Search results for "Seizure detection"

showing 3 items of 3 documents

Absence Seizure Detection Algorithm for Portable EEG Devices

2021

Absence seizures are generalized nonmotor epileptic seizures with abrupt onset and termination. Transient impairment of consciousness and spike-slow wave discharges (SWDs) in EEG are their characteristic manifestations. This type of seizure is severe in two common pediatric syndromes: childhood (CAE) and juvenile (JAE) absence epilepsy. The appearance of low-cost, portable EEG devices has paved the way for long-term, remote monitoring of CAE and JAE patients. The potential benefits of this kind of monitoring include facilitating diagnosis, personalized drug titration, and determining the duration of pharmacotherapy. Herein, we present a novel absence detection algorithm based on the propert…

Electroencephalographyportable devicewavelets03 medical and health sciencesEpilepsy0302 clinical medicineChildhood absence epilepsymedicineEEGRC346-429Portable EEG030304 developmental biology0303 health sciencesmedicine.diagnostic_testdetectorbusiness.industryBrief Research Reportmedicine.diseaseAbsence seizureNeurologySeizure detectionchildhood absence epilepsyFalse detectionAbrupt onsetNeurology. Diseases of the nervous systemNeurology (clinical)businessAlgorithm030217 neurology & neurosurgeryFrontiers in Neurology
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One Dimensional Convolutional Neural Networks for Seizure Onset Detection Using Long-term Scalp and Intracranial EEG

2021

Epileptic seizure detection using scalp electroencephalogram (sEEG) and intracranial electroencephalogram (iEEG) has attracted widespread attention in recent two decades. The accurate and rapid detection of seizures not only reflects the efficiency of the algorithm, but also greatly reduces the burden of manual detection during long-term electroencephalogram (EEG) recording. In this work, a stacked one-dimensional convolutional neural network (1D-CNN) model combined with a random selection and data augmentation (RS-DA) strategy is proposed for seizure onset detection. Firstly, we segmented the long-term EEG signals using 2-sec sliding windows. Then, the 2-sec interictal and ictal segments w…

intracranial electroencephalogram (iEEG)convolutional neural networks (CNN).signaalinkäsittelyscalp electroencephalogram (sEEG)epilepsyseizure detectionsignaalianalyysineuroverkotEEGepilepsia
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One and Two Dimensional Convolutional Neural Networks for Seizure Detection Using EEG Signals

2021

Deep learning for the automated detection of epileptic seizures has received much attention during recent years. In this work, one dimensional convolutional neural network (1D-CNN) and two dimensional convolutional neural network (2D-CNN) are simultaneously used on electroencephalogram (EEG) data for seizure detection. Firstly, using sliding windows without overlap on raw EEG to obtain the definite one-dimension time EEG segments (1D-T), and continuous wavelet transform (CWT) for 1D-T signals to obtain the two-dimension time-frequency representations (2D-TF). Then, 1D-CNN and 2D-CNN model architectures are used on 1D-T and 2D-TF signals for automatic classification, respectively. Finally, t…

convolutional neural networks (CNN)Computer scienceseizure detection02 engineering and technologyneuroverkotElectroencephalographyConvolutional neural network0202 electrical engineering electronic engineering information engineeringmedicineEEGContinuous wavelet transformSignal processingArtificial neural networkmedicine.diagnostic_testbusiness.industryelectroencephalogram (EEG)signaalinkäsittelyDeep learningtime-frequency representationtideep learningsignaalianalyysi020206 networking & telecommunicationsPattern recognitionkoneoppiminenBenchmark (computing)020201 artificial intelligence & image processingArtificial intelligencebusinessepilepsia
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