Kubicki S, Herrmann W M
Department of Psychiatry, Benjamin Franklin Hospital, Free University of Berlin, Germany.
J Clin Neurophysiol. 1996 Jul;13(4):285-94. doi: 10.1097/00004691-199607000-00003.
Previous attempts at automated analysis of sleep were mainly directed towards imitating the Rechtschaffen and Kales rules (RKR) in order to save scoring time and further objectify the procedure. RKR, however, do not take into consideration the sleep microstructure of REM, stage 2, and SWS. While the microstructure of stage 2 has been analyzed in the past decade, the microstructure of REM and SWS are virtually unknown. In stage 2 the amount and distribution of spindles, K complexes, and arousal reactions have been studied. At least two types of spindles (12/s and 14/s) with different dynamics and locations have been identified. Two different shapes for K complexes have been described: one related to external sensory stimuli with similarities to evoked potentials and another one more related to sinusoidal slow wave activity seen in SWS. These two different K complex shapes have different distributions and, obviously, different functions. The authors also suggest that one should differentiate between arousal reactions and true arousals. Recent investigations suggest two types of delta waves in SWS. The more sinusoidal 1-3/s delta waves with a frontal maximum are already seen with lower amplitude in late stage 2 and increase their amplitude and incidence towards stage 3 and Stage 4. The other delta-wave type is slower (< 1/s), polymorphic, and has varying amounts of theta and higher frequency waves superimposed. During REM sleep it seems to be important to separate phases with rapid eye movements from those with none (REM sine REM), and count the amount and distribution of sawtooth activity. Background activity during REM and REM sine REM, as well as intra- and interhemispheric coherence should be analyzed separately. Only if the microstructure of the sleep EEG can be analyzed automatically using newer techniques such as transformation into wavelets and pattern classification with neuronal networks, and only if we learn more about the importance of microstructure elements, can automated sleep analysis go beyond the limited information obtained from scoring according to RKR.
以往对睡眠进行自动分析的尝试主要是为了模仿 Rechtschaffen 和 Kales 规则（RKR），以节省评分时间并使该过程更具客观性。然而，RKR 并未考虑快速眼动（REM）、2 期和慢波睡眠（SWS）的睡眠微观结构。虽然在过去十年中对 2 期的微观结构进行了分析，但 REM 和 SWS 的微观结构实际上仍不清楚。在 2 期，已经研究了纺锤波、K 复合波和唤醒反应的数量及分布。至少已识别出两种具有不同动态和位置的纺锤波类型（12/秒和 14/秒）。已描述了 K 复合波的两种不同形状：一种与外部感觉刺激有关，类似于诱发电位；另一种更与 SWS 中所见的正弦慢波活动有关。这两种不同形状的 K 复合波具有不同的分布，显然也具有不同的功能。作者还建议应区分唤醒反应和真正的觉醒。最近的研究表明 SWS 中有两种类型的 delta 波。在 2 期末期，具有额叶最大值的更正弦的 1 - 3/秒 delta 波已经以较低幅度出现，并在向 3 期和 4 期发展时增加其幅度和发生率。另一种 delta 波类型较慢（<1/秒），多形性，并且叠加有不同数量的 theta 波和更高频率的波。在 REM 睡眠期间，将有快速眼动的阶段与无快速眼动的阶段（REM 无快速眼动）分开，并计算锯齿波活动的数量和分布似乎很重要。REM 和 REM 无快速眼动期间的背景活动以及半球内和半球间的相干性应分别进行分析。只有当使用诸如小波变换和神经网络模式分类等更新技术能够自动分析睡眠脑电图的微观结构，并且只有当我们更多地了解微观结构元素的重要性时，自动睡眠分析才能超越根据 RKR 评分所获得的有限信息。
