Filtering noise for synchronised activity in multi-trial electrophysiology data using Wiener and Kalman filters

Zhan, Yang; Guo, Shuixia; Kendrick, Keith M.; Feng, Jianfeng

doi:10.1016/j.biosystems.2008.11.007

Cited by 8 publications

(3 citation statements)

References 15 publications

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“…In MATLAB, data collected at 1000 Hz were downsampled to 500 Hz. The data were detrended using a Kalman filter, and segments containing high-amplitude artifact (> 150 μV) in any channel were excluded from further analysis [22]. Remaining data were broken into 1-s, non-overlapping segments.…”

Section: Methodsmentioning

confidence: 99%

Electroencephalographic spectral power as a marker of cortical function and disease severity in girls with Rett syndrome

Roche

LeBlanc

Levin

et al. 2019

J Neurodevelop Disord

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Background Rett syndrome is a neurodevelopmental disorder caused by a mutation in the X-linked MECP2 gene. Individuals with Rett syndrome typically develop normally until around 18 months of age before undergoing a developmental regression, and the disorder can lead to cognitive, motor, sensory, and autonomic dysfunction. Understanding the mechanism of developmental regression represents a unique challenge when viewed through a neuroscience lens. Are circuits that were previously established erased, and are new ones built to supplant old ones? One way to examine circuit-level changes is with the use of electroencephalography (EEG). Previous studies of the EEG in individuals with Rett syndrome have focused on morphological characteristics, but few have explored spectral power, including power as an index of brain function or disease severity. This study sought to determine if EEG power differs in girls with Rett syndrome and typically developing girls and among girls with Rett syndrome based on various clinical characteristics in order to better understand neural connectivity and cortical organization in individuals with this disorder. Methods Resting state EEG data were acquired from girls with Rett syndrome ( n = 57) and typically developing children without Rett syndrome ( n = 37). Clinical data were also collected for girls with Rett syndrome. EEG power across several brain regions in numerous frequency bands was then compared between girls with Rett syndrome and typically developing children and power in girls with Rett syndrome was compared based on these clinical measures. 1/ ƒ slope was also compared between groups. Results Girls with Rett syndrome demonstrate significantly lower power in the middle frequency bands across multiple brain regions. Additionally, girls with Rett syndrome that are postregression demonstrate significantly higher power in the lower frequency delta and theta bands and a significantly more negative slope of the power spectrum. Increased power in these bands, as well as a more negative 1/ƒ slope, trended with lower cognitive assessment scores. Conclusions Increased power in lower frequency bands is consistent with previous studies demonstrating a “slowing” of the background EEG in Rett syndrome. This increase, particularly in the delta band, could represent abnormal cortical inhibition due to dysfunctional GABAergic signaling and could potentially be used as a marker of severity due to associations with more severe Rett syndrome phenotypes.

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Section: Methodsmentioning

confidence: 99%

Electroencephalographic spectral power as a marker of cortical function and disease severity in girls with Rett syndrome

Roche

LeBlanc

Levin

et al. 2019

J Neurodevelop Disord

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“…In [42], the authors suggest rectifying the EMG signal a priori, which can enhance the muscle activation information but still struggles with high envelope variability [43]. In addition, Kalman and Wiener filter based algorithms have been developed for EMG by many works [44]- [48]. While the traditional Kalman setting has effective tracking when noise is white Gaussian; it provides no meaningful advantage against other methods, since EMG noise is strictly non-white [40].…”

Section: B Envelope Extractionmentioning

confidence: 99%

Nonparametric Extrema Analysis in Time Series for Envelope Extraction, Peak Detection and Clustering

Gokcesu,

Gokcesu

2021

Preprint

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In this paper, we propose a nonparametric approach that can be used in envelope extraction, peak-burst detection and clustering in time series. Our problem formalization results in a naturally defined splitting/forking of the time series. With a possibly hierarchical implementation, it can be used for various applications in machine learning, signal processing and mathematical finance. From an incoming input signal, our iterative procedure sequentially creates two signals (one upper bounding and one lower bounding signal) by minimizing the cumulative L1 drift. We show that a solution can be efficiently calculated by use of a Viterbi-like path tracking algorithm together with an optimal elimination rule. We consider many interesting settings, where our algorithm has near-linear time complexities.

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“…Optimal Kalman filter (KF) and Wiener filter algorithms have been developed for EMG signals by many authors [25][26][27][28][29]. Although the classic KF setting allows achieving a maximum estimation accuracy, noise is required to be white Gaussian with known statistics.…”

Section: Introductionmentioning

confidence: 99%

Analysis and Smoothing of EMG Signal Envelope Using Kalman and UFIR Filtering under Colored Measurement Noise

2019

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This article describes some filtering methods to remove artifacts from the EMG signal envelope. Diverse EMG waveforms are studied using the Kalman filter (KF) and unbiased finite impulse response (UFIR) filter. The filters are developed in discrete-time state-space for Gauss-Markov colored measurement noise (CMN) and termed as cKF and cUFIR. It is shown that a choice of a proper CMN factor allows extracting the EMG waveform envelope with a high robustness. Extensive investigation have shown that the cKF and cUFIR filter are most efficient when the density is low of the motor unit action potential (MUAP) of the EMG and the Hilbert transform is required. Otherwise, when the envelope is well-pronounced and well-shaped with sharp edges due to a high MUAP density, the filters can be applied directly without using the Hilbert transform.

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Filtering noise for synchronised activity in multi-trial electrophysiology data using Wiener and Kalman filters

Cited by 8 publications

References 15 publications

Electroencephalographic spectral power as a marker of cortical function and disease severity in girls with Rett syndrome

Electroencephalographic spectral power as a marker of cortical function and disease severity in girls with Rett syndrome

Nonparametric Extrema Analysis in Time Series for Envelope Extraction, Peak Detection and Clustering

Analysis and Smoothing of EMG Signal Envelope Using Kalman and UFIR Filtering under Colored Measurement Noise

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