Matching Pursuit-Based Time-Variant Bispectral Analysis and its Application to Biomedical Signals

Schiecke, Karin; Wacker, Matthias; Benninger, Franz; Feucht, Martha; Leistritz, Lutz; Witte, Herbert

doi:10.1109/tbme.2015.2407573

Cited by 17 publications

(6 citation statements)

References 43 publications

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“…Burst-interburst EEG signal patterns are used, which occur during quiet sleep in healthy newborns. Both data sets have already been used as bench-mark data for testing new algorithms [19]. By means of both applications, two main areas of biomedical signal analysis are considered: the cardiovascularcardiorespiratory system and neurophysiological brain processes.…”

Section: Discussionmentioning

confidence: 99%

“…As recently described by Schiecke et al [19], the EEG of a group of six full-term neonates (mean conceptual age 39.3 weeks, range 38 -41 weeks; mean birth weight 3152 g, range 2670-3420 g; mean 5 min APGAR-score 9, range 8 -10) was analyzed. Recordings were performed during sleep between 09.00 and 12.00 h, all neo nates lay in an incubator at temperatures adapted to maintain normal body temperature and none showed any EEG abnormality.…”

Section: Eeg Datamentioning

confidence: 99%

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

Schiecke¹,

Schmidt²,

Piper³

et al. 2015

Methods Inf Med

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SummaryObjectives: 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.

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Section: Discussionmentioning

confidence: 99%

Section: Eeg Datamentioning

confidence: 99%

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

Schiecke¹,

Schmidt²,

Piper³

et al. 2015

Methods Inf Med

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“…For high-resolution time-frequency decomposition, a matching pursuit (MP) algorithm was employed to identify the time-frequency components (TFCs) within the averaged SEP waveform [19]. The MP algorithm is advantageous in that it can process non-stationary signals with heavy background noise and can provide an adaptive approximation of the target signal with higher resolution than conventional time-frequency analysis methods [19,31].…”

Section: B Somatosensory Evoked Potentials (Sep) Evaluationmentioning

confidence: 99%

Different Time-Frequency Distribution Patterns of Somatosensory Evoked Potentials in Dual- and Single-Level Spinal Cord Compression

Cui

Wang

et al. 2022

IEEE Trans. Neural Syst. Rehabil. Eng.

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Among patients with cervical myelopathy, the most common level of stenosis at spinal cord of all ages was reported to be between cervical levels C5-6. Previous studies found that time-frequency components (TFCs) of somatosensory evoked potentials (SEPs) possess location information of spinal cord injury (SCI) in single-level deficits in the spinal cord. However, the clinical reality is that there are multiple compressions at multiple spinal cord segments. This study proposed a new algorithm to differentiate distribution patterns of SEP TFCs between the dual-level compression and the corresponding single-level compression, which is potential in providing precise diagnosis of cervical myelopathy. In the present animal study, a group of rats with dual-level compressive (C5+6) injury to cervical spinal cord was investigated. SEPs were collected at 2 weeks after surgery, while SEP TFCs were calculated. The SEP TFCs under dual-level compression were compared to an existent dataset with one sham control group and three single level compression groups at C4, C5, C6. Behavioral evaluation showed very similar scale of injury severity between individual rats, while histology evaluation confirmed the precise Manuscript

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“…Relatively high bic(f p , f q ) values indicate that brain function involves significant nonlinear processes (the brain is considered a nonlinear biological system that displays complex dynamics and deterministic chaotic behaviour) and, by observing changes in second-order phase couplings in response to various stimuli, information on, or a deeper understanding of, these nonlinear processes may be achieved [30][31][32][33][34][35]. One of the most established applications of EEG-based bispectral analysis involves the determination of an index for depth of brain anaesthesia in the form of the bispectral index, which is extensively documented [23,26,30,[36][37][38][39].…”

Section: Conventional Eeg Analysis Brief Overviewmentioning

confidence: 99%

A New Approach to High-Order Electroencephalogram Phase Analysis Details the Mathematical Mechanisms of Central Nervous System Impulse Encoding

Simeoni¹

2021

UNET JOSS

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This paper presents a new electroencephalogram (EEG) analysis technique which is applied to example EEGs pertaining to nine human subjects and a broad spectrum of clinical scenarios. While focusing on technique physical efficacy, the paper also paves the way for future clinically-focused studies with revelations of several quantified and detailed findings in relation to high-order central nervous system communicative impulse encoding akin to a sophisticated form of phase-shift keying. The fact that fine encoding details are extracted with confidence from a seemingly modest EEG set supports the paper’s position that vast amounts of accessible information currently goes unrecognised by conventional EEG analysis. The technique commences with high resolution Fourier analysis being twice applied to an EEG, providing newly-identified harmonics. Except for deep sleep where harmonic phase, φ, behaviour becomes highly linear, φ transitional values, ∆φ, measured between harmonics of progressively increasing order are found to cluster rather than follow a normal distribution (e.g., χ2 = 303, df = 12, p < 0.001). Clustering is categorised into ten Families for which many separations between ∆φ values are writable in terms of k = j/4 or j/3 (j = 1, 2, 3 ...), with a preference for k = j/2 (χ2 = 77, df = 1, p < 0.001), amounts of a Family-specific quantum increment value, α∆φ. A parabolic relationship (r > 0.9999, p < 0.001) exists between α∆φ (and the parabola minimum associates with an additional inter-Family or universal quantum increment value, αmin). Ratios of α∆φ typically align within ± 0.5% of simple common fractions (95% CI).

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Matching Pursuit-Based Time-Variant Bispectral Analysis and its Application to Biomedical Signals

Abstract: Results confirm that MGT-based bispectral analysis provides significant benefits for the analysis of QPC in biomedical signals.

Cited by 17 publications

References 43 publications

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

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

Different Time-Frequency Distribution Patterns of Somatosensory Evoked Potentials in Dual- and Single-Level Spinal Cord Compression

A New Approach to High-Order Electroencephalogram Phase Analysis Details the Mathematical Mechanisms of Central Nervous System Impulse Encoding

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