Automatic adaptive segmentation of clinical EEGs

Barlow, Joseph J.; Creutzfeldt, O. D.; Michael, D; Houchin, J; Epelbaum, H

doi:10.1016/0013-4694(81)90228-5

Cited by 56 publications

(15 citation statements)

References 4 publications

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“…Segmentation procedures have been previously proposed based on autocorrelation functions of the EEG wave patterns (Barlow et al, 1981;Creutzfeld 220 W.K. Strik et al et al, 1985).…”

Section: Discussionmentioning

confidence: 98%

Larger topographical variance and decreased duration of brain electric microstates in depression

Strik

Dierks

Becker

et al. 1995

J. Neural Transmission

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The topographical configuration of the spontaneous brain electric fields is considered to contain relevant information about the pattern of the generating cortical electrochemical activation and the associated cognitive processes. Space oriented segmentation allows to break down the stream of the spontaneous EEG into brain electric microstates with stable configuration of the fields. It has been shown that the mean duration of the microstates was consistent with the duration of elementary steps of cognitive processes, and that different topographies of the microstates are associated with different cognitive modalities. Space-oriented segmentation was applied to the resting EEG of 22 depressive patients and of 22 controls. The topographical variance was larger, and the most prominent brain electrical microstates of the EEG epochs were significantly shorter in the depressive group than in controls. No differences were found for the shortest microstates. This result cannot be explained by group differences in the frequency domain of the EEG. No topographical differences were found between the microstates of depressives and those of controls. Based on previous results in healthy volunteers during spontaneous cognition and in schizophrenic patients, the findings indicate that formal aspects rather than the modalities of the stream of cognition are altered in depression. Automatic and schematic processing, and attentional deficits as described in depressive patients might account for the finding of less sustained brain electrical microstates.

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“…Segmentation procedures have been previously proposed based on autocorrelation functions of the EEG wave patterns (Barlow et al, 1981;Creutzfeld 220 W.K. Strik et al et al, 1985).…”

Section: Discussionmentioning

confidence: 98%

Larger topographical variance and decreased duration of brain electric microstates in depression

Strik

Dierks

Becker

et al. 1995

J. Neural Transmission

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show abstract

“…Such change-point models have been developed and applied to a variety of data, including stock markets (Chen and Gupta, 1997; Xuan and Murphy, 2007; Koop and Potter, 2004; Hsu, 1977), process control (Aroian and Levene, 1950), disease demographics (Denison and Holmes, 2001), DNA (Liu and Lawrence, 1999; Fearnhead and Liu, 2007), EEG (Bodenstein and Praetorius, 1977; Barlow et al, 1981), NMR (Adams and MacKay, 2007; Fearnhead, 2006), and the bee “waggle dance” (Xuan and Murphy, 2007). However, most of this progress has been restricted to offline inference, which uses the entire data stream (past, present, and future) to infer change-point locations (Smith, 1975; Barry and Hartigan, 1993; Stephens, 1994; Green, 1995; Chib, 1998; Fearnhead, 2006).…”

Section: Introductionmentioning

confidence: 99%

Bayesian Online Learning of the Hazard Rate in Change-Point Problems

2010

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Change-point models are generative models of time-varying data in which the underlying generative parameters undergo discontinuous changes at different points in time known as change points. Changepoints often represent important events in the underlying processes, like a change in brain state reflected in EEG data or a change in the value of a company reflected in its stock price. However, change-points can be difficult to identify in noisy data streams. Previous attempts to identify change-points online using Bayesian inference relied on specifying in advance the rate at which they occur, called the hazard rate (h). This approach leads to predictions that can depend strongly on the choice of h and is unable to deal optimally with systems in which h is not constant in time. In this letter, we overcome these limitations by developing a hierarchical extension to earlier models. This approach allows h itself to be inferred from the data, which in turn helps to identify when change-points occur. We show that our approach can effectively identify change-points in both toy and real data sets with complex hazard rates and how it can be used as an ideal-observermodel for human and animal behavior when faced with rapidly changing inputs. Change-point models are generative models of time-varying data in which the underlying generative parameters undergo discontinuous changes at different points in time known as change points. Changepoints often represent important events in the underlying processes, like a change in brain state reflected in EEG data or a change in the value of a company reflected in its stock price. However, change-points can be difficult to identify in noisy data streams. Previous attempts to identify change-points online using Bayesian inference relied on specifying in advance the rate at which they occur, called the hazard rate (h). This approach leads to predictions that can depend strongly on the choice of h and is unable to deal optimally with systems in which h is not constant in time. In this letter, we overcome these limitations by developing a hierarchical extension to earlier models. This approach allows h itself to be inferred from the data, which in turn helps to identify when change-points occur. We show that our approach can effectively identify change-points in both toy and real data sets with complex hazard rates and how it can be used as an ideal-observer model for human and animal behavior when faced with rapidly changing inputs. Disciplines Medicine and Health Sciences

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“…Adecision about the boundary were made if a sharp change in at least one of 10 filter coefficients was observed (Duquesnoy 1976, cit. The principle procedures of the EEG adaptive segmentation based on the autoregressive models of a rather low order were first developed by Bodenstein and Praetorius [26] and then in various modifications were successfully used by other authors [27,9,165,287,20,73] (see also [19] for a review of earlier works). More recently, the most advanced technique for the EEG simulation, linear extrapolation, was applied for the EEG segmentation.…”

Section: Parametrie Eeg Segmentation Based On Autoregressive Modelsmentioning

confidence: 99%

Non-Parametric Statistical Diagnosis

Brodsky

Darkhovsky

2000

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Automatic adaptive segmentation of clinical EEGs

Cited by 56 publications

References 4 publications

Larger topographical variance and decreased duration of brain electric microstates in depression

Larger topographical variance and decreased duration of brain electric microstates in depression

Bayesian Online Learning of the Hazard Rate in Change-Point Problems

Non-Parametric Statistical Diagnosis

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