Automated sleep detection reveals differences in sleep patterns in an animal model of neocortical epilepsy.

Sleep in epilepsy is best studied in longitudinal preclinical animal models, where state changes can have significant effects on epileptic activities. Voluminous data makes it very difficult to mark sleep stages manually. This demands an automated way to detect sleep and wake states.

Engineering nonlinear epileptic biomarkers using deep learning and Benford's law.

In this study, we designed two deep neural networks to encode 16 features for early seizure detection in intracranial EEG and compared them and their frequency responses to 16 widely used engineered metrics to interpret their properties: epileptogenicity index (EI), phase locked high gamma (PLHG), time and frequency domain Cho Gaines distance (TDCG, FDCG), relative band powers, and log absolute band powers (from alpha, beta, theta, delta, low gamma, and high gamma bands). The deep learning models were pretrained for seizure identification on the time and frequency domains of 1 s, single-channel clips of 127 seizures (from 25 different subjects) using "leave-one-out" (LOO) cross validation. Each neural network extracted unique feature spaces that were interpreted using spectral power modulations before being used to train a Random Forest Classifier (RFC) for seizure identification.

Modeling Nerve Compression in Carpal Tunnel Syndrome.

Nerve function loss can result from a variety of conditions that are either sudden onset like head and spinal cord trauma or slowly develop from chronic pressure as in the case of carpal tunnel syndrome. In either case we see compression ofthe nerve ultimately resulting in axon demyelination and loss of signal conduction. For chronic conditions such as carpal tunnel syndrome, treatments focus on alleviating symptoms.