Assessing the feasibility of digital keypress statistics to detect seizures and capture cognitive impairment in patients with epilepsy: A pilot study.

BACKGROUND: Efficient, non-invasive monitoring may provide a more accurate and comprehensive understanding of seizure frequency and the development of some comorbidities in people with epilepsy. Novel keyboard technology measuring digital keypress statistics has demonstrated its practical value for neurodegenerative diseases including Parkinson's Disease and Dementia. Smartphones integrated into daily life may serve as a low-burden longitudinal monitoring system for patients with epilepsy. OBJECTIVE: This study aimed to assess the feasibility of keyboard statistics as an objective measure of seizure frequency for patients with epilepsy, in addition to tracking differences between cognitively normal and cognitively impaired patients. METHODS: Six adult patients admitted to the Epilepsy Monitoring Unit (EMU) at Mayo Clinic in Rochester, Minnesota were studied. The keyboard was installed on the patient's smartphone. In the EMU, typing statistics were correlated to electroencephalogram (EEG) confirmed seizures. After discharge, participants continued using their keyboards and kept a seizure log. We also analyzed the key press/release times and usage of participants' keyboards for adherence. RESULTS: Keyboard sessions during and after seizures assessed for key press/release differences versus baseline showed no statistically significant difference (p = 0.44). Using one-way ANOVA, cognitive impairment's potential impact on keyboard statistics was explored in patients who had neuropsychological testing (N = 3). Significant differences were found between patients with and without cognitive impairment (p < 0.001). No significant difference was noted between patients with mild intellectual disability and normal cognitive function (p = 0.55).

Automated sleep classification with chronic neural implants in freely behaving canines.

Objective.Long-term intracranial electroencephalography (iEEG) in freely behaving animals provides valuable electrophysiological information and when correlated with animal behavior is useful for investigating brain function.Approach.Here we develop and validate an automated iEEG-based sleep-wake classifier for canines using expert sleep labels derived from simultaneous video, accelerometry, scalp electroencephalography (EEG) and iEEG monitoring. The video, scalp EEG, and accelerometry recordings were manually scored by a board-certified sleep expert into sleep-wake state categories: awake, rapid-eye-movement (REM) sleep, and three non-REM sleep categories (NREM1, 2, 3). The expert labels were used to train, validate, and test a fully automated iEEG sleep-wake classifier in freely behaving canines.Main results. The iEEG-based classifier achieved an overall classification accuracy of 0.878 ± 0.055 and a Cohen's Kappa score of 0.786 ± 0.090. Subsequently, we used the automated iEEG-based classifier to investigate sleep over multiple weeks in freely behaving canines. The results show that the dogs spend a significant amount of the day sleeping, but the characteristics of daytime nap sleep differ from night-time sleep in three key characteristics: during the day, there are fewer NREM sleep cycles (10.81 ± 2.34 cycles per day vs. 22.39 ± 3.88 cycles per night;p< 0.001), shorter NREM cycle durations (13.83 ± 8.50 min per day vs. 15.09 ± 8.55 min per night;p< 0.001), and dogs spend a greater proportion of sleep time in NREM sleep and less time in REM sleep compared to night-time sleep (NREM 0.88 ± 0.09, REM 0.12 ± 0.09 per day vs. NREM 0.80 ± 0.08, REM 0.20 ± 0.08 per night;p< 0.001).Significance.These results support the feasibility and accuracy of automated iEEG sleep-wake classifiers for canine behavior investigations.