring by sleep technicians, this study uses the matching pursuit algorithm (MP) to locate the KCs in a sleep signal.
Similar to the Fourier expansion, the MP expands a signal by a set of bases. The expansion is composed of iterative matching pursuit processes. In each MP process, the correlations between the bases and the signal are computed and the basis that best matches the signal is extracted from the signal. Then the MP process is conducted repeatedly to find the signal segments that possess KC-like waveforms. That simulates the KC pattern recognition by sleep experts. After the KC candidates are obtained, several criteria are applied to screen the candidates, including the duration, peak-to-peak voltage, ratio of negative to positive amplitude, the interval between continuous KC, and the ratio of peak-to-peak voltage to the average of background voltage. Candidates that pass all criteria are considered KC in the signal.
This study uses two different data sources. The first one is the DREAMS Databases provided by Devuyst on the website. The sleep signals for 5 subjected were taken from the database. The proposed method was applied to detect KC in these signals and the results were compared those by manual scoring. Among all N2 epochs, the sensitivity (TPR) of the proposed method in KC detection was 65%, 74%, and 80%, respectively, compared with the KC marked by expert 1, expert 2, and the intersection of experts 1 and 2. In the detection of epochs with KC, the sensitivity of the proposed method was 77%, 92%, and 95%, respectively, compared with results by expert 1,expert 2, and the intersection of experts 1 and 2. In general, the performance of the proposed method surpasses the DREAMS automatic detection algorithm.
The second data set was collected from the Kang-Ning General Hospital, constituting of sleep signals of 9 healthy subjects. Among all N2 epochs, the TPR of the proposed method in the detection of KC and epochs with KC was 94% and 96%, respectively. Nevertheless, the overall performance of the proposed method is satisfactory, especially in the detection of epochs with KC.
Subjects
睡眠腦波
K複合波
自動判讀
睡眠第二期
匹配追蹤演算法
Type
thesis
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