Iterative expert-in-the-loop classification of sleep PSG recordings using a hierarchical clustering
Background The classification of sleep signals is a subjective and time consuming task. A
large number of automatic classifiers have been published in the past decade but a sleep …
large number of automatic classifiers have been published in the past decade but a sleep …
A convolutional-recurrent neural network approach to resting-state EEG classification in Parkinson's disease
Background Parkinson's disease (PD) is expected to become more common, particularly
with an aging population. Diagnosis and monitoring of the disease typically rely on the …
with an aging population. Diagnosis and monitoring of the disease typically rely on the …
[HTML][HTML] Epilepsy personal assistant device—A mobile platform for brain state, dense behavioral and physiology tracking and controlling adaptive stimulation
Epilepsy is one of the most common neurological disorders, and it affects almost 1% of the
population worldwide. Many people living with epilepsy continue to have seizures despite …
population worldwide. Many people living with epilepsy continue to have seizures despite …
Ictal autonomic activity recorded via wearable-sensors plus machine learning can discriminate epileptic and psychogenic nonepileptic seizures
A Zsom, WC LaFrance, AS Blum, P Li… - 2019 41st Annual …, 2019 - ieeexplore.ieee.org
Differentiating epileptic seizures (ES) and psychogenic nonepileptic seizures (PNES) is
commonly based on electroencephalogram and concurrent video recordings (vEEG). Here …
commonly based on electroencephalogram and concurrent video recordings (vEEG). Here …
Automatic sleep onset detection using single EEG sensor
Sleep has been shown to be imperative for the health and well-being of an individual. To
design intelligent sleep management tools, such as the music-induce sleep-aid device …
design intelligent sleep management tools, such as the music-induce sleep-aid device …
[HTML][HTML] A machine learning approach involving functional connectivity features to classify rest-EEG psychogenic non-epileptic seizures from healthy controls
Until now, clinicians are not able to evaluate the Psychogenic Non-Epileptic Seizures
(PNES) from the rest-electroencephalography (EEG) readout. No EEG marker can help …
(PNES) from the rest-electroencephalography (EEG) readout. No EEG marker can help …
[HTML][HTML] Artificial neural network trained on smartphone behavior can trace epileptiform activity in epilepsy
A range of abnormal electrical activity patterns termed epileptiform discharges can occur in
the brains of persons with epilepsy. These epileptiform discharges can be monitored and …
the brains of persons with epilepsy. These epileptiform discharges can be monitored and …
Machine‐learning‐derived sleep–wake staging from around‐the‐ear electroencephalogram outperforms manual scoring and actigraphy
KB Mikkelsen, JK Ebajemito… - Journal of sleep …, 2019 - Wiley Online Library
Quantification of sleep is important for the diagnosis of sleep disorders and sleep research.
However, the only widely accepted method to obtain sleep staging is by visual analysis of …
However, the only widely accepted method to obtain sleep staging is by visual analysis of …
Automated sleep staging via parallel frequency-cut attention
Stage-based sleep screening is a widely-used tool in both healthcare and neuroscientific
research, as it allows for the accurate assessment of sleep patterns and stages. In this …
research, as it allows for the accurate assessment of sleep patterns and stages. In this …
[HTML][HTML] Resting-state background features demonstrate multidien cycles in long-term EEG device recordings
Background Longitudinal EEG recorded by implanted devices is critical for understanding
and managing epilepsy. Recent research reports patient-specific, multi-day cycles in device …
and managing epilepsy. Recent research reports patient-specific, multi-day cycles in device …