Sleep apnea syndrome (SAS) is a very common sleep disorder disease. Reliable detection of apnea is very crucial for subsequent treatment. In this article, a novel method based on artificial neural network is proposed for such purpose. With its time-invariant property the time delay neural network (TDNN) is adopted in this system to employ the temporal trend of apnea event. As airflow and SaO2 take the most important roles in sleep apnea syndrome diagnosis, features extracted from both of them are simultaneously fed into the neural network. The proposed algorithm was tested with 15 overnight polysomnographic (PSG) records, and with a sensitivity rate of 90.7% and 80.8%, a specificity rate of 86.4% and 81.4% for apnea and hypopnea detection, respectively. Furthermore, the proposed algorithm can accommodate in some manner the airflow sensor failure due to technical errors. But, as the SaO2 changes are commonly delayed by 10 or more seconds compared to the airflow signal, integration of SaO2 make this method only suited for offline detection. In conclusion, systems based on this algorithm can be used as a valuable timesaving adjunct for PSG SAS diagnosis.
A hybrid system for automated EEG sleep staging is presented in this article. By combining a self-organizing feature map (SOFM) with a fuzzy reasoning-based classifier (FRBC) and utilizing both temporal and spectrum features of the EEG signal, the system provides a reliable tool for automatic EEG sleep staging. Conceptually, the system is divided into four passes: artifact detection, rough staging, stage refinement and post processing. The artifact detection module is firstly employed to exclude stage movement from other stages. Then, the SOFM with features as its inputs derived from the power spectrum divides sleep into three "extreme" stages: Wake, Light/REM and Deep stage. In stage refinement pass, the FRBC, which takes characteristic waveforms' activities as inputs, subdivides the extreme stages into the exact stages (i.e., stage 1, stage 2) defined by R&K standard. At last, in post processing pass, a stage-smoothing method that mainly utilizes the temporal context information is used to correct unexpected stage transitions, thus to improve the system's performance. The system was tested with eight whole night sleep records with an average man-machine agreement of 85.3%. Compared with the high inter-scorer disagreement, the performance is desirable.
In recent years, chat-bot has become a new type of intelligent terminal to guide users to consume services. However, it is criticized most that the services it provides are not what users expect or most expect. This defect mostly dues to two problems, one is that the incompleteness and uncertainty of user's requirement expression caused by the information asymmetry, the other is that the diversity of service resources leads to the difficulty of service selection. Conversational bot is a typical mesh device, so the guided multi-rounds Q&A is the most effective way to elicit user requirements. Obviously, complex Q&A with too many rounds is boring and always leads to bad user experience. Therefore, we aim to obtain user requirements as accurately as possible in as few rounds as possible. To achieve this, a user intention recognition method based on Knowledge Graph (KG) was developed for fuzzy requirement inference, and a requirement elicitation method based on Granular Computing was proposed for dialog policy generation. Experimental results show that these two methods can effectively reduce the number of conversation rounds, and can quickly and accurately identify the user intention.
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