Nasibov Efendi, Ozgören Murat, Ulutagay Gözde, Oniz Adile, Kocaaslan Sibel
Department of Computer Sciences, Faculty of Arts and Sciences, Dokuz Eylül University, Izmir, Turkey.
Biomed Tech (Berl). 2010 Jun;55(3):147-53. doi: 10.1515/BMT.2010.009.
Among various types of clustering methods, partition-based methods such as k-means and FCM are widely used in the analysis of such data. However, when duration between stimuli is different, such methods are not able to provide satisfactory results because they find equal size clusters according to the fundamental running principle of these methods. In such cases, neighborhood-based clustering methods can give more satisfactory results because measurement series are separated from one another according to dramatic breaking points. In recent years, bispectral index (BIS) monitoring, which is used for monitoring the level of anesthesia, has been used in sleep studies. Sleep stages are classically scored according to the Rechtschaffen and Kales (R&K) scoring system. BIS has been shown to have a strong correlation with the R&K scoring system. In this study, fuzzy neighborhood/density-based spatial clustering of applications with noise (FN-DBSCAN) that combines speed of the DBSCAN algorithm and robustness of the NRFJP algorithm is applied to BIS measurement series. As a result of experiments, we can conclude that, by using BIS data, the FN-DBSCAN method estimates sleep stages better than the fuzzy c-means method.
在各种聚类方法中，基于划分的方法（如k均值和模糊c均值）在这类数据的分析中被广泛使用。然而，当刺激之间的持续时间不同时，这些方法无法提供令人满意的结果，因为根据这些方法的基本运行原理，它们会找到大小相等的聚类。在这种情况下，基于邻域的聚类方法可以给出更令人满意的结果，因为测量序列会根据显著的断点彼此分离。近年来，用于监测麻醉深度的脑电双频指数（BIS）监测已被应用于睡眠研究。睡眠阶段传统上是根据 Rechtschaffen和Kales（R&K）评分系统进行评分的。研究表明，BIS与R&K评分系统有很强的相关性。在本研究中，将结合了DBSCAN算法速度和NRFJP算法鲁棒性的带噪声应用的模糊邻域/密度空间聚类（FN-DBSCAN）应用于BIS测量序列。实验结果表明，通过使用BIS数据，FN-DBSCAN方法比模糊c均值方法能更好地估计睡眠阶段。
