Schiecke Karin, Wacker Matthias, Benninger Franz, Feucht Martha, Leistritz Lutz, Witte Herbert
IEEE Trans Biomed Eng. 2015 Aug;62(8):1937-48. doi: 10.1109/TBME.2015.2407573. Epub 2015 Feb 26.
Principle aim of this study is to investigate the performance of a matching pursuit (MP)-based bispectral analysis in the detection and quantification of quadratic phase couplings (QPC) in biomedical signals. Nonlinear approaches such as time-variant bispectral analysis are able to provide information about phase relations between oscillatory signal components.
Time-variant QPC analysis is commonly performed using Gabor transform (GT) or Morlet wavelet transform (MWT), and is affected by either constant or frequency-dependent time-frequency resolution (TFR). The matched Gabor transform (MGT), which emerges from the incorporation of GT into MP, can overcome this obstacle by providing a complex time-frequency plane with an individually tailored TFR for each transient oscillatory component. QPC analysis was performed by MGT, and MWT was used as the state-of-the-art method for comparison.
Results were demonstrated using simulated data, which present the general case of QPC, and biomedical benchmark data with a priori knowledge about specific signal components. HRV of children during temporal lobe epilepsy and EEG during burst-interburst pattern of neonates during quiet sleep were used for the biomedical signal analysis to investigate the two main areas of biomedical signal analysis: The cardiovascular-cardiorespiratory system and neurophysiological brain activities, respectively. Simulations were able to show the applicability and reliability of the MGT for bispectral analysis. HRV and EEG analysis demonstrate the general validity of the MGT for QPC detection by quantifying statistically significant time patterns of QPC.
Results confirm that MGT-based bispectral analysis provides significant benefits for the analysis of QPC in biomedical signals.
本研究的主要目的是调查基于匹配追踪（MP）的双谱分析在生物医学信号中二次相位耦合（QPC）检测和量化方面的性能。诸如时变双谱分析等非线性方法能够提供有关振荡信号成分之间相位关系的信息。
时变QPC分析通常使用加窗傅里叶变换（GT）或Morlet小波变换（MWT）进行，并且受恒定或频率依赖的时频分辨率（TFR）影响。通过将GT纳入MP而产生的匹配加窗傅里叶变换（MGT），可以通过为每个瞬态振荡成分提供具有单独定制的TFR的复时频平面来克服这一障碍。通过MGT进行QPC分析，并将MWT用作最先进的方法进行比较。
使用模拟数据展示了结果，模拟数据呈现了QPC的一般情况，以及具有关于特定信号成分的先验知识的生物医学基准数据。颞叶癫痫患儿的心率变异性（HRV）和安静睡眠期间新生儿爆发 - 间歇模式下的脑电图（EEG）被用于生物医学信号分析，以分别研究生物医学信号分析的两个主要领域：心血管 - 心肺系统和神经生理脑活动。模拟能够显示MGT用于双谱分析的适用性和可靠性。HRV和EEG分析通过量化QPC的统计学显著时间模式，证明了MGT用于QPC检测的普遍有效性。
结果证实，基于MGT的双谱分析为生物医学信号中QPC的分析提供了显著优势。
