A review of web-based support systems for students in higher education

Abstract Background Recent evidence suggests that there is an increasing need for accessible and anonymous services to support higher education (HE) students suffering from psychological and/or academic difficulties. Such difficulties can lead to several negative outcomes, including poor academic performance, sub-optimal mental health, reduced study satisfaction, and dropout from study. Currently, universities in the UK lack financial resources and the on-campus mental health services traditionally offered to students are increasingly economically unsustainable. Compounded by the perceived stigma of using such services, mental health providers have been driven to address the escalating needs of students through online services. Methods In this paper, we review online support systems identified through a literature search and a manual search of references in the identified papers. Further systems were identified through web searches, and systems still in development were identified by consultation with researchers in the field. We accessed systems online to extract relevant information, regarding the main difficulties addressed by the systems, the psychological techniques used and any relevant research evidence to support their effectiveness. Conclusion A large number of web-based support systems have been developed to support mental health and wellbeing, although few specifically target HE students. Further research is necessary to establish the effectiveness of such interventions in providing a cost-effective alternative to face-to-face therapy, particularly in certain settings such as HE institutions

EEG artifacts reduction by multivariate empirical mode decomposition and multiscale entropy for monitoring depth of anaesthesia during surgery

Electroencephalography (EEG) has been widely utilized to measure the depth of anaesthesia (DOA) during operation. However, the EEG signals are usually contaminated by artifacts which have a consequence on the measured DOA accuracy. In this study, an effective and useful filtering algorithm based on multivariate empirical mode decomposition and multiscale entropy (MSE) is proposed to measure DOA. Mean entropy of MSE is used as an index to find artifacts-free intrinsic mode functions. The effect of different levels of artifacts on the performances of the proposed filtering is analysed using simulated data. Furthermore, 21 patients' EEG signals are collected and analysed using sample entropy to calculate the complexity for monitoring DOA. The correlation coefficients of entropy and bispectral index (BIS) results show 0.14 ± 0.30 and 0.63 ± 0.09 before and after filtering, respectively. Artificial neural network (ANN) model is used for range mapping in order to correlate the measurements with BIS. The ANN method results show strong correlation coefficient (0.75 ± 0.08). The results in this paper verify that entropy values and BIS have a strong correlation for the purpose of DOA monitoring and the proposed filtering method can effectively filter artifacts from EEG signals. The proposed method performs better than the commonly used wavelet denoising method. This study provides a fully adaptive and automated filter for EEG to measure DOA more accuracy and thus reduce risk related to maintenance of anaesthetic agents.This research was financially supported by the Center for Dynamical Biomarkers and Translational Medicine, National Central University, Taiwan, which is sponsored by Ministry of Science and Technology (Grant Number: NSC102-2911-I-008-001). Also, it was supported by Chung-Shan Institute of Science and Technology in Taiwan (Grant Numbers: CSIST-095-V301 and CSIST-095-V302) and National Natural Science Foundation of China (Grant Number: 51475342)