Development of AUC diagrams for analysis of interhemispheric asymmetry of amplitude-frequency characteristics of EEG to detect delayed cerebral ischemia induced by non-traumatic subarachnoid hemorrhage

Q4 Materials Science
O. Sushkova, A. Morozov, I. Kershner, I. V. Okuneva, M. Sinkin
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引用次数: 0

Abstract

The article describes a new type of AUC diagrams intended for the analysis of interhemispheric asymmetry of amplitude-frequency characteristics of electroencephalograms (EEG) of patients with subarachnoid hemorrhage, as well as a new type of head maps named maps of interhemispheric asymmetry of EEG. AUC diagrams are a new statistical tool for identifying regularities in biomedical signals. The idea of AUC diagrams is to visually represent the dependence of the area under the ROC curve (AUC) when comparing data samples from the bounds of the range of given characteristic of this data, for example, frequency or amplitude, etc. The article demonstrates that this principle of data analysis allows us to identify some signs of postoperative complications that may occur in patients undergoing intensive care unit. It is known that the signs of such complications are changes in the amplitude and frequency of EEG oscillations in the neurophysiological frequency ranges delta, theta, alpha, and beta; however, amplitude changes can be caused by other reasons including the state of sleep and exposure to pharmacological drugs. Changes in the amplitude caused by postoperative complications can be revealed by analysis of the interhemispheric asymmetry of the patient's EEG. The developed type of AUC diagrams and interhemispheric EEG asymmetry maps help to automate such EEG analysis. The effectiveness of the developed statistical tools was demonstrated by the analysis of data in two patients with clinically confirmed delayed cerebral ischemia induced by subarachnoid hemorrhage.
非外伤性蛛网膜下腔出血致迟发性脑缺血的脑电图振幅频率特征的半球间不对称性的AUC图的建立
本文介绍了一种用于分析蛛网膜下腔出血患者脑电图幅频特征半球间不对称的新型AUC图,以及一种新型脑电半球间不对称图。AUC图是识别生物医学信号规律的一种新的统计工具。AUC图的思想是在比较数据样本时,直观地表示ROC曲线下面积(AUC)的依赖关系,这些样本来自该数据的给定特征范围的边界,例如频率或幅度等。本文表明,这一数据分析原则使我们能够识别重症监护病房患者可能发生的术后并发症的一些迹象。众所周知,这些并发症的症状是脑电图在δ、θ、α和β神经生理频率范围内振幅和频率的变化;然而,振幅变化可能由其他原因引起,包括睡眠状态和接触药物。术后并发症引起的振幅变化可以通过分析患者脑电图的半球间不对称性来揭示。开发的AUC图和脑半球间不对称图有助于自动化这种脑电分析。通过对两例经临床证实的蛛网膜下腔出血所致迟发性脑缺血患者的数据分析,证明了所开发的统计工具的有效性。
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来源期刊
Radioelektronika, Nanosistemy, Informacionnye Tehnologii
Radioelektronika, Nanosistemy, Informacionnye Tehnologii Materials Science-Materials Science (miscellaneous)
CiteScore
0.60
自引率
0.00%
发文量
38
期刊介绍: Journal “Radioelectronics. Nanosystems. Information Technologies” (abbr RENSIT) publishes original articles, reviews and brief reports, not previously published, on topical problems in radioelectronics (including biomedical) and fundamentals of information, nano- and biotechnologies and adjacent areas of physics and mathematics. The authors of the journal are academicians, corresponding members and foreign members of the Russian Academy of Natural Sciences (RANS) and their colleagues, as well as other russian and foreign authors on the proposal of the members of RANS, which can be obtained by the author before sending articles to the editor or after its arrival on the recommendation of a member of the editorial board or another member of the RANS, who gave the opinion on the article at the request of the editior. The editors will accept articles in both Russian and English languages. Articles are internally peer reviewed (double-blind peer review) by members of the Editorial Board. Some articles undergo external review, if necessary. Designed for researchers, graduate students, physics students of senior courses and teachers. It turns out 2 times a year (that includes 2 rooms)
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