Acute Seizure Control Efficacy of Multi-Site Closed-Loop Stimulation in a Temporal Lobe Seizure Model

Zheng, Yongte; Jiang, Zifan; Ping, An; Zhang, Fang; Zhu, Junming; Wang, Yueming; Zhu, Wentao; Xu, Kedi

doi:10.1109/tnsre.2019.2894746

Cited by 23 publications

(9 citation statements)

References 43 publications

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“…Rats' behaviors were monitored by cameras and LFPs were recorded by our custom-made responsive neural stimulator; the detailed designs were described in our previous work ( 32 – 34 ) and depicted in Supplementary Figure 1 . Specifically, an interface programmed on the computer was used in an online pattern, which contains neural signal recording function, real-time neural signal display interface, auto seizure detection function based on Ostu's algorithm ( 35 ), and real-time neural stimulation function to satisfy all needs of experiments in this work.…”

Section: Methodsmentioning

confidence: 99%

“…Trigger stimulation, a sequence of biphasic square pulses with 200 μA amplitude, 1 ms pulse width, and 60 Hz frequency lasting for 5 s, was manually delivered to CA3 in the hippocampus to evoke acute seizures. The acute seizure onset was identified when the value of time-domain features including line-length (LL), average amplitude (AMP), and slope (SLP) all exceeded the threshold which was calculated by OSTU algorithm in all detected channels ( 34 ). Each acute seizure induction stimulation was performed with an interval longer than 10 min.…”

Section: Methodsmentioning

confidence: 99%

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Combination of Matching Responsive Stimulations of Hippocampus and Subiculum for Effective Seizure Suppression in Temporal Lobe Epilepsy

et al. 2021

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Responsive neural stimulation (RNS) is considered a promising neural modulation therapy for refractory epilepsy. Combined stimulation on different targets may hold great promise for improving the efficacy of seizure control since neural activity changed dynamically within associated brain targets in the epileptic network. Three major issues need to be further explored to achieve better efficacy of combined stimulation: (1) which nodes within the epileptogenic network should be chosen as stimulation targets? (2) What stimulus frequency should be delivered to different targets? and (3) Could the efficacy of RNS for seizure control be optimized by combined different stimulation targets together? In our current study, Granger causality (GC) method was applied to analyze epileptogenic networks for finding key targets of RNS. Single target stimulation (100 μA amplitude, 300 μs pulse width, 5s duration, biphasic, charge-balanced) with high frequency (130 Hz, HFS) or low frequency (5 Hz, LFS) was firstly delivered by our lab designed RNS systems to CA3, CA1, subiculum (SUB) of hippocampi, and anterior nucleus of thalamus (ANT). The efficacy of combined stimulation with different groups of frequencies was finally assessed to find out better combined key targets with optimal stimulus frequency. Our results showed that stimulation individually delivered to SUB and CA1 could shorten the average duration of seizures. Different stimulation frequencies impacted the efficacy of seizure control, as HFS delivered to CA1 and LFS delivered to SUB, respectively, were more effective for shortening the average duration of electrographic seizure in Sprague-Dawley rats (n = 3). Moreover, the synchronous stimulation of HFS in CA1 combined with LFS in SUB reduced the duration of discharge significantly in rats (n = 6). The combination of responsive stimulation at different targets may be an inspiration to optimize stimulation therapy for epilepsy.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Combination of Matching Responsive Stimulations of Hippocampus and Subiculum for Effective Seizure Suppression in Temporal Lobe Epilepsy

et al. 2021

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“…Seizures were analyzed in hippocampus, CA1, CA3, M1 channels of rats using multisite closed loop stimulation method (MSCLS). 17,18 Efficacy of stimulation and seizure control mechanisms are studied. Seizure forecasting accurate algorithm is developed in Bou Assi et al 19 using precision call genetic algorithm with SVM classifier.…”

Section: Various Brain Diseasesmentioning

confidence: 99%

“…However, cross specialty collaboration must be encouraged for better research in these areas. Seizures were analyzed in hippocampus, CA1, CA3, M1 channels of rats using multisite closed loop stimulation method (MSCLS) 17,18 . Efficacy of stimulation and seizure control mechanisms are studied.…”

