Multiway analysis of epilepsy tensors

Acar, Evrim; Aykut-Bingöl, Canan; Bingol, Haluk O.; Bro, Rasmus; Yener, Bülent

doi:10.1093/bioinformatics/btm210

Cited by 231 publications

(214 citation statements)

References 17 publications

(26 reference statements)

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“…The analysis of ictal EEG recordings (scalp or intracranial) is a gold standard for definition of localization of sn epileptic seizure origin. Several linear (Parra, Spence, Gerson, & Sajda, 2005) and nonlinear methods (Acar, Aykut-Bingol, Bingol, Bro, & Yener, 2007) for analysis of epileptic EEG recordings as well as multi-way arrays models (Miwakeichi et al, 2004) have been used to understand the complex structure of epileptic seizure and localize seizure origin. Table 1 shows a number of automated epileptic seizure detection methods found in the literature which is evaluated using the same dataset ).…”

Section: Automated Epileptic Seizure Analysismentioning

confidence: 99%

Automated Epileptic Seizure Detection Methods: A Review Study

Tzallas¹,

Tsipouras²,

Tsalikakis³

et al. 2012

Epilepsy - Histological, Electroencephalographic and Psychological Aspects

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Section: Automated Epileptic Seizure Analysismentioning

confidence: 99%

Automated Epileptic Seizure Detection Methods: A Review Study

Tzallas¹,

Tsipouras²,

Tsalikakis³

et al. 2012

Epilepsy - Histological, Electroencephalographic and Psychological Aspects

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“…Multi-way arrays, known as tensors, arise in many different application fields, for instance sales analysis (Cerf et al 2008), web mining (Kolda et al 2005;Acar et al 2006;Jaschke et al 2006), neuroscience (Beckmann and Smith 2005), and computational biology (Zhao and Zaki 2005;Baranzini et al 2004;Ji et al 2006;Acar et al 2007). The goal of tensor clustering is to partition each dimension into a pre-defined number of clusters such that the corresponding multi-way clusters are as homogeneous as possible.…”

Section: Related Workmentioning

confidence: 99%

Multi-way set enumeration in weight tensors

2010

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The analysis of n-ary relations receives attention in many different fields, for instance biology, web mining, and social studies. In the basic setting, there are n sets of instances, and each observation associates n instances, one from each set. A common approach to explore these n-way data is the search for n-set patterns, the n-way equivalent of itemsets. More precisely, an n-set pattern consists of specific subsets of the n instance sets such that all possible associations between the corresponding instances are observed in the data. In contrast, traditional itemset mining approaches consider only two-way data, namely items versus transactions. The n-set patterns provide a higher-level view of the data, revealing associative relationships between groups of instances. Here, we generalize this approach Learn (2011) 82: 123-155 in two respects. First, we tolerate missing observations to a certain degree, that means we are also interested in n-sets where most (although not all) of the possible associations have been recorded in the data. Second, we take association weights into account. In fact, we propose a method to enumerate all n-sets that satisfy a minimum threshold with respect to the average association weight. Technically, we solve the enumeration task using a reverse search strategy, which allows for effective pruning of the search space. In addition, our algorithm provides a ranking of the solutions and can consider further constraints. We show experimental results on artificial and real-world datasets from different domains.

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“…In addition, unlike SVD, it is essentially unique under mild conditions. Therefore, it is naturally well suited for the analysis of data sets constituted by observations of a function of multiple discrete indices, as encountered in signal processing [9,10,11], data mining [12] and biomedical engineering [13] ; see [8,14] for other examples.…”

Section: Introductionmentioning

confidence: 99%

Performance estimation for tensor CP decomposition with structured factors

Boizard

Boyer

Favier

et al. 2015

2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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The Canonical Polyadic tensor decomposition (CPD), also known as Candecomp/Parafac, is very useful in numerous scientific disciplines. Structured CPDs, i.e. with Toeplitz, circulant, or Hankel factor matrices, are often encountered in signal processing applications. As subsequently pointed out, specialized algorithms were recently proposed for estimating the deterministic parameters of structured CP decompositions. A closed-form expression of the Cramér-Rao bound (CRB) is derived, related to the problem of estimating CPD parameters, when the observed tensor is corrupted with an additive circular i.i.d. Gaussian noise. This CRB is provided for arbitrary tensor rank and sizes. Finally, the proposed CRB expression is used to asses the statistical efficiency of the existing algorithms by means of simulation results in the cases of third-order tensors having three circulant factors on one hand, and an Hankel factor on the other hand.

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Multiway analysis of epilepsy tensors

Cited by 231 publications

References 17 publications

Automated Epileptic Seizure Detection Methods: A Review Study

Automated Epileptic Seizure Detection Methods: A Review Study

Multi-way set enumeration in weight tensors

Performance estimation for tensor CP decomposition with structured factors

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