The most important part of sleep quality assessment is the classification of sleep stages, which helps to diagnose sleep-related disease. In the traditional sleep staging method, subjects have to spend a night in the sleep clinic for recording polysomnogram. Sleep expert classifies the sleep stages by monitoring the signals, which is time consuming and frustrating task and can be affected by human error. New studies propose fully automated techniques for classifying sleep stages that makes sleep scoring possible at home. Despite comprehensive studies have been presented in this field the classification results have not yet reached the gold standard due to the concentration on the use of a limited source of information such as single channel EEG. Therefore, this article introduces a new method for fusing two sources of information, including electroencephalogram (EEG) and electrooculogram (EOG), to achieve promising results in the classification of sleep stages. In the proposed method, extracted features from the EEG and EOG signals, are divided into two feature sets consisting of the EEG features and fused features of EEG and EOG. Then, each feature set transformed into a horizontal visibility graph (HVG). The images of the HVG are produced in a novel framework and classified by proposed transfer learning convolutional neural network for data fusion (TLCNN-DF). Employing transfer learning at the training stage of the model has accelerated the training process of the CNN and improved the performance of the model. The proposed algorithm is used to classify the Sleep-EDF and Sleep-EDFx benchmark datasets. The algorithm can classify the Sleep-EDF dataset with an accuracy of 93.58% and Cohen's kappa coefficient of 0.899. The results show proposed method can achieve superior performance compared to state-of-the-art studies on classification of sleep stages. Furthermore, it can attain reliable results as an alternative to conventional sleep staging. INDEX TERMS Convolutional neural network, data fusion, horizontal visibility graph, Sleep stage classification, transfer learning.
SummaryEktaphelenchus cupressi n. sp. was isolated during a survey of nematodes associated with bark samples of a dead cypress tree (Cupressus sempervirens) in Mazandaran province, northern Iran. The new species is characterised by a female body length of 612 (520-693) μm, stylet 17-19 μm long with wide lumen and lacking basal swellings, head region hemispherical in lateral view and slightly offset from the body contour by a shallow constriction, three incisures in the lateral field, excretory pore situated anterior to level of metacorpus valve, hemizonid not seen, post-uterine sac 29-35 μm long, shape of the tail terminus, and arrangement of the male caudal papillae. By a combination of morphological characters, e.g., stylet without knobs or swellings at the base, short conical tail, PUS length, and only a vestigial anus and rectum in most individuals, the new species shares similarities with species belonging to three genera, i.e., Anomyctus, Ektaphelenchus, and Seinura. Phylogenetic analysis based on small subunit (SSU) and partial large subunit (LSU) sequences of rDNA confirmed its status as a new species.
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