Neuromodulation Therapy: Nonmedical, Nonsurgical Treatment for Intractable Epilepsy

Lee, Hyang Woon

doi:10.5772/57441

Cited by 3 publications

(3 citation statements)

References 56 publications

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“…Neuromodulation therapy such as vagus nerve stimulation (VNS), deep brain stimulation (DBS) or responsive neurostimulation (RNS) have recently been applied for patients with intractable epilepsy (Howland, 2014 ; Lee, 2014 ). In the closed loop neuromodulation system, which involves combined early seizure detection with automatic cortical stimulation, the accuracy of a seizure detection algorithm is crucial to improve therapeutic efficacy (Liang et al, 2013 ; Howbert et al, 2014 ; Moghim and Corne, 2014 ).…”

Section: Introductionmentioning

confidence: 99%

Early Seizure Detection by Applying Frequency-Based Algorithm Derived from the Principal Component Analysis

Lee

Park

Yang

et al. 2017

Front. Neuroinform.

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The use of automatic electrical stimulation in response to early seizure detection has been introduced as a new treatment for intractable epilepsy. For the effective application of this method as a successful treatment, improving the accuracy of the early seizure detection is crucial. In this paper, we proposed the application of a frequency-based algorithm derived from principal component analysis (PCA), and demonstrated improved efficacy for early seizure detection in a pilocarpine-induced epilepsy rat model. A total of 100 ictal electroencephalographs (EEG) during spontaneous recurrent seizures from 11 epileptic rats were finally included for the analysis. PCA was applied to the covariance matrix of a conventional EEG frequency band signal. Two PCA results were compared: one from the initial segment of seizures (5 sec of seizure onset) and the other from the whole segment of seizures. In order to compare the accuracy, we obtained the specific threshold satisfying the target performance from the training set, and compared the False Positive (FP), False Negative (FN), and Latency (Lat) of the PCA based feature derived from the initial segment of seizures to the other six features in the testing set. The PCA based feature derived from the initial segment of seizures performed significantly better than other features with a 1.40% FP, zero FN, and 0.14 s Lat. These results demonstrated that the proposed frequency-based feature from PCA that captures the characteristics of the initial phase of seizure was effective for early detection of seizures. Experiments with rat ictal EEGs showed an improved early seizure detection rate with PCA applied to the covariance of the initial 5 s segment of visual seizure onset instead of using the whole seizure segment or other conventional frequency bands.

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Section: Introductionmentioning

confidence: 99%

Early Seizure Detection by Applying Frequency-Based Algorithm Derived from the Principal Component Analysis

Lee

Park

Yang

et al. 2017

Front. Neuroinform.

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“…Recently, neuromodulation therapy using various brain stimulation modalities, including deep brain stimulation, vagal nerve stimulation, or external responsive neurostimulation, have been attempted to control intractable seizures. 18 – 22 Those therapies modulate brain functions or pathological states at the entire brain network level, by stimulating the specific brain region to induce changes not only in the stimulated brain regions but also in distant areas that are connected to them through the anatomical or functional brain connectivity. While the neuromodulation therapies have been known as novel and promising treatment methods, demonstrating seizure reduction in about one-third to one-half of patients, 18 – 22 there are still many obstacles for improving the treatment efficacy by finding the optimal stimulation sites or stimulation parameters, or understanding the mechanisms behind the treatment.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence and Computational Approaches for Epilepsy

An¹,

Kang²,

Lee³

2020

J Epilepsy Res

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Studies on treatment of epilepsy have been actively conducted in multiple avenues, but there are limitations in improving its efficacy due to between-subject variability in which treatment outcomes vary from patient to patient. Accordingly, there is a growing interest in precision medicine that provides accurate diagnosis for seizure types and optimal treatment for an individual epilepsy patient. Among these approaches, computational studies making this feasible are rapidly progressing in particular and have been widely applied in epilepsy. These computational studies are being conducted in two main streams: 1) artificial intelligence-based studies implementing computational machines with specific functions, such as automatic diagnosis and prognosis prediction for an individual patient, using machine learning techniques based on large amounts of data obtained from multiple patients and 2) patient-specific modeling-based studies implementing biophysical in-silico platforms to understand pathological mechanisms and derive the optimal treatment for each patient by reproducing the brain network dynamics of the particular patient per se based on individual patient’s data. These computational approaches are important as it can integrate multiple types of data acquired from patients and analysis results into a single platform. If these kinds of methods are efficiently operated, it would suggest a novel paradigm for precision medicine.

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“…Although antiepileptic drug medications and part surgical treatment have been widely used for epilepsy management, 25%-30% of patients remain intractable to conventional treatment methods [4]. Brain stimulation has recently been introduced for intractable epilepsy patients, such as responsive neurostimulation, in which a closed loop system consists of an early seizure detector and an automatic electrical stimulator to abort seizures acutely [5]. Because this type of system often requires implantable devices, especially in the brain, they have a limited power source and the batteries need to be changed regularly under brain surgery.…”

Section: Introductionmentioning

confidence: 99%

Performance Estimation of an Implantable Epileptic Seizure Detector with a Low-power On-chip Oscillator

Kim¹,

Choi²,

Choi³

et al. 2015

Journal of Biomedical Engineering Research

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Implantable closed-loop epilepsy controllers require ideally both accurate epileptic seizure detection and low power consumption. On-chip oscillators can be used in implantable devices because they consume less power than other oscillators such as crystal oscillators. In this study, we investigated the tolerable error range of a lower power on-chip oscillator without losing the accuracy of seizure detection. We used 24 ictal and 14 interictal intracranial electroencephalographic segments recorded from epilepsy surgery patients. The performance variations with respect to oscillator frequency errors were estimated in terms of specificity, modified sensitivity, and detection timing difference of seizure onset using Generic Osorio Frei Algorithm. The frequency errors of on-chip oscillators were set at ± 10% as the worst case. Our results showed that an oscillator error of ± 10% affected both specificity and modified sensitivity by less than 3%. In addition, seizure onsets were detected with errors earlier or later than without errors and the average detection timing difference varied within less than 0.5 s range. The results suggest that on-chip oscillators could be useful for low-power implantable devices without error compensation circuitry requiring significant additional power. These findings could help the design of closed-loop systems with a seizure detector and automated stimulators for intractable epilepsy patients.

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Neuromodulation Therapy: Nonmedical, Nonsurgical Treatment for Intractable Epilepsy

Cited by 3 publications

References 56 publications

Early Seizure Detection by Applying Frequency-Based Algorithm Derived from the Principal Component Analysis

Early Seizure Detection by Applying Frequency-Based Algorithm Derived from the Principal Component Analysis

Artificial Intelligence and Computational Approaches for Epilepsy

Performance Estimation of an Implantable Epileptic Seizure Detector with a Low-power On-chip Oscillator

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