Sleep stage classification plays an important role in the diagnosis of sleep-related diseases. However, traditional automatic sleep stage classification is quite challenging because of the complexity associated with the establishment of mathematical models and the extraction of handcrafted features. In addition, the rapid fluctuations between sleep stages often result in blurry feature extraction, which might lead to an inaccurate assessment of electroencephalography (EEG) sleep stages. Hence, we propose an automatic sleep stage classification method based on a convolutional neural network (CNN) combined with the fine-grained segment in multiscale entropy. First, we define every 30 seconds of the multichannel EEG signal as a segment. Then, we construct an input time series based on the fine-grained segments, which means that the posterior and current segments are reorganized as an input containing several segments and the size of the time series is decided based on the scale chosen depending on the fine-grained segments. Next, each segment in this series is individually put into the designed CNN and feature maps are obtained after two blocks of convolution and max-pooling as well as a full-connected operation. Finally, the results from the full-connected layer of each segment in the input time sequence are put into the softmax classifier together to get a single most likely sleep stage. On a public dataset called ISRUC-Sleep, the average accuracy of our proposed method is 92.2%. Moreover, it yields an accuracy of 90%, 86%, 93%, 97%, and 90% for stage W, stage N1, stage N2, stage N3, and stage REM, respectively. Comparative analysis of performance suggests that the proposed method is better, as opposed to that of several state-of-the-art ones. The sleep stage classification methods based on CNN and the fine-grained segments really improve a key step in the study of sleep disorders and expedite sleep research.

At present, most brain functional studies are based on traditional frequency bands to explore the abnormal functional connections and topological organization of patients with depression. However, they ignore the characteristic relationship of electroencephalogram (EEG) signals in the time domain. Therefore, this paper proposes a network decomposition model based on Improved Empirical Mode Decomposition (EMD), it is suitable for timefrequency analysis of brain functional network. On the one hand, it solves the problem of mode mixing on original EMD method, especially on high-density EEG data. On the other hand, by building brain function networks on different intrinsic mode function (IMF), we can perform time-frequency analysis of brain function connections. It provides a new insight for brain function connectivity analysis of major depressive disorder (MDD). Experimental results found that the IMFs waveform decomposed by Improved EMD was more stable and the difference between IMFs was obvious, it indicated that the mode mixing can be effectively solved.Besides, the analysis of the brain network, we found that the changes in MDD functional connectivity on different IMFs, it may be related to the pathological changes for MDD. More statistical results on three network metrics proved that there were significant differences between MDD and normal controls (NC) group. In addition, the aberrant brain network structure of MDDs was also confirmed in the hubs characteristic. These findings may provide potential biomarkers for the clinical diagnosis of MDD patients.

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