S-134 | Analyzing Sleep Dynamics Using Permutation Entropy: A Novel Approach to Hypnogram Visualization

Tools Development and Open Source Neuroscience
Author: Cristina Daiana Duarte | Email: cristinaduarte88@gmail.com

Cristina Daiana Duarte^1°, Marianela Pacheco^1°2°3°, Alejandro Wainselboim^4°,  Claudio Augusto Delrieux^3°, Gustavo Gasaneo^1°

^1° Instituto de Física del Sur, Departamento de Física, Universidad Nacional del Sur (UNS) – CONICET, 8000 Bahía Blanca, Argentina.
^2° Doctorado en Neurociencias, Universidad Nacional de Córdoba (UNC), Córdoba, Argentina.
^3° Instituto de Ciencias e Ingeniería de la Computación (ICIC), Universidad Nacional del Sur – CONICET, Bahía Blanca, Argentina.
^4° Lingüística y Neurobiología Experimental del Lenguaje (LyNEL), INCIHUSA-CCT Mendoza

Sleep is a biobehavioral state characterized by changes in the electrical activity of the brain. It has the important adaptive function of contributing to homeostasis in mammals. It is a vital neurological process, necessary for rest and replenishment of the body’s energy reserves. Normal human sleep is divided into non-rapid eye movement (NREM) and rapid eye movement (REM) stages, which alternate throughout a night of sleep. NREM sleep is subdivided into stages of different depth. Alterations in the quality, quantity and pattern of sleep can cause sleep disorders, the prevalence of which is worrying and significantly affects the general population.

A hypnogram, a common tool for analyzing sleep macrostructure and diagnosing sleep disorders, graphically represents sleep stages over time. It is usually derived from polysomnography (PSG), a comprehensive sleep study that records various physiological parameters such as brain activity, eye movements, heart rate, and muscle activity.

In this work, we present an innovative method to visualize hypnograms using PSG data. By
By calculating Bandt and Pompe permutation entropy from brain electrical signals recorded during the night, we provide new insights into sleep cycles. This approach not only captures each sleep cycle, but also reveals the decrease in sleep depth as the night progresses. Finally, we applied this method to compare normal sleep patterns with patterns from a sleep disorder registry.