Seizure detection with deep neural networks for review of two-channel electroencephalogram.

Ultra-long-term electroencephalographic (EEG) registration using minimally invasive low-channel devices is an emerging technology to assess sporadic seizure events. Highly sensitive automatic seizure detection algorithms are needed for semiautomatic evaluation of these prolonged recordings. We describe the design and validation of a deep neural network for two-channel seizure detection.

Accurate identification of EEG recordings with interictal epileptiform discharges using a hybrid approach: Artificial intelligence supervised by human experts.

Objective: To evaluate the diagnostic performance of artificial intelligence (AI)-based algorithms for identifying the presence of interictal epileptiform discharges (IEDs) in routine (20-min) electroencephalography (EEG) recordings.

Methods: We evaluated two approaches: a fully automated one and a hybrid approach, where three human raters applied an operational IED definition to assess the automated detections grouped into clusters by the algorithms. We used three previously developed AI algorithms: Encevis, SpikeNet, and Persyst.

Deep learning for estimation of functional brain maturation from EEG of premature neonates.

EEG monitoring of early brain function and development in neonatal intensive care units may help to identify infants with high risk of serious neurological impairment and to assess brain maturation for evaluation of neurodevelopmental progress. Automated analysis of EEG data makes continuous evaluation of brain activity fast and accessible. A convolutional neural network (CNN) for regression of EEG maturational age of premature neonates from marginally preprocessed serial EEG recordings is proposed.

Automatic ictal onset source localization in presurgical epilepsy evaluation.

Objective: To test the diagnostic accuracy of a new automatic algorithm for ictal onset source localization (IOSL) during routine presurgical epilepsy evaluation following STARD (Standards for Reporting of Diagnostic Accuracy) criteria.

Methods: We included 28 consecutive patients with refractory focal epilepsy (25 patients with temporal lobe epilepsy (TLE) and 3 with extratemporal epilepsy) who underwent resective epilepsy surgery. Ictal EEG patterns were analyzed with a novel automatic IOSL algorithm.

High density wireless EEG prototype: Design and evaluation against reference equipment.

A high density wireless electroencephalographic (EEG) platform has been designed. It is able to record up to 64 EEG channels with electrode to tissue impedance (ETI) monitoring. The analog front-end is based on two kinds of low power ASICs implementing the active electrodes and the amplifier.

Spatial correlation based artifact detection for automatic seizure detection in EEG.

Automatic EEG-processing systems such as seizure detection systems are more and more in use to cope with the large amount of data that arises from long-term EEG-monitorings. Since artifacts occur very often during the recordings and disturb the EEG-processing, it is crucial for these systems to have a good automatic artifact detection. We present a novel, computationally inexpensive automatic artifact detection system that uses the spatial distribution of the EEG-signal and the location of the electrodes to detect artifacts on electrodes.

EpiScan: online seizure detection for epilepsy monitoring units.

An online seizure detection algorithm for long-term EEG monitoring is presented, which is based on a periodic waveform analysis detecting rhythmic EEG patterns and an adaptation module automatically adjusting the algorithm to patient-specific EEG properties. The algorithm was evaluated using 4.300 hours of unselected EEG recordings from 48 patients with temporal lobe epilepsy.