Evaluation and Comparison of EEG Traces: Latent Structure in Nonstationary Time Series

West, Mike; Prado, Raquel; Krystal, Andrew D.

doi:10.2307/2669922

Cited by 49 publications

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“…, 12, and examined a decomposition of the latent process {x t } with the time-frequency structure based on the eigenstructure of the TV-AR evolution matrix G t . The basic result of West et al (1999) states that (6.4) where p z,t is the number of pairs of complex characteristic roots of the time t instantaneous AR characteristic polynomial based on δ t , and p y,t is the number of pairs of real characteristic roots (i.e., 2p z,t + p y,t = p). Under certain conditions in West et al (1999), it is shown that the processes {x Figure 6.2 displays the posterior means of the factor latent process {x t } and stochastic variance {w t } for the latent process innovation, as well as the trajectories of estimated characteristic frequency and modulus for the quasiperiodic components.…”

Section: Data and Priorsmentioning

confidence: 99%

“…The convulsive seizure activity drives multichannel EEG traces and a statistical interest here is to model such multivariate time series processes that reflect dynamic relations across EEG channels and time in order to reveal underlying characteristics and effects of ECT. Various classes of dynamic time series models have been studied to explore features of EEG time series (e.g., Kitagawa and Gersch 1996;West et al 1999;Prado et al 2001;Prado 2010a,b;Prado and West 2010). Among them, time-varying parameter autoregressive (TV-AR) models and decompositions Prado et al 2001) reveal considerable changes in the patterns of evolution of time-frequency structure with spectral content, differences and changes in relationships among the channels.…”

Section: Context and Literaturementioning

confidence: 99%

“…The analysis uses 19-channel EEG traces recorded in one seizure of one patient, analyzed in West et al (1999), Prado et al (2001), Prado and West (2010). 19 parallel EEG series are recorded from 19 electrodes of the International 10-20 EEG System, located around and over the patient's scalp.…”

Section: Data and Priorsmentioning

confidence: 99%

“…The original data set is stored with sampling rate at 256Hz to total about 26,000 observations. Following West et al (1999) and Prado et al (2001), the original series are subsampled every sixth observation after removing about 2,000 observations from the beginning (up to a higher amplitude portion of the seizure). The subsampling is a standard practice adopted for computational efficiency and no significant information of the characteristics in the original data is lost .…”

Section: Data and Priorsmentioning

confidence: 99%

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Bayesian Analysis of Latent Threshold Dynamic Models

Nakajima

West

2013

Journal of Business & Economic Statistics

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125

145

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Chapter 1 overviews the idea of the latent threshold approach and outlines the dissertation. Chapter 2 introduces the new approach to dynamic sparsity using latent threshold modeling and also discusses Bayesian analysis and computation for model fitting. Chapter 3 describes latent threshold multivariate models for a wide range of applications in the real data analysis that follows. Chapter 4 provides US and Japanese macroeconomic data analysis using latent threshold VAR models. Chapter 5 analyzes time series of foreign currency exchange rates (FX) using latent threshold dynamic factor models. Chapter 6 provides a study of electroencephalographic iv (EEG) time series using latent threshold factor process models. Chapter 7 develops a new framework of dynamic network modeling for multivariate time series using the latent threshold approach. Finally, Chapter 8 concludes the dissertation with open questions and future works.

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Section: Data and Priorsmentioning

confidence: 99%

Section: Context and Literaturementioning

confidence: 99%

Section: Data and Priorsmentioning

confidence: 99%

Section: Data and Priorsmentioning

confidence: 99%

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Bayesian Analysis of Latent Threshold Dynamic Models

Nakajima

West

2013

Journal of Business & Economic Statistics

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125

145

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“…Proietti (2002) has also provided a brief but thorough account. A brief introductory survey has been provided by West (1997), and an interesting biomedical application has been demonstrated by West et al (1999).…”

Section: σ(T) = Ln S(t) and η(T) = Ln H(t)mentioning

confidence: 99%

Statistical Signal Extraction and Filtering: A Partial Survey

Pollock

2009

Handbook of Computational Econometrics

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A wide variety of techniques have been devised for the purpose of extracting the components of econometric data sequences. The traditional approach, which continues to prevail in the central statistical agencies of many countries, has relied upon filters that have been derived with refernce to the principles of actuarial graduation. Latterly, filters that are grounded in the Wiener-Kolmogorov theory of signal extraction have become prominent in academic research. There are several alternative ways of applying the Wiener-Kolmogorov filters to the data.

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EEG effects of ECT: Implications for rTMS

et al. 2000

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Electroconvulsive therapy (ECT) involves the use of electrical stimulation to elicit a series of generalized tonic–clonic seizures for therapeutic purposes and is the most effective treatment known for major depression. These treatments have significant neurophysiologic effects, many of which are manifest in the electroencephalogram (EEG). The relationship between EEG data and the response to ECT has been studied since the 1940s, but for many years no consistent correlates were found. Recent studies indicate that a number of specific EEG features recorded during the induced seizures (ictal EEG) as well as before and after a course of treatment (interictal EEG) are related to both the therapeutic efficacy and cognitive side effects. Similar to ECT, repetitive transcranial magnetic stimulation (rTMS), which involves focal electromagnetic stimulation of cortical neurons, has also been studied as an antidepressant therapy and also appears to have neurophysiologic effects, although these have not been as fully investigated as is the case with ECT. Given the similarity of these treatments, it is natural to consider whether advances in understanding the electrophysiologic correlates of the ECT response might have implications for rTMS. The present article reviews the literature on the EEG effects of ECT and discusses the implications in terms of the likely efficacy and side effects associated with rTMS in specific anatomic locations, the potential for producing an antidepressant response with rTMS without eliciting seizure activity, eliciting focal seizures with rTMS, and the possibility of using rTMS to focally modulate seizure induction and spread with ECT to optimize treatment. Depression and Anxiety 12:157–165, 2000. © 2000 Wiley‐Liss, Inc.

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Evaluation and Comparison of EEG Traces: Latent Structure in Nonstationary Time Series

Cited by 49 publications

References 0 publications

Bayesian Analysis of Latent Threshold Dynamic Models

Bayesian Analysis of Latent Threshold Dynamic Models

Statistical Signal Extraction and Filtering: A Partial Survey

EEG effects of ECT: Implications for rTMS

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