Psychology Department, Trent University, Peterborough, ON, Canada.
J Sleep Res. 2010 Jun;19(2):374-8. doi: 10.1111/j.1365-2869.2009.00802.x. Epub 2010 Feb 10.
The goal of the current investigation was to develop a systematic method to validate the accuracy of an automated method of sleep spindle detection that takes into consideration individual differences in spindle amplitude. The benchmarking approach used here could be employed more generally to validate automated spindle scoring from other detection algorithms. In a sample of Stage 2 sleep from 10 healthy young subjects, spindles were identified both manually and automatically. The minimum amplitude threshold used by the Prana (PhiTools, Strasbourg, France) software spindle detection algorithm to identify a spindle was subject-specific and determined based upon each subject's mean peak spindle amplitude. Overall sensitivity and specificity values were 98.96 and 88.49%, respectively, when compared to manual scoring. Selecting individual amplitude thresholds for spindle detection based on systematic benchmarking data may validate automated spindle detection methods and improve reproducibility of experimental results. Given that interindividual differences are accounted for, we feel that automatic spindle detection provides an accurate and efficient alternative approach for detecting sleep spindles.
本研究旨在开发一种系统的方法，以验证一种自动睡眠纺锤波检测方法的准确性，该方法考虑了纺锤波幅度的个体差异。这里使用的基准方法可以更广泛地用于验证来自其他检测算法的自动纺锤波评分。在 10 名健康年轻受试者的第二阶段睡眠样本中，手动和自动识别了纺锤波。Prana（PhiTools，斯特拉斯堡，法国）软件纺锤波检测算法用于识别纺锤波的最小幅度阈值是针对每个受试者的，基于每个受试者的平均峰值纺锤波幅度确定。与手动评分相比，总体灵敏度和特异性值分别为 98.96%和 88.49%。基于系统基准数据选择用于纺锤波检测的个体幅度阈值，可以验证自动纺锤波检测方法并提高实验结果的可重复性。鉴于考虑了个体间的差异，我们认为自动纺锤波检测为检测睡眠纺锤波提供了一种准确且高效的替代方法。
