Expert level of detection of interictal discharges with a deep neural network.

Objective: Deep learning methods have shown potential in automating the detection of interictal epileptiform discharges (IEDs) in electroencephalography (EEG). We compared IED detection using our previously trained deep neural network with a group of experts to assess its potential applicability.

Methods: First, we performed clinical validation on an internal data set.

Comparison of state-of-the-art deep learning architectures for detection of freezing of gait in Parkinson's disease.

Introduction: Freezing of gait (FOG) is one of the most debilitating motor symptoms experienced by patients with Parkinson's disease (PD). FOG detection is possible using acceleration data from wearable sensors, and a convolutional neural network (CNN) is often used to determine the presence of FOG epochs. We compared the performance of a standard CNN for the detection of FOG with two more complex networks, which are well suited for time series data, the MiniRocket and the InceptionTime.

Ultrafast review of ambulatory EEGs with deep learning.

Objective: Interictal epileptiform discharges (IED) are hallmark biomarkers of epilepsy which are typically detected through visual analysis. Deep learning has shown potential in automating IED detection, which could reduce the burden of visual analysis in clinical practice. This is particularly relevant for ambulatory electroencephalograms (EEGs), as these entail longer review times.

Neurophysiology State Dynamics Underlying Acute Neurologic Recovery After Cardiac Arrest.

Background And Objectives: Epileptiform activity and burst suppression are neurophysiology signatures reflective of severe brain injury after cardiac arrest. We aimed to delineate the evolution of coma neurophysiology feature ensembles associated with recovery from coma after cardiac arrest.

Methods: Adults in acute coma after cardiac arrest were included in a retrospective database involving 7 hospitals.

Preservation of thalamocortical circuitry is essential for good recovery after cardiac arrest.

Continuous electroencephalographam (EEG) monitoring contributes to prediction of neurological outcome in comatose cardiac arrest survivors. While the phenomenology of EEG abnormalities in postanoxic encephalopathy is well known, the pathophysiology, especially the presumed role of selective synaptic failure, is less understood. To further this understanding, we estimate biophysical model parameters from the EEG power spectra from individual patients with a good or poor recovery from a postanoxic encephalopathy.

Good vibrations: tactile cueing for freezing of gait in Parkinson's disease.

Background: Cueing strategies can alleviate freezing of gait (FOG) in people with Parkinson's disease (PD). We evaluated tactile cueing delivered via vibrating socks, which has the benefit of not being noticeable to bystanders.

Objective: To evaluate the effect of tactile cueing compared to auditory cueing on FOG.

Early EEG monitoring predicts clinical outcome in patients with moderate to severe traumatic brain injury.

There is a need for reliable predictors in patients with moderate to severe traumatic brain injury to assist clinical decision making. We assess the ability of early continuous EEG monitoring at the intensive care unit (ICU) in patients with traumatic brain injury (TBI) to predict long term clinical outcome and evaluate its complementary value to current clinical standards. We performed continuous EEG measurements in patients with moderate to severe TBI during the first week of ICU admission.

The Gray Area of Freezing of Gait Annotation: A Guideline and Open-Source Practical Tool.

Background: Freezing of gait, a disabling episodic symptom, is difficult to assess as the exact begin- and endpoint of an episode is not easy to specify. This hampers scientific and clinical progress. The current golden standard is video annotation by two independent raters.

Outcome Prediction of Postanoxic Coma: A Comparison of Automated Electroencephalography Analysis Methods.

Background: To compare three computer-assisted quantitative electroencephalography (EEG) prediction models for the outcome prediction of comatose patients after cardiac arrest regarding predictive performance and robustness to artifacts.

Methods: A total of 871 continuous EEGs recorded up to 3 days after cardiac arrest in intensive care units of five teaching hospitals in the Netherlands were retrospectively analyzed. Outcome at 6 months was dichotomized as "good" (Cerebral Performance Category 1-2) or "poor" (Cerebral Performance Category 3-5).

Treating Rhythmic and Periodic EEG Patterns in Comatose Survivors of Cardiac Arrest.

Background: Whether the treatment of rhythmic and periodic electroencephalographic (EEG) patterns in comatose survivors of cardiac arrest improves outcomes is uncertain.

Methods: We conducted an open-label trial of suppressing rhythmic and periodic EEG patterns detected on continuous EEG monitoring in comatose survivors of cardiac arrest. Patients were randomly assigned in a 1:1 ratio to a stepwise strategy of antiseizure medications to suppress this activity for at least 48 consecutive hours plus standard care (antiseizure-treatment group) or to standard care alone (control group); standard care included targeted temperature management in both groups.

Effects of targeted temperature management at 33 °C vs. 36 °C on comatose patients after cardiac arrest stratified by the severity of encephalopathy.

Objectives: To assess neurological outcome after targeted temperature management (TTM) at 33 °C vs. 36 °C, stratified by the severity of encephalopathy based on EEG-patterns at 12 and 24 h.

Design: Post hoc analysis of prospective cohort study.

Predicting Neurological Outcome From Electroencephalogram Dynamics in Comatose Patients After Cardiac Arrest With Deep Learning.

