Quadratic Phase Couplings in the EEG of Premature and Full-term Newborn during Quiet Sleep.

INTRODUCTION: This article is part of the Focus Theme of Methods of Information in Medicine on "Biosignal Interpretation: Advanced Methods for Neural Signals and Images". OBJECTIVES: The aim of this study was to compare rhythmicities in the quadratic phase coupling (QPC) in the tracé discontinue EEG patterns (TD) of premature newborns and the tracé alternant EEG patterns (TA) of full-term newborns by means of time-variant bispectral analysis. Both pattern occur during quiet sleep and are characterized by an ongoing sequence of interburst and burst patterns. The courses of time-variant bispectral measures during the EEG burst most likely indicate specific interrelations between cortical and thalamocortical brain structures. METHODS: The EEG of a group of premature (n = 5) and of full-term (n = 5) newborns was analysed. Time-variant QPC was investigated by means of time-variant parametric bispectral analysis. The frequency plain [0.5 Hz, 1.5 Hz] x [3 Hz, 6 Hz] was used as the region-of-interest (ROI). RESULTS: QPC rhythms with a frequency of 0.1 Hz (8 - 11 s) were found in all full-term newborns at all electrodes. For the premature newborns the QPC rhythms were less stable and slower (< 0.1 Hz, 11 -  17 s) at all electrodes and showed a higher inter-individual variation than for the full-term newborns. Statistically, the adaptation of a linear mixed model revealed a difference of about 5 s between both groups of newborns. CONCLUSIONS: The comparison of the results of both groups of newborns indicates a development in the interaction between cortical, thalamocortical and neurovegetative structures in the neonatal brain.

Assignment of Empirical Mode Decomposition Components and Its Application to Biomedical Signals.

OBJECTIVES: Empirical mode decomposition (EMD) is a frequently used signal processing approach which adaptively decomposes a signal into a set of narrow-band components known as intrinsic mode functions (IMFs). For multi-trial, multivariate (multiple simultaneous recordings), and multi-subject analyses the number and signal properties of the IMFs can deviate from each other between trials, channels and subjects. A further processing of IMFs, e.g. a simple ensemble averaging, should determine which IMFs of one signal correspond to IMFs from another signal. When the signal properties have similar characteristics, the IMFs are assigned to each other. This problem is known as correspondence problem. METHODS: From the mathematical point of view, in some cases the correspondence problem can be transformed into an assignment problem which can be solved e.g. by the Kuhn-Munkres algorithm (KMA) by which a minimal cost matching can be found. We use the KMA for solving classic assignment problems, i.e. the pairwise correspondence between two sets of IMFs of equal cardinalities, and for pairwise correspondences between two sets of IMFs with different cardinalities representing an unbalanced assignment problem which is a special case of the k-cardinality assignment problem. RESULTS: A KMA-based approach to solve the correspondence problem was tested by using simulated, heart rate variability (HRV), and EEG data. The KMA-based results of HRV decomposition are compared with those obtained from a hierarchical cluster analysis (state-of-the-art). The major difference between the two approaches is that there is a more consistent assignment pattern using KMA. Integrating KMA into complex analysis concepts enables a comprehensive exploitation of the key advantages of the EMD. This can be demonstrated by non-linear analysis of HRV-related IMFs and by an EMD-based cross-frequency coupling analysis of the EEG data. CONCLUSIONS: The successful application to HRV and EEG analysis demonstrates that our solutions can be used for automated EMD-based processing concepts for biomedical signals.

Time-variant partial directed coherence for analysing connectivity: a methodological study.

For the past decade, the detection and quantification of interactions within and between physiological networks has become a priority-in-common between the fields of biomedicine and computer science. Prominent examples are the interaction analysis of brain networks and of the cardiovascular-respiratory system. The aim of the study is to show how and to what extent results from time-variant partial directed coherence analysis are influenced by some basic estimator and data parameters. The impacts of the Kalman filter settings, the order of the autoregressive (AR) model, signal-to-noise ratios, filter procedures and volume conduction were investigated. These systematic investigations are based on data derived from simulated connectivity networks and were performed using a Kalman filter approach for the estimation of the time-variant multivariate AR model. Additionally, the influence of electrooculogram artefact rejection on the significance and dynamics of interactions in 29 channel electroencephalography recordings, derived from a photic driving experiment, is demonstrated. For artefact rejection, independent component analysis was used. The study provides rules to correctly apply particular methods that will aid users to achieve more reliable interpretations of the results.