A fast general purpose algorithm for the computation of auto- and cross-correlation integrals from single channel data

Widman, Guido; Lehnertz, Klaus; Jansen, Peter; Meyer, Wolfgang; Burr, W.; Elger, Christian E.

doi:10.1016/s0167-2789(98)00100-6

Cited by 28 publications

(13 citation statements)

References 29 publications

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“…Briefly, data sets were segmented into consecutive and normalized epochs of 30 sec duration (N = 5,190 data points) for which quasistationarity may be assumed (27). After digitally lowpass-filtering the data (cutoff frequency, 40 Hz; 4th order Butterworth characteristic), correlation sums C , (r) and their local derivatives C' , (r) were calculated for embedding dimensions m = 1 to 30 using an optimized Grassberger-Procaccia algorithm (28,29). The (30) was set to one sampling point, and a Theiler cutoff of five sampling points was used to limit autocorrelative effects (31).…”

Section: Estimation Of L*mentioning

confidence: 99%

Spatial Distribution of Neuronal Complexity Loss in Neocortical Lesional Epilepsies

et al. 2000

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Summary:Purpose: Nonlinear EEG analysis is valuable in characterizing the spatiotemporal dynamics of the epileptogenic process in mesial temporal lobe epilepsy. We examined the ability of the measure neuronal complexity loss (L*) to characterize the primary epileptogenic area of neocortical lesional epilepsies during the interictal state.Methods: Spatial distribution of L* (L* map) was extracted from electrocorticograms (n = 52) recorded during presurgical assessment via subdural 64-contact grid electrodes covering lesions in either frontal, parietal, or temporal neocortex in 15 patients. The exact location of recording contacts on the brain surface was identified by matching a postimplant lateral x-ray of the skull with a postoperatively obtained sagittal MRI scan. Reprojecting L* maps onto the subject's brain surface allowed us to compare the spatial distribution of L* with the resection range of the extended lesionectomy.Results: In each of the six patients who became seizure-free, maximum values of L* were restricted to recording sites coinciding with the area of resection. In contrast, L* maps of most patients who had no benefit from the resection indicated a more widespread extent or the existence of additional, probably autonomous, foci. The mean of Id* values obtained from recording sites outside the area of resection correctly distinguished 13 patients (86.7 %) with respect to seizure outcome. Conclu,sion,s: Relevant information obtained from longlasting interictal electrocorticographic recordings can be compressed to a single L* map that contributes to a spatial characterization of the primary epileptogenic area. In neocortical lesional epilepsies, L* allows for identification and characterization of epileptogenic activity and thus provides an additional diagnostic tool for presurgical assessment.

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Section: Estimation Of L*mentioning

confidence: 99%

Spatial Distribution of Neuronal Complexity Loss in Neocortical Lesional Epilepsies

et al. 2000

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“…The measures calculated here comprise (cf. Chapter 2.6.3) the statistical moments up to fourth order, the relative and absolute power of the δ, θ, α, β, and ␥ band, an estimate of an effective correlation dimension (using an improved algorithm proposed by Widman et al, 1998), the largest Lyapunov exponent, indices derived from the cross correlation function, the nonlinear interdependencies, and phase synchronization indices based on both the Hilbert and the wavelet transform, respectively. For the bivariate measures, the number of non-redundant channel combinations amounted to 4753.…”

Section: Resultsmentioning

confidence: 99%

“…One of the main challenges in nonlinear time series analysis are the time-consuming algorithms. Although a number of improvements have been proposed to efficiently reconstruct high-dimensional state spaces (see Hegger et al, 1999 for an overview), estimate dimensions (Grassberger, 1990;Toledo et al, 1997;Lai and Lerner, 1998;Widman et al, 1998;Sprott and Rowlands, 2001), Lyapunov exponents (von Bremen et al, 1997;Oiwa and Fiedler-Ferrara, 1998a,b), and entropies (Corana and Rolando, 1995), along with efficient techniques to search for neighbors in high-dimensional state spaces (Schreiber, 1995;Merkwirth et al, 2000), real time applications are nevertheless limited by the number of time series M (i.e. the number of recording channels) and by the number of data points N in each time series.…”

Section: Introductionmentioning

confidence: 99%

A distributed computing system for multivariate time series analyses of multichannel neurophysiological data

Müller

Osterhage

Sowa

et al. 2006

Journal of Neuroscience Methods

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“…In Reference [3], it was shown that by calculating e ective correlation dimension D * 2 , changes in brain electrical activity could be characterized and used for predicting the onset of an epileptic seizure. Since the computational complexity in determining the e ective correlation dimension is high [4], a group of workstations was used to compute it in Reference [5]. In order to build a portable system that could be used for online prediction of epileptic seizures, a compact, high-speed processor is needed.…”

Section: Introductionmentioning

confidence: 99%

A mixed‐mode polynomial‐type CNN for analysing brain electrical activity in epilepsy

Laiho

Paasio

Kananen

et al. 2002

Circuit Theory & Apps

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SUMMARYIn this paper, a mixed-mode polynomial-type cellular neural network (CNN) for analysing brain electrical activity in epilepsy is presented. The principles and design characteristics of this application are overviewed. The main di erence of the requirements of this application compared to conventional CNN realizations is that in addition to linear, cells are also coupled with polynomial-type couplings. A mixedmode CNN is shown to be suitable for the realization of a polynomial-type CNN in the brain electrical activity analysis application. In a mixed-mode CNN multiplication is done in analog domain, whereas the integration and storage are digital. Suitability of di erent integration methods for cell level realization are studied and cell and network level design of a mixed-mode CNN is described. First-, secondand third-order polynomial feedback terms are included in the cell and Heun's integration method is used. In order to reduce the cell count, the array is designed so that it can process input data that has been divided into blocks. The whole input data is stored in parallel with the cells so that all input=output operations during processing are local. Cell structure is shown along with register connections between edge cells of the network. Analog power consumption and computing speed are estimated by HSPICE simulations using 0:25 m digital CMOS process parameters. The die area of a network with 2 × 72 cells with 36 layers in each was determined by drawing the layout.

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A fast general purpose algorithm for the computation of auto- and cross-correlation integrals from single channel data

Cited by 28 publications

References 29 publications

Spatial Distribution of Neuronal Complexity Loss in Neocortical Lesional Epilepsies

Spatial Distribution of Neuronal Complexity Loss in Neocortical Lesional Epilepsies

A distributed computing system for multivariate time series analyses of multichannel neurophysiological data

A mixed‐mode polynomial‐type CNN for analysing brain electrical activity in epilepsy

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