Automatic Sleep-Arousal Detection with Single-Lead EEG Using Stacking Ensemble Learning.

Poor-quality sleep substantially diminishes the overall quality of life. It has been shown that sleep arousal serves as a good indicator for scoring sleep quality. However, patients are conventionally asked to perform overnight polysomnography tests to collect their physiological data, which are used for the manual judging of sleep arousals. Even worse, not only is this process time-consuming and cumbersome, the judgment of sleep-arousal events is subjective and differs widely from expert to expert. Therefore, this work focuses on designing an automatic sleep-arousal detector that necessitates only a single-lead electroencephalogram signal. Based on the stacking ensemble learning framework, the automatic sleep-arousal detector adopts a meta-classifier that stacks four sub-models: one-dimensional convolutional neural networks, recurrent neural networks, merged convolutional and recurrent networks, and random forest classifiers. This meta-classifier exploits both advantages from deep learning networks and conventional machine learning algorithms to enhance its performance. The embedded information for discriminating the sleep-arousals is extracted from waveform sequences, spectrum characteristics, and expert-defined statistics in single-lead EEG signals. Its effectiveness is evaluated using an open-accessed database, which comprises polysomnograms of 994 individuals, provided by PhysioNet. The improvement of the stacking ensemble learning over a single sub-model was up to 9.29%, 7.79%, 11.03%, 8.61% and 9.04%, respectively, in terms of specificity, sensitivity, precision, accuracy, and area under the receiver operating characteristic curve.

Neuronal oscillations in Golgi cells and Purkinje cells are accompanied by decreases in Shannon information entropy.

Neuronal oscillations have been shown to contribute to the function of the cerebral cortex by coordinating the neuronal activities of distant cortical regions via a temporal synchronization of neuronal discharge patterns. This can occur regardless whether these regions are linked by cortico-cortical pathways or not. Less is known concerning the role of neuronal oscillations in the cerebellum. Golgi cells and Purkinje cells are both principal cell types in the cerebellum. Purkinje cells are the sole output cells of the cerebellar cortex while Golgi cells contribute to information processing at the input stage of the cerebellar cortex. Both cell types have large cell bodies, as well as dendritic structures, that can generate large currents. The discharge patterns of both these cell types also exhibit oscillations. In view of the massive afferent information conveyed by the mossy fiber-granule cell system to different and distant areas of the cerebellar cortex, it is relevant to inquire the role of cerebellar neuronal oscillations in information processing. In this study, we compared the discharge patterns of Golgi cells and Purkinje cells in conscious rats and in rats anesthetized with urethane. We assessed neuronal oscillations by analyzing the regularity in the timing of individual spikes within a spike train by using autocorrelograms and fast-Fourier transform. We measured the differences in neuronal oscillations and the amount of information content in a spike train (defined by Shannon entropy processed per unit time) in rats under anesthesia and in conscious, awake rats. Our findings indicated that anesthesia caused more prominent neuronal oscillations in both Golgi cells and Purkinje cells accompanied by decreases in Shannon information entropy in their spike trains.

Effects of dopamine D2 agonist quinpirole on neuronal activity of anterior cingulate cortex and striatum in rats.

RATIONALE: The influence of acute D2 agonist quinpirole on locomotor activity has been effectively characterized. However, few studies have addressed the dynamic changes in neuronal activity of the anterior cingulate cortex (ACC) and striatum (STR), two crucial regions for cognitive and motor functions, after quinpirole administration. OBJECTIVE: This study was conducted in order to acquire detailed information on the evoked activity of the neurons in the ACC and STR after acute quinpirole administration. METHODS: Multichannel electrophysiological recording was used for tracking neuronal activity in the ACC and STR of urethane-anesthetized rats after administration of saline or 0.05 or 0.5 mg/kg quinpirole. RESULTS: In contrast to the responses to saline, quinpirole dose-dependently increased the ratio of neurons, the activity of which was inhibited in the ACC and STR. By examining the ensemble neuronal activities of inhibition-responded neurons, there was no significant activity difference among the "treatments" (saline and low- and high-dose quinpirole), the "periods" (the duration of 0-15 and 16-45 min after i.v. injection), and the interaction between "treatments" and "periods." Regarding activation-responded neurons, however, there was a significant "periods" difference in both ACC and STR, and the activity of 16-45 min was significantly higher than the activity of 0-15 min after high-dose quinpirole administration in ACC (p < 0.05) and STR (p < 0.001). CONCLUSION: Dose-dependent ACC and STR neuronal responses to quinpirole may offer a possible mechanism for understanding the locomotor responses to quinpirole in behaving rats. The late excitatory effect of high-dose quinpirole in the STR further suggests that this region would be critical for the activation of locomotor activity.