Objective: Most cardiac arrest patients who are successfully resuscitated are initially comatose due to hypoxic-ischemic brain injury. Quantitative electroencephalography (EEG) provides valuable prognostic information. However, prior approaches largely rely on snapshots of the EEG, without taking advantage of temporal information.

Predicting neurological outcome in comatose patients after cardiac arrest with multiscale deep neural networks.

Objective: Electroencephalography (EEG) is an important tool for neurological outcome prediction after cardiac arrest. However, the complexity of continuous EEG data limits timely and accurate interpretation by clinicians. We develop a deep neural network (DNN) model to leverage complex EEG trends for early and accurate assessment of cardiac arrest coma recovery likelihood.

Modulation of the Somatosensory Evoked Potential by Attention and Spinal Cord Stimulation.

Spinal Cord Stimulation (SCS) is a last-resort treatment for patients with intractable chronic pain in whom pharmacological and other treatments have failed. Conventional tonic SCS is accompanied by tingling sensations. More recent stimulation protocols like burst SCS are not sensed by the patient while providing similar levels of pain relief.

Machine learning for detection of interictal epileptiform discharges.

The electroencephalogram (EEG) is a fundamental tool in the diagnosis and classification of epilepsy. In particular, Interictal Epileptiform Discharges (IEDs) reflect an increased likelihood of seizures and are routinely assessed by visual analysis of the EEG. Visual assessment is, however, time consuming and prone to subjectivity, leading to a high misdiagnosis rate and motivating the development of automated approaches.

EEG functional connectivity contributes to outcome prediction of postanoxic coma.

Objective: To investigate the additional value of EEG functional connectivity features, in addition to non-coupling EEG features, for outcome prediction of comatose patients after cardiac arrest.

Methods: Prospective, multicenter cohort study. Coherence, phase locking value, and mutual information were calculated in 19-channel EEGs at 12 h, 24 h and 48 h after cardiac arrest.

Efficient use of clinical EEG data for deep learning in epilepsy.

Objective: Automating detection of Interictal Epileptiform Discharges (IEDs) in electroencephalogram (EEG) recordings can reduce the time spent on visual analysis for the diagnosis of epilepsy. Deep learning has shown potential for this purpose, but the scarceness of expert annotated data creates a bottleneck in the process.

Methods: We used EEGs from 50 patients with focal epilepsy, 49 patients with generalized epilepsy (IEDs were visually labeled by experts) and 67 controls.

Dynamic functional connectivity of the EEG in relation to outcome of postanoxic coma.

Objective: Early EEG contains reliable information for outcome prediction of comatose patients after cardiac arrest. We introduce dynamic functional connectivity measures and estimate additional predictive values.

Methods: We performed a prospective multicenter cohort study on continuous EEG for outcome prediction of comatose patients after cardiac arrest.

Detecting multiple sclerosis via breath analysis using an eNose, a pilot study.

In the present study we investigated whether multiple sclerosis (MS) can be detected via exhaled breath analysis using an electronic nose (eNose). The Aeonose(an eNose, The eNose Company, Zutphen, the Netherlands) is a diagnostic test device to detect patterns of volatile organic compounds in exhaled breath. We evaluated whether the Aeonosecan make a distinction between the breath patterns of patients with MS and healthy control subjects.

Relevance of Somatosensory Evoked Potential Amplitude After Cardiac Arrest.

We present relations of SSEP amplitude with neurological outcome and of SSEP amplitude with EEG amplitude in comatose patients after cardiac arrest. This is a analysis of a prospective cohort study in comatose patients after cardiac arrest. Amplitude of SSEP recordings obtained within 48-72 h, and EEG patterns obtained at 12 and 24h after cardiac arrest were related to good (CPC 1-2) or poor (CPC 3-5) outcome at 6 months.

Predicting outcome in patients with moderate to severe traumatic brain injury using electroencephalography.

Background: Better outcome prediction could assist in reliable quantification and classification of traumatic brain injury (TBI) severity to support clinical decision-making. We developed a multifactorial model combining quantitative electroencephalography (qEEG) measurements and clinically relevant parameters as proof of concept for outcome prediction of patients with moderate to severe TBI.

Methods: Continuous EEG measurements were performed during the first 7 days of ICU admission.

Association between somatosensory evoked potentials and EEG in comatose patients after cardiac arrest.

Objective: To analyze the association between SSEP results and EEG results in comatose patients after cardiac arrest, including the added value of repeated SSEP measurements.

Methods: Continuous EEG was measured in 619 patients during the first 3-5 days after cardiac arrest. SSEPs were recorded daily in the first 55 patients, and on indication in later patients.

Outcome Prediction in Postanoxic Coma With Deep Learning.

Objectives: Visual assessment of the electroencephalogram by experienced clinical neurophysiologists allows reliable outcome prediction of approximately half of all comatose patients after cardiac arrest. Deep neural networks hold promise to achieve similar or even better performance, being more objective and consistent.

Design: Prospective cohort study.

Early electroencephalography for outcome prediction of postanoxic coma: A prospective cohort study.

Objective: To provide evidence that early electroencephalography (EEG) allows for reliable prediction of poor or good outcome after cardiac arrest.

Methods: In a 5-center prospective cohort study, we included consecutive, comatose survivors of cardiac arrest. Continuous EEG recordings were started as soon as possible and continued up to 5 days.