Section: Related Workmentioning

confidence: 99%

Artifact cleaning of motor imagery EEG by statistical features extraction using wavelet families

Nagabushanam

George

Dolly

et al. 2020

Circuit Theory & Apps

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Electroencephalogram (EEG) and its sub-bands represent electrical pattern of human brain. EEG signal contains transient components, spikes, and different types of artifacts due to eye blinking, movement of the person, anxiety, and so forth, during EEG capture. Wavelet transforms are powerful mathematical tool for sampling approximation to get clean EEG. It also helps in filtering, sampling, interpolation, noise reduction, signal approximation and signal enhancement, and feature extraction. In this paper, we have analyzed artifact cleaning via PSD graphs and statistical features extracted from motor imagery EEG-like standard deviation variance. For this, we considered 19 channels EEG signal and applied orthogonal Daubechies wavelet, bi-orthogonal rbio wavelet and Coifman wavelets to check the better performance of different wavelets. Coifman wavelet uses both scaling function and vanishing moments for sampling approximation and hence give smooth sampling compared to rbio and Daubechies wavelet transforms. Coif is a compactly supported wavelet system which also helps in smooth sampling approximations than other wavelets in the state of arts. The detailed coefficients and approximate coefficients can be further used for extracting features from EEG and classification purposes. Artifacts cleaning is thus observed better in coif wavelet analysis compared to other wavelets from the power distributions as power spectral density (PSD) graphs, standard deviation and variance obtained. Matlab R2013b is used for filtering and sampling EEG. Python 2.7 is used for statistical features extraction.

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“…So far, the closed-loop neuro-modulation has become a trend in the treatment of epilepsy [13], and numerous efforts have been made on such topic, such as PID control [4], [14], feedback linearization control [15], fuzzy PID control [16], closed-loop iterative learning control (ILC) based on unscented Kalman filter (UKF) [17] and parameter estimation and control based on particle swarm optimization (PSO) [18]. Above closed-loop neuro-modulation algorithms are effective to some extent.…”

Section: Introductionmentioning

confidence: 99%

Seizure Control by a Learning Type Active Disturbance Rejection Approach

Wei

Xia

et al. 2019

IEEE Access

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Epilepsy is one of the most common neurological disorders. Neuro-modulation becomes a promising way to address it. For an effective modulation, closed-loop mode is necessary but difficult. A control algorithm, which can adjust itself to get desired suppression of epileptic activity, is in great need. In this paper, active disturbance rejection control (ADRC) is utilized for its satisfied disturbance rejection and regulation performance. However, fixed observer parameters are difficult to fit the time-varying electrophysiological signals. Therefore, based on the estimation errors, an iterative learning approach is designed to get the parameters of an extended state observer (ESO). By combining the advantages of ADRC and the iterative learning, a learning type ADRC (LTADRC) is proposed to suppress the high amplitude epileptiform waves generated by the Jansen's neural mass model (NMM). For those variable parameters of an ESO, scalable bandwidths can be obtained to adapt to time-varying disturbance signals. It is of great significance for both ADRC and the neuro-modulation of epilepsy. Simulation results show that, compared with ADRC, much better performance can be obtained. It may provide a promising closed-loop regulation way for epilepsy in clinics. INDEX TERMS ADRC, a learning type ADRC, epilepsy, NMM, closed-loop modulation.

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Acute Seizure Control Efficacy of Multi-Site Closed-Loop Stimulation in a Temporal Lobe Seizure Model

Cited by 23 publications

References 43 publications

Combination of Matching Responsive Stimulations of Hippocampus and Subiculum for Effective Seizure Suppression in Temporal Lobe Epilepsy

Combination of Matching Responsive Stimulations of Hippocampus and Subiculum for Effective Seizure Suppression in Temporal Lobe Epilepsy

Artifact cleaning of motor imagery EEG by statistical features extraction using wavelet families

Seizure Control by a Learning Type Active Disturbance Rejection Approach

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