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Koppert M.M.J.,Foundation Epilepsy Institute of the Netherlands SEIN | Kalitzin S.,SEIN | Lopes Da Silva F.,University of Amsterdam | Lopes Da Silva F.,University of Lisbon | Viergever M.A.,University Utrecht
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS | Year: 2012

A phenomenological neural network model with bi-stable oscillatory units is used to model up- and down-states. These states have been observed in vivo in biological neuronal systems and feature oscillatory, limit cycle type of behavior in the up-states. A network is formed by a set of interconnected units. Two different types of network layouts are considered in this work: networks with hierarchical connections and hubs and networks with random connections. The phase coherence between the different units is analyzed and compared to the connectivity distance between nodes. In addition the connectivity degree of a node is associated to the average phase coherence with all other units. The results show that we may be able to identify the set of hubs in a network based on the phase coherence estimates between the different nodes. If the network is very dense or randomly connected, the underlying network structure, however, can not be derived uniquely from the phase coherence. © 2012 IEEE.


Kalitzin S.,Foundation Epilepsy Institute of The Netherlands SEIN | Koppert M.,Foundation Epilepsy Institute of The Netherlands SEIN | Petkov G.,Foundation Epilepsy Institute of The Netherlands SEIN | Velis D.,Foundation Epilepsy Institute of The Netherlands SEIN | And 2 more authors.
Epilepsy and Behavior | Year: 2011

Epilepsy is a pathological condition of the human central nervous system in which normal brain functions are impaired by unexpected transitions to states called seizures. We developed a lumped neuronal model that has the property of switching between two states as a result of intrinsic or extrinsic perturbations, such as noisy fluctuations. In one version of the model, seizure risk is controlled by a single connectivity parameter representing excitatory couplings between two model lumps. We show that this risk can be reconstructed from calculation of the cross-covariance between the activities of the two neural populations during the nonictal phase. In a second simulation sequence, we use a system of 10 interconnected lumps with randomly generated connectivity matrices. We show again that the tendency to develop seizures can be inferred from the cross-covariances calculated during the nonictal states. Our conclusion is that the risk of epileptic transitions in biological systems can be objectively quantified. This article is part of a Supplemental Special Issue entitled The Future of Automated Seizure Detection and Prediction. © 2011 Elsevier Inc.


Kalitzin S.,Foundation Epilepsy Institute of the Netherlands SEIN | Petkov G.,Foundation Epilepsy Institute of the Netherlands SEIN | Velis D.,Foundation Epilepsy Institute of the Netherlands SEIN | Vledder B.,Foundation Epilepsy Institute of the Netherlands SEIN | And 2 more authors.
IEEE Transactions on Biomedical Engineering | Year: 2012

Epilepsy is a neurological disorder characterized by sudden, often unexpected transitions from normal to pathological behavioral states called epileptic seizures. Some of these seizures are accompanied by uncontrolled, often rhythmic movements of body parts when seizure activity propagates to brain areas responsible for the initiation and control of movement. The dynamics of these transitions is, in general, unknown. As a consequence, individuals have to be monitored for long periods in order to obtain sufficient data for adequate diagnosis and to plan therapeutic strategy. Some people may require long-term care in special units to allow for timely intervention in case seizures get out of control. Our goal is to present a method by which a subset of motor seizures can be detected using only remote sensing devices (i.e., not in contact with the subject) such as video cameras. These major motor seizures (MMS) consist of clonic movements and are often precursors of generalized tonic-clonic (convulsive) seizures, sometimes leading to a condition known as status epilepticus, which is an acute life-threatening event. We propose an algorithm based on optical flow, extraction of global group transformation velocities, and band-pass temporal filtering to identify occurrence of clonic movements in video sequences. We show that for a validation set of 72 prerecorded epileptic seizures in 50 people, our method is highly sensitive and specific in detecting video segments containing MMS with clonic movements. © 2012 IEEE.


Kalitzin S.,Foundation Epilepsy Institute of the Netherlands SEIN | Zijlmans M.,SEIN | Petkov G.,SEIN | Velis D.,SEIN | And 4 more authors.
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS | Year: 2012

High frequency oscillations (HFO) in stereo electroencephalographic (SEEG) signals have been recently the focus of attention as biomarkers that can have potential predictive power for the spatial location and possibly the timing of the onset of epileptic seizures. In this work we present a case study where we compare two quantitative paradigms for automated detection of biomarkers, one based on spontaneous SEEG recordings of HFOs and the other using activity induced by direct electrical stimulation (relative Phase Clustering Index algorithm). We compare the performance of these automated methods with manually detected HFO ripples by a trained EEG analyst and explore their potential diagnostic relevance. Intracranial recordings from patients undergoing pre-surgical evaluation are processed with a combination of morphological filtering and the analysis of the auto-correlation function. The results were compared to those obtained by visual inspection and to results from an active paradigm involving stimulation with 20 Hz trains of biphasic pulses. The quantity of HFOs, estimated automatically, or rippleness, was found to correspond to the findings of a trained EEG analyst. The relative phase clustering index (rPCI) obtained using periodic stimulation appeared to be associated with the closeness to the seizure onset zone (SOZ) detected from ictal epochs. The HFO estimates were also indicative for the SOZ but with less specificity. © 2012 IEEE.


