A comparative analysis of sleep spindle characteristics of sleep-disordered patients and normal subjects

Anesthetic spindles serve as EEG markers of the depth variations in anesthesia induced by multifarious general anesthetics in mouse experiments

Background

Mice play a crucial role in studying the mechanisms of general anesthesia. However, identifying reliable EEG markers for different depths of anesthesia induced by multifarious agents remains a significant challenge. Spindle activity, typically observed during NREM sleep, reflects synchronized thalamocortical activity and is characterized by a frequency range of 7-15 Hz and a duration of 0.5-3 s. Similar patterns, referred to as "anesthetic spindles," are also observed in the EEG during general anesthesia. However, the variability of anesthetic spindles across different anesthetic agents and depths is not yet fully understood.

Method

Mice were anesthetized with dexmedetomidine, propofol, ketamine, etomidate, isoflurane, or sevoflurane, and cortical EEG recordings were obtained. EEG signals were bandpass filtered between 0.1 and 60 Hz and analyzed using a custom MATLAB script for spindle detection. Anesthesia depth was assessed based on Guedel's modified stages of anesthesia and the presence of burst suppression in the EEG.

Results

Compared to sleep spindles, anesthetic spindles induced by the different agents exhibited higher amplitudes and longer durations. Isoflurane- and sevoflurane-induced spindles varied with the depth of anesthesia. Spindles associated with etomidate were prominent during induction and light anesthesia, whereas those induced by sevoflurane and isoflurane were more dominant during deep anesthesia and emergence. Post-anesthesia, spindles persisted but ceased more quickly following inhalational anesthesia.

Conclusion

Anesthesia spindle waves reflect distinct changes in anesthesia depth and persist following emergence, serving as objective EEG markers for assessing both anesthesia depth and the recovery process.

Association between sleep microarchitecture and cognition in obstructive sleep apnea

STUDY OBJECTIVES

Obstructive sleep apnea (OSA) increases the risk of cognitive impairment. Measures of sleep microarchitecture from EEG may help identify patients at risk of this complication.

METHODS

Participants with suspected OSA (n = 1142) underwent in-laboratory polysomnography and completed sleep and medical history questionnaires, and tests of global cognition (Montreal Cognitive Assessment, MoCA), memory (Rey Auditory Verbal Learning Test, RAVLT) and information processing speed (Digit-Symbol Coding, DSC). Associations between cognitive scores and stage 2 non-rapid eye movement (NREM) sleep spindle density, power, frequency and %-fast (12-16Hz), odds-ratio product (ORP), normalized EEG power (EEGNP), and the delta:alpha ratio were assessed using multivariable linear regression (MLR) adjusted for age, sex, education, and total sleep time. Mediation analyses were performed to determine if sleep microarchitecture indices mediate the negative effect of OSA on cognition.

RESULTS

All spindle characteristics were lower in participants with moderate and severe OSA (p ≤ .001, vs. no/mild OSA) and positively associated with MoCA, RAVLT, and DSC scores (false discovery rate corrected p-value, q ≤ 0.026), except spindle power which was not associated with RAVLT (q = 0.185). ORP during NREM sleep (ORPNREM) was highest in severe OSA participants (p ≤ .001) but neither ORPNREM (q ≥ 0.230) nor the delta:alpha ratio were associated with cognitive scores in MLR analyses (q ≥ 0.166). In mediation analyses, spindle density and EEGNP (p ≥ .048) mediated moderate-to-severe OSA's negative effect on MoCA scores while ORPNREM, spindle power, and %-fast spindles mediated OSA's negative effect on DSC scores (p ≤ .018).

CONCLUSIONS

Altered spindle activity, ORP and normalized EEG power may be important contributors to cognitive deficits in patients with OSA.

Association between alterations in sleep spindles and cognitive decline in persons with Parkinson's disease

BACKGROUND

Sleep macro and microstructural features have a relevant role for cognition. Although alterations in sleep macrostructure have been reported in persons with neurodegenerative disorders, including Parkinson's disease (PD), it is unknown whether there is a relationship between alterations in microstructure (sleep spindles) and global cognitive deficits in this disease.

OBJECTIVE

To explore the association between the macro and microstructure of sleep (sleep spindles) and the general cognitive state in persons with PD.

METHODS

Thirty-three patients with idiopathic PD underwent a one-night polysomnography (PSG) and a global cognitive assessment using the Montreal Cognitive Assessment (MoCA) test. PSG-based macrostructural sleep values and quantification and spectral estimation of sleep spindles were obtained.

RESULTS

We found increases in total sleep time, latency to rapid eye movement (REM) sleep, and percentage of N1 stage, as well as a decrease in percentage of REM sleep and sleep efficiency compared to values reported in healthy adults. Compared to expected values, a decrease in the number of sleep spindles was found at frontal regions. Participants with cognitive impairment showed an even lower count of sleep spindles, as well as an increase in the amplitude of underlying sigma (12-16 Hz) waves (fast spindles). When exploring MoCA subdomains, we found a consistent relationship between the number and amplitude of sleep spindles and attention capacity.

CONCLUSIONS

Decreased number and increased amplitude of sleep spindles are linked to cognitive impairment in persons with PD, especially in attention capacity. Therefore, sleep spindles characteristics could serve as prognostic indicators of cognitive deterioration in PD.

Automatic epileptic seizure detection based on persistent homology

Epilepsy is a prevalent brain disease, which is quite difficult-to-treat or cure. This study developed a novel automatic seizure detection method based on the persistent homology method. In this study, a Vietoris-Rips (VR) complex filtration model was constructed based on the EEG data. And the persistent homology method was applied to calculate the VR complex filtration barcodes to describe the topological changes of EEG recordings. Afterward, the barcodes as the topological characteristics of EEG signals were fed into the GoogLeNet for classification. The persistent homology is applicable for multi-channel EEG data analysis, where the global topological information is calculated and the features are extracted by considering the multi-channel EEG data as a whole, without the multiple calculations or the post-stitching. Three databases were used to evaluate the proposed approach and the results showed that the approach had high performances in the epilepsy detection. The results obtained from the CHB-MIT Database recordings revealed that the proposed approach can achieve a segment-based averaged accuracy, sensitivity and specificity values of 97.05%, 96.71% and 97.38%, and achieve an event-based averaged sensitivity value of 100% with 1.22 s average detection latency. In addition, on the Siena Scalp Database, the proposed method yields averaged accuracy, sensitivity and specificity values of 96.42%, 95.23% and 97.6%. Multiple tasks of the Bonn Database also showed achieved accuracy of 99.55%, 98.63%, 98.28% and 97.68%, respectively. The experimental results on these three EEG databases illustrate the efficiency and robustness of our approach for automatic detection of epileptic seizure.