Health-Oriented Sleep States: Making Sleep States Reflect Health Conditions.

Study Objectives: The rich information in sleep offers insights into brain function and overall health. The current guidelines for sleep staging by the American Academy of Sleep Medicine (AASM) rely on relatively broad categorizations. These traditional sleep stages are not optimized to reflect health status.

Automated Scoring of Respiratory Events in Sleep With a Single Effort Belt and Deep Neural Networks.

Objective: Automatic detection and analysis of respiratory events in sleep using a single respiratoryeffort belt and deep learning.

Methods: Using 9,656 polysomnography recordings from the Massachusetts General Hospital (MGH), we trained a neural network (WaveNet) to detect obstructive apnea, central apnea, hypopnea and respiratory-effort related arousals. Performance evaluation included event-based analysis and apnea-hypopnea index (AHI) stratification.

Impact of stimulus duration on motor unit thresholds and alternation in compound muscle action potential scans.

Objective: To investigate the impact of stimulus duration on motor unit (MU) thresholds and alternation within compound muscle action potential (CMAP) scans.

Methods: The stimulus duration (0.1, 0.