Identifying Seizure Onset Zone From the Causal Connectivity Inferred Using Directed Information

Malladi, Rakesh; Kalamangalam, Giridhar P.; Tandon, Nitin; Aazhang, Behnaam

doi:10.1109/jstsp.2016.2601485

Cited by 43 publications

(83 citation statements)

References 48 publications

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“…However, bias is reduced by considering the same neighbour for one of the marginal entropies estimator and for joint entropy estimator (which means considering a different k for estimation in marginal and joint spaces). With this procedure, the last term of equation (13) will be partially cancelled, when subtracted in identity (14).…”

Section: B Nearest Neighbours Methodsmentioning

confidence: 99%

Estimation of Transfer Entropy between Discrete and Continuous Random Processes

Assis

2018

JCIS

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Abstract-Transfer entropy is a measure of causality that has been widely applied and one of its identities is the sum of mutual information terms. In this article we evaluate two existing methods of mutual information estimation in the specific application of detecting causality between a discrete random process and a continuous random process: binning method and nearest neighbours method. Simulated examples confirm, in the overall scenario, that the nearest neighbours method detects causality more reliably than the binning method.

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Section: B Nearest Neighbours Methodsmentioning

confidence: 99%

Estimation of Transfer Entropy between Discrete and Continuous Random Processes

Assis

2018

JCIS

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“…Finally, for each node in the graph, the sum of the incoming weights is subtracted from the sum of outgoing weights, and we rank the nodes (processes) according to this metric. In [11], [12], [15] this approach was denoted by net-flow, and was shown to be effective for ranking nodes in a causal influence graph. In the case that all the weights are equal, net-flow ranks according to the degree centrality of the nodes.…”

Section: Problem Formulationmentioning

confidence: 99%

Ranking causal influence of financial markets via directed information graphs

Diamandis

Murin

Goldsmith

2018

2018 52nd Annual Conference on Information Sciences and Systems (CISS)

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A non-parametric method for ranking stock indices according to their mutual causal influences is presented. Under the assumption that indices reflect the underlying economy of a country, such a ranking indicates which countries exert the most economic influence in an examined subset of the global economy. The proposed method represents the indices as nodes in a directed graph, where the edges' weights are estimates of the pair-wise causal influences, quantified using the directed information functional. This method facilitates using a relatively small number of samples from each index. The indices are then ranked according to their net-flow in the estimated graph (sum of the incoming weights subtracted from the sum of outgoing weights). Daily and minute-by-minute data from nine indices (three from Asia, three from Europe and three from the US) were analyzed. The analysis of daily data indicates that the US indices are the most influential, which is consistent with intuition that the indices representing larger economies usually exert more influence. Yet, it is also shown that an index representing a small economy can strongly influence an index representing a large economy if the smaller economy is indicative of a larger phenomenon. Finally, it is shown that while interregion interactions can be captured using daily data, intra-region interactions require more frequent samples.

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“…Additionally, we have modeled seizure onset networks using nonlinear quantitative measures (directed information and mutual information) applied to intracranial recordings at seizure onset. 43 The seizure onset zone in MTLE is hypothesized to be distributed among the hippocampus, amygdala, entorhinal cortex, and thalamus. 44 Some studies have reported epileptogenic regions in mesial temporal lobe structures including the hippocampus and amygdala 18 and identified strong coupling between mesial structures, insula, and inferior, middle, and superior temporal gyrus.…”

Section: Epileptogenic Network Of Mtle Patients Using Intracranial mentioning

confidence: 99%

“…43 The seizure onset zone in MTLE is hypothesized to be distributed among the hippocampus, amygdala, entorhinal cortex, and thalamus. This suggests that these structures can be strategically modulated during interictal periods to chronically alter the network and decrease epileptiform activity.…”

mentioning

confidence: 99%