全自動辨識睡眠紡錘波

Automatic Identification of Sleep Spindle

賴昱安(Yu-An Lai)

指導教授 ： 蕭瑛東

共同指導教授 ： 黃聰亮(Tsong-Liang Huang)

淡江大學/工學院/電機工程學系碩士班/碩士(2007年)

https://doi.org/10.6846/TKU.2007.00181

摘要

睡眠障礙在精神疾病的領域上扮演著重要的角色，也是其他病患常有的合併症。睡眠窒息症、睡眠困難為常見的睡眠疾病，而且有時候甚至會導致死亡。
睡眠紡錘波是短暫的睡眠EEG波形，與K複合波是睡眠第二期的特徵，而且紡錘波可以分析睡眠微細構造。關注睡眠紡錘波在於不只是它是代表QS-2的開始，而且也要關注其他的發現，例如有關睡眠紡錘波，正常老化，嬰兒病理學或者睡眠姿勢。
以視覺模式偵測整夜EEG記錄是件耗時及冗長乏味，因為在一整夜裡記錄著典型地1000個紡錘波。因此，發展一個快速且準確的全自動辨識睡眠紡錘系統，將可以減少醫生勞力和了解睡眠紡錘波的數量診斷。

關鍵字

腦電訊號 ； 睡眠紡錘波 ； 小波

並列摘要

Sleep disorders has an important part in area of the psychiatric diseases. Usually, it's also the complication of other diseases. Sleep apnea syndrome and sleep dyssomnia are common sleep diseases, which sometimes will even cause the death.
Sleep spindles are transient sleep EEG waveforms. Along with K-complexes, they are the hallmarks of Stage 2 sleep. Furthermore, sleep spindles can be used to analyse sleep microstructure. The interest in sleep spindles lies not only in its representation to the onset of QS-2, but also in other interesting findings, such as those relating SS, normal aging, infant pathologies and sleeping positions.
The visual pattern detection of all-night EEG recordings which have typically 1000 spindles is time consuming and tedious. Therefore, developing a fast and accurate automatic identification system of sleep spindle can reduce doctor’s labor and realize quantitative diagnosis of sleep spindle.

並列關鍵字

EEG ； sleep spindles ； wavelet

參考文獻

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