Petkov G.,Foundation Epilepsy Institute of the Netherlands SEIN | Kalitzin S.,SEIN | Velis D.,SEIN | Vledder B.,SEIN | And 2 more authors.
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS | Year: 2012

Rationale. The goal of this study is to evaluate the electroencephalographic (EEG) events, prior to clonic phases of epileptic motor seizures. Analyzing video sequences we were able to detect these special phases of motor seizures, by image features. This can be used for an early detection and alerting for these events. In the study we analyzed 42 seizures. Based on collected data we compare the quantitative results from video detection of seizures with the features computed from EEG scalp recordings from about 3 minutes prior to the seizure. We analyze the non-stationary frequency spectrum of the EEG recordings and match it against our automated video detection output in order to investigate possible precursory EEG events. Methods. Video recordings are analyzed by applying optical flow theory, reconstruction of geometrical flow invariants, low and high pass filtering, and suitable normalizations. EEG recordings are processed with use of a Gabor wavelet technique. Comparison is achieved by means of analysis of the cross-correlation function between the derivatives of the Gabor amplitudes and the measure of seizureness produced by our video detection algorithm. Results. In the present study certain ranges of EEG frequencies were found, where electro-graphical events precede clonic phases of clinical motor seizures from 2-8 up to 30-40 seconds. These results could be used for construction of new generation of methods for automated motor seizure detection. © 2012 IEEE.


PubMed | Foundation Epilepsy Institute of The Netherlands SEIN
Type: | Journal: Epilepsy & behavior : E&B | Year: 2011

Epilepsy is a pathological condition of the human central nervous system in which normal brain functions are impaired by unexpected transitions to states called seizures. We developed a lumped neuronal model that has the property of switching between two states as a result of intrinsic or extrinsic perturbations, such as noisy fluctuations. In one version of the model, seizure risk is controlled by a single connectivity parameter representing excitatory couplings between two model lumps. We show that this risk can be reconstructed from calculation of the cross-covariance between the activities of the two neural populations during the nonictal phase. In a second simulation sequence, we use a system of 10 interconnected lumps with randomly generated connectivity matrices. We show again that the tendency to develop seizures can be inferred from the cross-covariances calculated during the nonictal states. Our conclusion is that the risk of epileptic transitions in biological systems can be objectively quantified. This article is part of a Supplemental Special Issue entitled The Future of Automated Seizure Detection and Prediction.


Koppert M.M.,Foundation Epilepsy Institute of The Netherlands SEIN
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference | Year: 2012

A phenomenological neural network model with bi-stable oscillatory units is used to model up- and down-states. These states have been observed in vivo in biological neuronal systems and feature oscillatory, limit cycle type of behavior in the up-states. A network is formed by a set of interconnected units. Two different types of network layouts are considered in this work: networks with hierarchical connections and hubs and networks with random connections. The phase coherence between the different units is analyzed and compared to the connectivity distance between nodes. In addition the connectivity degree of a node is associated to the average phase coherence with all other units. The results show that we may be able to identify the set of hubs in a network based on the phase coherence estimates between the different nodes. If the network is very dense or randomly connected, the underlying network structure, however, can not be derived uniquely from the phase coherence.


PubMed | Foundation Epilepsy Institute of The Netherlands SEIN
Type: Journal Article | Journal: IEEE transactions on bio-medical engineering | Year: 2012

Epilepsy is a neurological disorder characterized by sudden, often unexpected transitions from normal to pathological behavioral states called epileptic seizures. Some of these seizures are accompanied by uncontrolled, often rhythmic movements of body parts when seizure activity propagates to brain areas responsible for the initiation and control of movement. The dynamics of these transitions is, in general, unknown. As a consequence, individuals have to be monitored for long periods in order to obtain sufficient data for adequate diagnosis and to plan therapeutic strategy. Some people may require long-term care in special units to allow for timely intervention in case seizures get out of control. Our goal is to present a method by which a subset of motor seizures can be detected using only remote sensing devices (i.e., not in contact with the subject) such as video cameras. These major motor seizures (MMS) consist of clonic movements and are often precursors of generalized tonic-clonic (convulsive) seizures, sometimes leading to a condition known as status epilepticus, which is an acute life-threatening event. We propose an algorithm based on optical flow, extraction of global group transformation velocities, and band-pass temporal filtering to identify occurrence of clonic movements in video sequences. We show that for a validation set of 72 prerecorded epileptic seizures in 50 people, our method is highly sensitive and specific in detecting video segments containing MMS with clonic movements.

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