New subdural electrode contacts for intracerebral electroencephalographic recordings: Comparative studies on neural signal recording in vivo

Salam, Muhammad Tariqus; Desgent, Sébastien; Duss, Sandra; Carmant, Lionel; Nguyen, Dang Khoa

doi:10.1109/biocas.2011.6107772

Cited by 6 publications

(7 citation statements)

References 7 publications

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“…The study of epileptic seizure detection and analysis has been carried out with both scalp EEG recordings (EEG) and intracranial EEG recordings (iEEG). Scalp EEG recordings use electrodes placed on the surface of the head at equal distance with the 10-20 system as the most commonly used configuration [20,33]. The iEEG signals use intracranial electrodes placed inside the skull when the clinical, structural and functional data are obtained before implantation to locate the epileptogenicity region in the brain [22].…”

Section: Data Acquisition and Eeg Databasementioning

confidence: 99%

A Recent Investigation on Detection and Classification of Epileptic Seizure Techniques Using EEG Signal

Saminu

Zhang

et al. 2021

Brain Sciences

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The benefits of early detection and classification of epileptic seizures in analysis, monitoring and diagnosis for the realization and actualization of computer-aided devices and recent internet of medical things (IoMT) devices can never be overemphasized. The success of these applications largely depends on the accuracy of the detection and classification techniques employed. Several methods have been investigated, proposed and developed over the years. This paper investigates various seizure detection algorithms and classifications in the last decade, including conventional techniques and recent deep learning algorithms. It also discusses epileptiform detection as one of the steps towards advanced diagnoses of disorders of consciousness (DOCs) and their understanding. A performance comparison was carried out on the different algorithms investigated, and their advantages and disadvantages were explored. From our survey, much attention has recently been paid to exploring the efficacy of deep learning algorithms in seizure detection and classification, which are employed in other areas such as image processing and classification. Hybrid deep learning has also been explored, with CNN-RNN being the most popular.

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Section: Data Acquisition and Eeg Databasementioning

confidence: 99%

A Recent Investigation on Detection and Classification of Epileptic Seizure Techniques Using EEG Signal

Saminu

Zhang

et al. 2021

Brain Sciences

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“…We have previously shown that pure platinum (Pt) or pure Au electrodes provided higher conductivity than commercial electrodes. 22 Therefore, contacts of the mini-grid electrodes are made of pure Pt or Au, and wire connections between the electrodes (thickness, 0.05 mm) and amplifiers are made using the same materials used for electrodes but with 0.1 mm diameter wires.…”

Section: Proposed Novel Intracranial Recording Systemmentioning

confidence: 99%

Subdural porous and notched mini-grid electrodes for wireless intracranial electroencephalographic recordings

Salam¹,

Gelinas²,

Desgent³

et al. 2014

JMDH

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BackgroundIntracranial electroencephalography (EEG) studies are widely used in the presurgical evaluation of drug-refractory patients with partial epilepsy. Because chronic implantation of intracranial electrodes carries a risk of infection, hemorrhage, and edema, it is best to limit the number of electrodes used without compromising the ability to localize the epileptogenic zone (EZ). There is always a risk that an intracranial study may fail to identify the EZ because of suboptimal coverage. We present a new subdural electrode design that will allow better sampling of suspected areas of epileptogenicity with lower risk to patients.MethodImpedance of the proposed electrodes was characterized in vitro using electrochemical impedance spectroscopy. The appearance of the novel electrodes on magnetic resonance imaging (MRI) was tested by placing the electrodes into a gel solution (0.9% NaCl with 14 g gelatin). In vivo neural recordings were performed in male Sprague Dawley rats. Performance comparisons were made using microelectrode recordings from rat cortex and subdural/depth recordings from epileptic patients. Histological examinations of rat brain after 3-week icEEG intracerebral electroencephalography (icEEG) recordings were performed.ResultsThe in vitro results showed minimum impedances for optimum choice of pure gold materials for electrode contacts and wire. Different attributes of the new electrodes were identified on MRI. The results of in vivo recordings demonstrated signal stability, 50% noise reduction, and up to 6 dB signal-to-noise ratio (SNR) improvement as compared to commercial electrodes. The wireless icEEG recording system demonstrated on average a 2% normalized root-mean-square (RMS) deviation. Following the long-term icEEG recording, brain histological results showed no abnormal tissue reaction in the underlying cortex.ConclusionThe proposed subdural electrode system features attributes that could potentially translate into better icEEG recordings and allow sampling of large of areas of epileptogenicity at lower risk to patients. Further validation for use in humans is required.

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“…We have studied the fundamentals facts for these false detections. Primary requirement for a seizure detector is to analyze high quality icEEG recording so that noise does not degrade detection performance [6]. To do so, we increase conductivity of electrodes and wired path in order to improve signal-to-noise ratio [6].…”

Section: Epilepsy Seizure Onset Detectionmentioning

confidence: 99%

“…Primary requirement for a seizure detector is to analyze high quality icEEG recording so that noise does not degrade detection performance [6]. To do so, we increase conductivity of electrodes and wired path in order to improve signal-to-noise ratio [6]. Secondly, we have analyzed electrographic seizure onset of many patients and found optimize detection criteria [1], [4], [5].…”

Section: Epilepsy Seizure Onset Detectionmentioning

confidence: 99%

A Smart Biological Signal-Responsive Focal Drug Delivery System for Treatment of Refractory Epilepsy

Salam

Hamie

Nguyen

2012

Wearable/Wireless Body Sensor Networks for Healthcare Applications

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In this paper, we propose a new biological signal-responsive implantable device that triggers direct an anticonvulsive drug into the epileptogenic zone at electrographic seizure onset. We describe the high-performance seizure-onset detection algorithm, low-power circuit technique and focal drug delivery system. The implantable device is composed of a preamplifier, a signal processor, a seizure detector and a micropump. The device records high quality intracerebral electroencephalographic (icEEG) signals using high conductive electrodes and a low noise preamplifier. The recorded signal is processed continuously using low-power technique to detect onset of seizures accurately. The low-power miniaturized micropump is able to deliver sufficient amount of anticonvulsive drug in a short duration (50µL/sec) to epileptogenic zone. The detection algorithm was validated with Matlab tools and a prototype device was assembled with discrete components in a circular (Ø 40 mm) printed circuit board. The device was validated offline using the icEEG recordings obtained from 3 drug-resistant epilepsy patients. The average seizure detection delay was 10 sec from electrographic seizure onset, well before seizure progression to adjacent functional cortex.

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New subdural electrode contacts for intracerebral electroencephalographic recordings: Comparative studies on neural signal recording in vivo

Cited by 6 publications

References 7 publications

A Recent Investigation on Detection and Classification of Epileptic Seizure Techniques Using EEG Signal

A Recent Investigation on Detection and Classification of Epileptic Seizure Techniques Using EEG Signal

Subdural porous and notched mini-grid electrodes for wireless intracranial electroencephalographic recordings

A Smart Biological Signal-Responsive Focal Drug Delivery System for Treatment of Refractory Epilepsy

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