Brain electrical activity in epilepsy: characterization of the spatio-temporal dynamics with cellular neural networks based on a correlation dimension analysis

We advance our approach of analyzing the dynamics of interacting complex systems with the nonlinear dynamics of interacting nonlinear elements. We replace the widely used lattice-like connection topology of cellular neural networks (CNN) by complex topologies that include both short-and long-ranged connections. With an exemplary time-resolved analysis of asymmetric nonlinear interdependences between the seizure generating area and its immediate surrounding we provide first evidence for complex CNN connection topologies to allow for a faster network optimization together with an improved approximation accuracy of directed interactions.

show abstract

“…29,30 All calculations were performed using our distributed computing system, 31 and network simulations were performed with Conedy. …”

Section: Optimization and Validationmentioning

confidence: 99%

Measuring Directed Interactions Using Cellular Neural Networks With Complex Connection Topologies

Dickten

Elger

Lehnertz

2013

Recent Advances in Predicting and Preventing Epileptic Seizures

View full text Add to dashboard Cite

show abstract

“…Briefly, we performed all simulations on a software CNN (cf. [22]) implemented within our distributed computing system [23]. We consider a homogeneous two dimensional lattice of M x = M y = 64 cells, and the state equation for cell (i, j) reads:…”

Section: Estimating the Strength Of Generalized Synchronization Wimentioning

confidence: 99%

A CNN-based synchronization analysis for epileptic seizure prediction: Inter- and intraindividual generalization properties

Krug

Elger

Lehnertz

2008

2008 11th International Workshop on Cellular Neural Networks and Their Applications

View full text Add to dashboard Cite

We investigate the generalization capability of our recently proposed CNN-based approach to measure the strength of generalized synchronization in EEG recordings from epilepsy patients. With an in-sample optimization on short-lasting EEG data taken from two recording sites of a single patient we obtain a CNN with polynomial-type templates that allows us to approximate the strength of generalized synchronization in continuous long-lasting multichannel EEG recordings from this patient at a high accuracy. In an out-of-sample study we use the same CNN to analyze days of multichannel EEG data from other patients and observe that the strength of generalized synchronization between different brain regions in different patients can be approximated with a sufficient accuracy. These inter-and intraindividual generalization properties render CNN highly attractive for the development of miniaturized seizure prediction devices.

show abstract

“…[22][23][24][25][26][27]) aim at a better understanding of neural dynamics related to epileptic seizure generation; many of them using methods from signal processing, complex systems theory and system biology. Several publications indicate that a possible transition from interictal to ictal states might be detected 625 by a higher-dimensional analysis of brain electrical activity [28][29][30][31][32] but still seizures cannot be anticipated with necessary sensitivity and specificity.…”

Section: Cellular Nonlinear Networkmentioning

confidence: 99%

Toward an autonomous platform for spatio‐temporal EEG‐signal analysis based on cellular nonlinear networks

Gollas

Niederhofer

Tetzlaff

2008

Circuit Theory & Apps

View full text Add to dashboard Cite

SUMMARYAlthough in the field of epileptic seizure prediction many spatio-temporal approaches have been carried out, the precursor detection problem remains unsolved up to now. It can be observed that an increasing number of algorithms are developed based on cellular nonlinear networks (CNNs). They are dealing with the extraction of signal features using intracranial EEG recordings in order to detect possible preseizure states. In general, reliable precursor detections cannot be obtained for all treated cases. The performance of these algorithms can be enhanced by adapting them to specific patients. Combining different features in a feature vector in a future seizure anticipation platform may lead to a reliably working seizure prediction system.In this contribution we focus on two different CNN-based algorithms-a nonlinear identification approach and a prediction algorithm. They will be discussed in detail and recently obtained results will be given.

show abstract

Brain electrical activity in epilepsy: characterization of the spatio-temporal dynamics with cellular neural networks based on a correlation dimension analysis

Cited by 18 publications

References 9 publications

Measuring Directed Interactions Using Cellular Neural Networks With Complex Connection Topologies

Measuring Directed Interactions Using Cellular Neural Networks With Complex Connection Topologies

A CNN-based synchronization analysis for epileptic seizure prediction: Inter- and intraindividual generalization properties

Toward an autonomous platform for spatio‐temporal EEG‐signal analysis based on cellular nonlinear networks

Contact Info

Product

Resources

About