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New methods for signal-adaptive, time-variant analysis of phase properties and directed interactions of and between EEG/MEG oscillations

用于信号自适应、时变分析相位特性以及 EEG/MEG 振荡之间的定向相互作用的新方法

基本信息

批准号：
231138331
负责人：
Dr. Lutz Leistritz
金额：
--
依托单位：
Institut für Medizinische Statistik, Informatik und Dokumentation
依托单位国家：
德国
项目类别：
Research Grants
财政年份：
2012
资助国家：
德国
起止时间：
2011-12-31 至 2016-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/231138331?language=en
关键词：
New methods signal adaptive time

项目摘要

The advantage of signal-adaptive, time-variant analysis methods is that (e.g.) time-frequency properties can be analysed in a signal-driven and signal-optimal manner. The proposed methodological approaches are composed of two processing units. In the first unit, signals are decomposed into signal-atoms or signal components. The resulting components are subsequently processed (directly or via a suitable elimination scheme) by a specific analysis method in the second unit. According to the envisaged signal property, both processing units form a holistically optimized analysis concept. Phase properties and directed interactions (effective connectivity) of and between EEG/MEG oscillations are of particular interest. For an optimal time-frequency analysis of the instantaneous phase the Matched Gabor Transform, which was introduced by us, will be generalized. Additionally, the methodology of the Hilbert-Huang Transform (HHT) will be improved and advanced to enable multi-trial as well as multi-channel analyses. The concept of the Granger causality will be expanded by elimination schemes for frequency components, which are extracted by filtering, to optimize frequency-selective connectivity analysis. Furthermore, we aimed at a replacement of filtering by Empirical Mode Decomposition (part of the HHT) and a decomposition of signals in ARMA(2,1) components. This would lead to the possibility to perform signal-adaptive, frequency-selective connectivity analyses. All new methods will be tested by simulated and clinical data and a critical comparison with the results provided by other methods is planned.

信号自适应、时变分析方法的优点是(例如)时频特性可以以信号驱动和信号优化的方式进行分析。建议的方法论方法由两个处理单元组成。在第一单元中，信号被分解成信号原子或信号分量。随后通过第二单元中的特定分析方法(直接或通过适当的消除方案)处理所得到的组分。根据设想的信号特性，两个处理单元形成整体优化的分析概念。EEG/MEG振荡的相位特性和定向相互作用(有效连通性)以及它们之间的相互作用特别令人感兴趣。为了对瞬时相位进行最佳时频分析，我们将推广我们提出的匹配Gabor变换。此外，希尔伯特-黄变换(HHT)的方法将得到改进和改进，以实现多试验和多通道分析。Granger因果关系的概念将通过对通过滤波提取的频率分量的消除方案来扩展，以优化频率选择性连通性分析。此外，我们的目标是用经验模式分解(HHT的一部分)取代滤波，并将信号分解为ARMA(2，1)分量。这将导致执行信号自适应、频率选择性连通性分析的可能性。所有新方法都将通过模拟和临床数据进行测试，并计划与其他方法提供的结果进行关键比较。

项目成果

期刊论文数量（9）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Time-Variant, Frequency-Selective, Linear and Nonlinear Analysis of Heart Rate Variability in Children With Temporal Lobe Epilepsy

DOI：
10.1109/tbme.2014.2307481
发表时间：
2014-02
期刊：
IEEE Transactions on Biomedical Engineering
影响因子：
4.6
作者：
K. Schiecke;M. Wacker;D. Piper;F. Benninger;M. Feucht;H. Witte
通讯作者：
K. Schiecke;M. Wacker;D. Piper;F. Benninger;M. Feucht;H. Witte

Discussion of “Computational Electrocardiography: Revisiting Holter ECG Monitoring”

“计算心电图：重新审视动态心电图监测”的讨论

DOI：
10.3414/me15-15-0009
发表时间：
2016
期刊：
Methods of Information in Medicine
影响因子：
1.7
作者：
Baumgartner;Caiani;Dickhaus;Kulikowski;Schiecke;van Bemmel
通讯作者：
van Bemmel

Synchronization analysis between heart rate variability and EEG activity before, during, and after epileptic seizure

DOI：
10.1515/bmt-2013-0139
发表时间：
2014-08
期刊：
Biomedical Engineering / Biomedizinische Technik
影响因子：
0
作者：
D. Piper;K. Schiecke;L. Leistritz;Britta Pester;F. Benninger;M. Feucht;M. Ungureanu;R. Strungaru;H. Witte
通讯作者：
D. Piper;K. Schiecke;L. Leistritz;Britta Pester;F. Benninger;M. Feucht;M. Ungureanu;R. Strungaru;H. Witte

A time domain frequency-selective multivariate Granger causality approach.

时域频率选择性多元格兰杰因果关系方法