A feedback control systems view of epileptic seizures

Tsakalis, Konstantinos; Chakravarthy, Niranjan; Sabesan, Shivkumar; Iasemidis, Leonidas D.; Pardalos, Pãnos M.

doi:10.1007/s10559-006-0087-2

Cited by 33 publications

(18 citation statements)

References 24 publications

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“…This issue is clearly critical for a seizure detection algorithm coupled to an automated intervention system; typically, such a detection algorithm has been applied to intracranial EEG recordings (Peters et al, 2001; Tsakalis et al, 2006; Founta et al, 2007). A scalp EEG-based seizure detection algorithm is most often used for rapid off-line review of long-term EEG recordings or for online detection to alert health care staff when a seizure occurs, which could greatly benefit from a detector with a low rate of false detections resulting in less unnecessary work for the staff.…”

Section: Discussionmentioning

confidence: 99%

Assessment of a scalp EEG-based automated seizure detection system

Kelly

Shiau

Kern

et al. 2010

Clinical Neurophysiology

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Objective-The purpose of this study was to evaluate and validate an offline, automated scalp EEGbased seizure detection system and to compare its performance to commercially available seizure detection software.Methods-The test seizure detection system, IdentEvent™, was developed to enhance the efficiency of post-hoc long-term EEG review in epilepsy monitoring units. It translates multi-channel scalp EEG signals into multiple EEG descriptors and recognizes ictal EEG patterns. Detection criteria and thresholds were optimized in 47 long-term scalp EEG recordings selected for training (47 subjects, ~3653 hours with 141 seizures). The detection performance of IdentEvent was evaluated using a separate test dataset consisting of 436 EEG segments obtained from 55 subjects (~1200 hours with 146 seizures). Each of the test EEG segments was reviewed by three independent epileptologists and the presence or absence of seizures in each epoch was determined by majority rule. Seizure detection sensitivity and false detection rate were calculated for IdentEvent as well as for the comparable detection software (Persyst's Reveal ® , version 2008.03.13, with three parameter settings). Bootstrap re-sampling was applied to establish the 95% confidence intervals of the estimates and for the performance comparison between two detection algorithms.Results-The overall detection sensitivity of IdentEvent was 79.5% with a false detection rate (FDR) of 2 per 24 hours, whereas the comparison system had 80.8%, 76%, and 74% sensitivity using its three detection thresholds (perception score) with FDRs of 13, 8, and 6 per 24 hours, respectively. Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Bootstrap 95% confidence intervals of the performance difference revealed that the two detection systems had comparable detection sensitivity, but IdentEvent generated a significantly (p < 0.05) smaller FDR. NIH Public AccessConclusions-The study validates the performance of the IdentEvent™ .seizure detection system.Significance-With comparable detection sensitivity, an improved false detection rate makes the automated seizure detection software more useful in clinical practice.

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Section: Discussionmentioning

confidence: 99%

Assessment of a scalp EEG-based automated seizure detection system

Kelly

Shiau

Kern

et al. 2010

Clinical Neurophysiology

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“…The referential channel was decided at the clinical sites, but was usually placed at a location between Cz and Pz, as recommended by the American Clinical Neurophysiology Society [15]. In order to reduce the effects from muscle and movement artifacts, each of the 16 EEG signals were band-pass filtered with a low cut = 1 Hz and high cut = 20 Hz, which covers the frequency ranges of most of the ictal EEG patterns classified in [16]. …”

Section: Methodsmentioning

confidence: 99%

“…This pattern usually consists of multiple EEG channels each with a period (seconds to minutes) of continuous repeated waveforms, typically between 2 to 20 Hz in scalp EEG depending on the type of ictal discharge [16]. Because PMRS can be used to detect signals with high regularity, it was used as the primary seizure detector in this seizure detection algorithm.…”

Section: Methodsmentioning

confidence: 99%

Signal regularity-based automated seizure detection system for scalp EEG monitoring

et al. 2010

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The purpose of the present study was to build a clinically useful automated seizure detection system for scalp EEG recordings. To achieve this, a computer algorithm was designed to translate complex multichannel scalp EEG signals into several dynamical descriptors, followed by the investigations of their spatiotemporal properties that relate to the ictal (seizure) EEG patterns as well as to normal physiologic and artifact signals. This paper describes in detail this novel seizure detection algorithm and reports its performance in a large clinical dataset.

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“…The objective is to provide appropriate external feedback stimulation to maintain "normal" pyramidal neuron subpopulation firing-rates, that cannot be achieved by the pathological internal feedback. We investigate a feedback control technique where the stimulation signal is a function of the system state, instead of using predefined control stimulation signals such as biphasic pulse waveforms [1]- [3].…”

Section: Seizure Controlmentioning

confidence: 99%

A feedback systems approach to modeling neural firing-rate homeostasis

Chakravarthy

Sabesan

Spanias

et al. 2007

2007 46th IEEE Conference on Decision and Control

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We consider a cortical neural population model in an effort to understand the basic mechanisms that regulate and maintain "normal" neural firing-rate despite changes in the cortical input level. Towards this goal, we have postulated the existence of internal feedback structures that functionally model firing-rate homeostasis via the balance of excitation and inhibition. In particular, normal internal feedback action maintains normal firing-rate by regulating the strength of excitatory input from pyramidal neurons in other cortical populations, and the strength of inhibitory input from interneurons. We observe that a pathology in the internal feedback that regulates the inhibitory input can lead to seizure-like high amplitude oscillations. These arise from two mechanisms, namely, excessive specific recurrent excitation between cortical populations, and excessive non-specific excitatory input. Further, we develop an external closed-loop control technique where the controller acts to achieve the operational objective of maintaining normal firing-rate. In addition to that, the external controller is also successful in avoiding the occurrence of "seizures". The results of this analysis are consistent with recent experimental observations in the epileptic brain, and have an interesting physical interpretation and implications for the treatment of disorders such as epilepsy.

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A feedback control systems view of epileptic seizures

Cited by 33 publications

References 24 publications

Assessment of a scalp EEG-based automated seizure detection system

Assessment of a scalp EEG-based automated seizure detection system

Signal regularity-based automated seizure detection system for scalp EEG monitoring

A feedback systems approach to modeling neural firing-rate homeostasis

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