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

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)可以以信号驱动和信号最佳的方式分析时间-频率特性。拟议的方法学方针由两个处理单位组成。在第一单元中,信号被分解成信号原子或信号分量。随后在第二单元中通过特定的分析方法(直接或通过合适的消除方案)处理所得组分。 根据所设想的信号特性,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(有效连接)以及EEG/MEG振荡之间的相位特性和定向相互作用(有效连接)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。格兰杰因果关系的概念将通过用于频率分量的消除方案来扩展,频率分量通过滤波来提取,以优化频率选择性连接性分析。此外,我们的目标是通过经验模式分解(HHT的一部分)和信号在阿尔马(2,1)分量中的分解来代替滤波。这将导致执行信号自适应、频率选择性连接性分析的可能性。所有新方法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:
  • 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:
  • 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:
  • 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
Time-variant coherence between heart rate variability and EEG activity in epileptic patients: an advanced coupling analysis between physiological networks
  • DOI:
    10.1088/1367-2630/16/11/115012
  • 发表时间:
    2014-11
  • 期刊:
  • 影响因子:
    3.3
  • 作者:
    D. Piper;K. Schiecke;Britta Pester;F. Benninger;M. Feucht;Herbert Witte
  • 通讯作者:
    D. Piper;K. Schiecke;Britta Pester;F. Benninger;M. Feucht;Herbert Witte
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Dr. Lutz Leistritz其他文献

Dr. Lutz Leistritz的其他文献

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{{ truncateString('Dr. Lutz Leistritz', 18)}}的其他基金

The advancement of time-variant and non-linear methods for describing directed effective connectivity in interaction networks. Applications to the investigation of EEG and fMRI signals
用于描述交互网络中定向有效连接的时变和非线性方法的进步。
  • 批准号:
    5330714
  • 财政年份:
    2001
  • 资助金额:
    --
  • 项目类别:
    Priority Programmes

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