Analyzing the dynamics of sleep electroencephalographic (EEG) signals with different pathologies using threshold-dependent symbolic entropy

Abstract

Sleep is regulated autonomously with circadian behavior. The sleep disorders greatly impact major sleep disturbances in patients suffered from Parkinson’s disease (PD) and epilepsy affecting sleep at night and increased abnormality of muscles tones during NREM stage. The aim of this research is to quantify the dynamics of different sleep pathologies by applying threshold- dependent symbolic entropy. The threshold-dependent symbolic entropy is applied to distinguish the healthy subjects with sleep disorders such as narcolepsy, sleep behavior disorder (RBD), sleep disordered breathing (SDB), period leg movement (PLM), and nocturnal frontal lobe epilepsy (NFLE) subjects. At certain smaller threshold, the healthy and narcolepsy subjects exbibit higher NCSE values than other sleep disorders showing these subjects are more complex followed by SBD, NFLE, PLM and RDB respectively. To distinguish the healthy subjects from narcolepsy, the highest separation was obtained at threshold 550ms with P-value (3.69e-04), NFLE at threshold 550ms with P-value (2.36e-12), PLM at threshold 550ms with Pvalue (2.24e-06), RBD at threshold 550ms with P-value (8.87e-09) and SDB at threshold 550ms with P-value (0.0012). Likewise, the highest AUC was obtained as AUC=0.9688 to distinguish healthy subjects from NFLE, narcolepsy, PLM and SDB followed by RBD with AUC (0.8438).

KEYWORDS:

• Normalized corrected
• Shannon entropy (NCSE)
• narcolepsy
• physiological
• complexity
• symbolic dynamics
• sleep disorders
• sleep behavior disorder (RBD)

Disclosure statement

No potential conflict of interest was reported by the author(s).

Ethical approval

This article does not contain any studies with human participants performed by any of the authors.