Automatic wavelet-based assessment of behavioral sleep using multichannel electrocorticography in rats

Automatic sleep-wake classification and Parkinson's disease recognition using multifeature fusion with support vector machine

AIMS

Sleep disturbance is a prevalent nonmotor symptom of Parkinson's disease (PD), however, assessing sleep conditions is always time-consuming and labor-intensive. In this study, we performed an automatic sleep-wake state classification and early diagnosis of PD by analyzing the electrocorticography (ECoG) and electromyogram (EMG) signals of both normal and PD rats.

METHODS

The study utilized ECoG power, EMG amplitude, and corticomuscular coherence values extracted from normal and PD rats to construct sleep-wake scoring models based on the support vector machine algorithm. Subsequently, we incorporated feature values that could act as diagnostic markers for PD and then retrained the models, which could encompass the identification of vigilance states and the diagnosis of PD.

RESULTS

Features extracted from occipital ECoG signals were more suitable for constructing sleep-wake scoring models than those from frontal ECoG (average Cohen's kappa: 0.73 vs. 0.71). Additionally, after retraining, the new models demonstrated increased sensitivity to PD and accurately determined the sleep-wake states of rats (average Cohen's kappa: 0.79).

CONCLUSION

This study accomplished the precise detection of substantia nigra lesions and the monitoring of sleep-wake states. The integration of circadian rhythm monitoring and disease state assessment has the potential to improve the efficacy of therapeutic strategies considerably.

A simple and fast ANN-based method of studying slow-wave sleep microstructure in freely moving rats

Electroencephalography (EEG) is a common technique for measuring brain activity. Artificial Neuronal Networks (ANNs) can provide valuable insights into the brain dynamics of humans and animals. We built a simple and fast shallow ANN-based solution for sleep recognition in EEGs recorded in freely moving rats. The ANN was constructed using open-source software and truncated to one formula with empirically defined weight coefficients. The optimization of the ANN model's performance (i.e., post-processing) relied on a probability-related approach to sleep microstructure. This approach could be a good way to analyze large datasets. In the current dataset, the slow-wave sleep was recognized with the sensitivity of 0.91 and the specificity of 0.98. The optimal model performance achieved with minimum sleep duration of 80-90 s and sleep interruption of 14-18 s. Our results suggest the following fundamental issues. First, 14-18 s sleep interruptions might be the archetypal micro-arousals in rats. Second, slow-wave sleep in rats might be built up of a set of sleep "building blocks" lasting 80-90 s.

Analysis two types of K complexes on the human EEG based on classical continuous wavelet transform

In our work, we compare EEG time-frequency features for two types of K-complexes detected in volunteers performing the monotonous psychomotor test with their eyes closed. Type I K-complexes preceded spontaneous awakenings, while after type II K-complexes, subjects continued to sleep at least for 10 s after. The total number of K-complexes in the group of 18 volunteers was 646, of which of which type I K-complexes was 150 and type II K-complexes was 496. Time-frequency analysis was performed using continuous wavelet transform. EEG wavelet spectral power was averaged upon several brain zones for each of the classical frequency ranges (slow wave, δ, θ, α, β₁, β₂, γ bands). The low-frequency oscillatory activity ( δ-band) preceding type I K-complexes was asymmetrical and most prominent in the left hemisphere. Statistically significant differences were obtained by averaging over the left and right hemispheres, as well as projections of the motor area of the brain, p<0.05. The maximal differences between the types I and II of K-complexes were demonstrated in δ-, θ-bands in the occipital and posterior temporal regions. The high amplitude of the motor cortex projection response in β₂-band, [20;30] Hz, related to the sensory-motor modality of task in monotonous psychomotor test. The δ-oscillatory activity preceding type I K-complexes was asymmetrical and most prominent in the left hemisphere may be due to the important role of the left hemisphere in spontaneous awakening from sleep during monotonous work, which is an interesting issue for future research.

Spike-Wave Seizures, NREM Sleep and Micro-Arousals in WAG/Rij Rats with Genetic Predisposition to Absence Epilepsy: Developmental Aspects

The current study was done in Wistar Albino Glaxo Rijswijk (WAG/Rij) rats, which are genetically prone to develop spontaneous spike-wave discharges (SWDs) and are widely used as a genetic model of absence epilepsy. Here, we examined functional links between sleep and spike-wave epilepsy in aging WAG/Rij rats using advanced techniques of EEG analysis. SWDs, periods of NREM sleep and micro-arousals were automatically detected in three-channel epidural EEG recorded in freely moving WAG/Rij rats consequently at the age 5, 7 and 9 months. We characterized the developmental profile of spike-wave epilepsy in drug-naïve WAG/Rij rats and defined three epi-phenotypes—severe, mild and minor epilepsy. Age-related changes of SWDs were associated with changes in NREM sleep. Several signs of NREM sleep fragmentation were defined in epileptic WAG/Rij rats. It seems that spike-wave epilepsy per se promotes micro-arousals during NREM sleep. However, subjects with a higher number of micro-arousals (and NREM sleep episodes) at the age of 5 months were characterized by a reduction of SWDs between 5 and 7 months of age.

Sleep Disturbances in Rats With Genetic Pre-disposition to Spike-Wave Epilepsy (WAG/Rij)

Wistar Albino Glaxo Rijswijk (WAG/Rij) rats are widely used in basic and pre-clinical studies as a valid genetic model of absence epilepsy. Adult WAG/Rij rats exhibit generalized 8–10-Hz spike-wave discharges (SWDs) in the electroencephalogram. SWDs are known to result from thalamocortical circuit dysfunction, and this implies an intimate relationship between slow-wave EEG activity, sleep spindles, and SWDs. The present mini review summarizes relevant research on sleep-related disturbances associated with spike-wave epilepsy in WAG/Rij rats in the domain of slow-wave sleep EEG and microarousals. It also discusses enhancement of the intermediate stage of sleep. In general, sleep EEG studies provide important information about epileptogenic processes related to spike-wave epilepsy.