An ultrasonic-assisted extraction (UAE) procedure of epimedin A, epimedin B, epimedin C and icariin from Herba Epimedii was developed. The effects of ethanol concentration, ratio of liquid to solid, UAE time, extraction temperature and number of extraction cycles on the extraction yields of the four flavonoids from Herba Epimedii were investigated. The optimal UAE condition was found using orthogonal test: 50% (v/v) ethanol solution, liquid:solid ratio of 30 ml/g, ultrasonication duration 30 min, extraction temperature 50 degrees C and three extraction cycles. The UAE method showed a high reproducibility. Epimedin A, B, C and icariin in the crude extract exhibited photodegradation under ultraviolet irradiation. This UAE method was shown to be highly efficient compared with the conventional Soxhlet extraction and boiling extraction. The effect of ultrasound on cell destruction was examined by scanning electron microscopy. The contents of epimedin A, B, C and icariin in the leaves of 20 Epimedium species were determined using high-performance liquid chromatographic method following UAE method.

Sleep apnea syndrome (SAS) is a disorder in which respiratory airflow frequently stops during sleep. Alterations in electroencephalogram (EEG) signal are one of the physiological changes that occur during apnea, and can be used to diagnose and monitor sleep apnea events. Herein, we proposed a method to automatically distinguish sleep apnea events using characteristics of EEG signals in order to categorize obstructive sleep apnea (OSA) events, central sleep apnea (CSA) events and normal breathing events. Through the use of an Infinite Impulse Response Butterworth Band pass filter, we divided the EEG signals of C3-A2 and C4-A1 into five sub-bands. Next, we extracted sample entropy and variance of each sub-band. The neighbor composition analysis (NCA) method was utilized for feature selection, and the results are used as input coefficients for classification using random forest, K-nearest neighbor, and support vector machine classifiers. After a 10-fold cross-validation, we found that the average accuracy rate was 88.99%. Specifically, the accuracy of each category, including OSA, CSA and normal breathing were 80.43%, 84.85%, and 95.24%, respectively. The proposed method has great potential in the automatic classification of patients' respiratory events during clinical examinations, and provides a novel idea for the development of an automatic classification system for sleep apnea and normal events without the need for expert intervention.

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Sleep apnea-hypopnea syndrome is a relatively common disease, characterized by a repetitive reduction or cessation of respiratory airflow, which seriously affects sleep quality and in the long term, can lead to heart disease, hypertension, diabetes, and stroke. Polysomnography is currently the standard method for diagnosing apnea/hypopnea. However, accurate diagnosis can be difficult due to the complex process of multi-signals acquisition in polysomnography. Instead, this paper presents a novel automated fuzzy entropy-based method for detecting apnea/hypopnea using single-lead electroencephalogram signals. The method consists of four steps: (1) The electroencephalogram signals corresponding to respiratory events are partitioned into five sub-bands according to frequency; (2) Features of fuzzy entropy in each sub-band are extracted; (3) The extracted features are evaluated using statistical methods; (4) The features are classified using a classifier, such as the support vector machine, k-nearest neighbor, and random forest algorithms. In this study, data were obtained from a total of 55 subjects with sleep apnea-hypopnea syndrome from both a public and clinical database. The experimental results indicated that all of the selected metrics, including accuracy, sensitivity, and specificity were close to or above 90% for both publicly available and clinical data. Moreover, this approach is sensitive to all types of sleep apnea/hypopnea, an important aspect that is rarely explicitly discussed in the literature.