Quantitative Electroencephalography (EEG) Predicting Acute Neurologic Deterioration in the Pediatric Intensive Care Unit: A Case Series.

Continuous neurologic assessment in the pediatric intensive care unit is challenging. Current electroencephalography (EEG) guidelines support monitoring status epilepticus, vasospasm detection, and cardiac arrest prognostication, but the scope of brain dysfunction in critically ill patients is larger. We explore quantitative EEG in pediatric intensive care unit patients with neurologic emergencies to identify quantitative EEG changes preceding clinical detection.

Optical Flow Estimation Improves Automated Seizure Detection in Neonatal EEG.

Purpose: Existing automated seizure detection algorithms report sensitivities between 43% and 77% and specificities between 56% and 90%. The algorithms suffer from false alarms when applied to neonatal EEG because of the high degree of nurse handling and rhythmic patting used to soothe neonates. Computer vision technology that quantifies movement in real time could distinguish artifactual motion and improve automated neonatal seizure detection algorithms.

Seizure Detection: Interreader Agreement and Detection Algorithm Assessments Using a Large Dataset.

Purpose: To compare the seizure detection performance of three expert humans and two computer algorithms in a large set of epilepsy monitoring unit EEG recordings.

Methods: One hundred twenty prolonged EEGs, 100 containing clinically reported EEG-evident seizures, were evaluated. Seizures were marked by the experts and algorithms.

Semiautomated Spike Detection Software Persyst 13 Is Noninferior to Human Readers When Calculating the Spike-Wave Index in Electrical Status Epilepticus in Sleep.

Purpose: Our objective was to use semiautomatic methods for calculating the spike-wave index (SWI) in electrical status epilepticus in slow-wave sleep (ESES) and to determine whether this calculation is noninferior to human experts (HEs).

Methods: Each HE marked identical 300-second epochs for all spikes and calculated the SWI in sleep EEGs of patients diagnosed with ESES. Persyst 13 was used to mark spikes (high sensitivity setting) in the same 300-second epochs marked by HEs.

Spike detection: Inter-reader agreement and a statistical Turing test on a large data set.

Objective: Compare the spike detection performance of three skilled humans and three computer algorithms.

Methods: 40 prolonged EEGs, 35 containing reported spikes, were evaluated. Spikes and sharp waves were marked by the humans and algorithms.

Seizure detection with automated EEG analysis: a validation study focusing on periodic patterns.

Objective: To evaluate an automated seizure detection (ASD) algorithm in EEGs with periodic and other challenging patterns.

Methods: Selected EEGs recorded in patients over 1year old were classified into four groups: A. Periodic lateralized epileptiform discharges (PLEDs) with intermixed electrical seizures.

Spike separation from EEG/MEG data using morphological filter and wavelet transform.

In the analysis of epileptic electroencephalographic (EEG) and magnetoencephalography (MEG) data, spike separation is diagnostically important because localization of epileptic focus often depends on accurate extraction of spiky activity from the raw data. In this paper, we present a method to automatically extract spikes using the wavelet transform combined with morphological filtering based on a circular structuring element. Our experimental results have shown that this method is highly effective in spike separation.

Data analysis for continuous EEG monitoring in the ICU: seeing the forest and the trees.

Continuous EEG monitoring (CEEG) is a powerful tool for evaluating cerebral function in obtunded and comatose critically ill patients. The ongoing analysis of CEEG data is a major task because of the volume of data generated during monitoring and the need for near real-time interpretation of a patient's EEG patterns. Advances in digital EEG data acquisition, computer processing, data transmission, and data display have made CEEG monitoring in the intensive care unit technically feasible.

Seizure detection: evaluation of the Reveal algorithm.

Objective: The aim of this study is to evaluate an improved seizure detection algorithm and to compare with two other algorithms and human experts.

Methods: 672 seizures from 426 epilepsy patients were examined with the (new) Reveal algorithm which utilizes 3 methods, novel in their application to seizure detection: Matching Pursuit, small neural network-rules and a new connected-object hierarchical clustering algorithm.

Results: Reveal had a sensitivity of 76% with a false positive rate of 0.

Seizure detection: correlation of human experts.

Objective: The description and application of a new, overlap-integral comparison method and the quantification of human vs. human accuracies that can be used as goals for algorithms.

Methods: Four human experts marked ten 8 h electroencephalography (EEG) records from seizure patients.

Evidence for distinct genetic influences on generalized and localization-related epilepsy.

Purpose: Determining the existence of syndrome-specific genetic factors in epilepsy is essential for phenotype definition in genetic linkage studies, and informs research on basic mechanisms. Analysis of concordance of epilepsy syndromes in families has been used to assess shared versus distinct genetic influences on generalized epilepsy (GE) and localization-related epilepsy (LRE). However, it is unclear how the results should be interpreted in relation to specific genetic hypotheses.

Acute stroke syndrome with fixed neurological deficit and false-negative diffusion-weighted imaging.

Diffusion-weighted imaging (DWI) is sensitive for the detection of acute ischemic stroke. However, a negative DWI study of the brain does not always exclude a patient from the possibility of acute cerebral ischemia. The authors report 1 case in which the patient presented with a fixed ischemic neurological deficit (National Institute of Health Stroke Scale score = 22) that included global aphasia, right hemiparesis, and a right visual field neglect.

Continuous EEG monitoring in the intensive care unit.

Continuous EEG (CEEG) monitoring allows uninterrupted assessment of cerebral cortical activity with good spatial resolution and excellent temporal resolution. Thus, this procedure provides a means of constantly assessing brain function in critically ill obtunded and comatose patients. Recent advances in digital EEG acquisition, storage, quantitative analysis, and transmission have made CEEG monitoring in the intensive care unit (ICU) technically feasible and useful.