Huupponen E, Himanen S L, Hasan J, Värri A
Signal Processing Laboratory, Tampere University of Technology, Finland.
Med Biol Eng Comput. 2003 Nov;41(6):727-32. doi: 10.1007/BF02349981.
A fully automatic method to analyse electro-encephalogram (EEG) sleep spindle frequency evolution during the night was developed and tested. Twenty all-night recordings were studied from ten healthy control subjects and ten sleep apnoea patients. A total of 22,868 spindles were detected. The overall mean spindle frequency was significantly higher in the control subjects than in the apnoea patients (12.5 Hz against 11.7 Hz, respectively; p<0.004). The proposed method further identified the sleep depth cycles, and the mean spindle frequency was automatically determined inside each sleep depth cycle. In control subjects, the mean spindle frequency increased from 12.0 Hz in the first sleep depth cycle to 12.6 Hz in the fifth cycle. No such increase was observed in the sleep apnoea patients. This difference in the spindle frequency evolution was statistically significant (p<0.004). The advantage of the method is that no EEG amplitude thresholds are needed.
开发并测试了一种用于分析夜间脑电图（EEG）睡眠纺锤波频率演变的全自动方法。对来自10名健康对照受试者和10名睡眠呼吸暂停患者的20份整夜记录进行了研究。共检测到22868个纺锤波。对照受试者的总体平均纺锤波频率显著高于呼吸暂停患者（分别为12.5Hz和11.7Hz；p<0.004）。所提出的方法进一步识别了睡眠深度周期，并在每个睡眠深度周期内自动确定平均纺锤波频率。在对照受试者中，平均纺锤波频率从第一个睡眠深度周期的12.0Hz增加到第五个周期的12.6Hz。睡眠呼吸暂停患者未观察到这种增加。纺锤波频率演变的这种差异具有统计学意义（p<0.004）。该方法的优点是不需要EEG幅度阈值。
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