Durka Piotr J, Malinowska Urszula, Zieleniewska Magdalena, O'Reilly Christian, Różański Piotr T, Żygierewicz Jarosław
Faculty of Physics, University of Warsaw Warsaw, Poland.
Department of Neurology, Epilepsy Center, Johns Hopkins University School of Medicine Baltimore, MD, USA.
Front Hum Neurosci. 2015 May 8;9:258. doi: 10.3389/fnhum.2015.00258. eCollection 2015.
We present a complete framework for time-frequency parametrization of EEG transients, based upon matching pursuit (MP) decomposition, applied to the detection of sleep spindles. Ranges of spindles duration (>0.5 s) and frequency (11-16 Hz) are taken directly from their standard definitions. Minimal amplitude is computed from the distribution of the root mean square (RMS) amplitude of the signal within the frequency band of sleep spindles. Detection algorithm depends on the choice of just one free parameter, which is a percentile of this distribution. Performance of detection is assessed on the first cohort/second subset of the Montreal Archive of Sleep Studies (MASS-C1/SS2). Cross-validation performed on the 19 available overnight recordings returned the optimal percentile of the RMS distribution close to 97 in most cases, and the following overall performance measures: sensitivity 0.63 ± 0.06, positive predictive value 0.47 ± 0.08, and Matthews coefficient of correlation 0.51 ± 0.04. These concordances are similar to the results achieved on this database by other automatic methods. Proposed detailed parametrization of sleep spindles within a universal framework, encompassing also other EEG transients, opens new possibilities of high resolution investigation of their relations and detailed characteristics. MP decomposition, selection of relevant structures, and simple creation of EEG profiles used previously for assessment of brain activity of patients in disorders of consciousness are implemented in a freely available software package Svarog (Signal Viewer, Analyzer and Recorder On GPL) with user-friendly, mouse-driven interface for review and analysis of EEG. Svarog can be downloaded from http://braintech.pl/svarog.
我们提出了一个用于脑电图瞬变的时频参数化的完整框架,该框架基于匹配追踪(MP)分解,应用于睡眠纺锤波的检测。纺锤波持续时间(>0.5秒)和频率(11 - 16赫兹)的范围直接取自其标准定义。最小幅度是根据睡眠纺锤波频段内信号的均方根(RMS)幅度分布计算得出的。检测算法仅取决于一个自由参数的选择,该参数是此分布的一个百分位数。在蒙特利尔睡眠研究档案库的第一队列/第二子集(MASS - C1/SS2)上评估检测性能。对19份可用的夜间记录进行交叉验证,在大多数情况下,RMS分布的最佳百分位数接近97,并得出以下总体性能指标:灵敏度0.63±0.06,阳性预测值0.47±0.08,以及马修斯相关系数0.51±0.04。这些一致性与其他自动方法在该数据库上取得的结果相似。在一个通用框架内对睡眠纺锤波进行详细的参数化,该框架还涵盖其他脑电图瞬变,为高分辨率研究它们的关系和详细特征开辟了新的可能性。MP分解、相关结构的选择以及先前用于评估意识障碍患者脑活动的脑电图图谱的简单创建,都在一个免费软件包Svarog(基于GPL的信号查看器、分析仪和记录器)中实现,该软件包具有用户友好的鼠标驱动界面,用于脑电图的查看和分析。Svarog可从http://braintech.pl/svarog下载。