Evolutionary transfer optimization-based approach for automated ictal pattern recognition using brain signals.

The visual scrutinization process for detecting epileptic seizures (ictal patterns) is time-consuming and prone to manual errors, which can have serious consequences, including drug abuse and life-threatening situations. To address these challenges, expert systems for automated detection of ictal patterns have been developed, yet feature engineering remains problematic due to variability within and between subjects. Single-objective optimization approaches yield less reliable results.

Guidelines of the Indian Society for Sleep Research (ISSR) for Practice of Sleep Medicine during COVID-19.

Background: Sleep services are assigned a non-essential status during COVID-19. The American Academy of Sleep Medicine strongly urges sleep clinicians to continue postponing non-urgent care until a later date, if such a recommendation is made by state officials due to local conditions. At the same time, one cannot ignore the fact that sleep is important for people's health and wellbeing.

Selection of optimum frequency bands for detection of epileptiform patterns.

The significant research effort in the domain of epilepsy has been directed toward the development of an automated seizure detection system. In their usage of the electrophysiological recordings, most of the proposals thus far have followed the conventional practise of employing all frequency bands following signal decomposition as input features for a classifier. Although seemingly powerful, this approach may prove counterproductive since some frequency bins may not carry relevant information about seizure episodes and may, instead, add noise to the classification process thus degrading performance.

Diagnosis and management of narcolepsy in the Indian scenario.

Introduction: The diagnosis and management of narcolepsy in the Indian context needs to be revisited especially in the wake of concerns raised by sleep medicine experts that the entity could be formidably underdiagnosed, as well as undertreated in our setting.

Materials And Methods: The history, clinical records, polysomnographic/multiple sleep latency test data, and treatment records of five hundred consecutive patients attending a dedicated sleep clinic between the years 2013 and 2016 were retrospectively analyzed. The response to treatment measures and improvement in daytime functioning were periodically assessed by personal/telephonic interview and E-mail communication.