Prevalence of fibromyalgia among university students and its impact on their health-related quality of life: a survey-based study from Egypt.

BACKGROUND: University students are more likely to experience stress, anxiety, and depression. All these factors are regarded as psychological contributors to fibromyalgia syndrome (FMS). AIM: To investigate the prevalence and determinants of FMS among university students and its impact on their health-related quality of life (HRQoL). METHODS: This online survey-based study involved 2146 university students who were recruited from various faculties at several Egyptian universities. The participants' demographics, medical history, academic pursuits, and sleep data were collected. To identify the existence of FMS, the 2016 updates to the 2010/2011 FMS diagnostic criteria were used. Additionally, the participants completed the Short-Form Health Survey-36 (SF-36). RESULTS: The mean age was 21.26 ± 2.015 years and 76% were females. Of 2146 students, 266 (12.4%) fulfilled the criteria of FMS. FMS group had a significantly lower age (p < 0.001) with predominant female gender (89.5% vs. 74.1%, p < 0.001), positive family history of FMS (8.6% vs. 3.7%, p < 0.001), previous history of traffic accident (10.2% vs. 6.8%, p = 0.045), lower level of physical activity (p = 0.002),higher time spent in study per week (p = 0.002), lower sleep time (p = 0.002), with frequent walk up (p < 0.001) and snoring (p < 0.001) during sleep. Regarding HRQoL, students with FMS had significantly lower scores than students without in all domains. CONCLUSION: FMS is prevalent among Egyptian university students and is linked to female gender, positive family history, lower levels of physical activity, and more time spent studying each week. FMS has a negative impact on HRQoL. Therefore, early detection and treatment are recommended.

Transmit BeampattarnOptimization for Automotive MIMO Radar Coexistedwith Communication in V2V Networks.

Due to the flourishing development of vehicle-to-vehicle (V2V) communications and autonomous driving, interference between radar sensing and communication signals becomes a challenging issue. We propose a transmit beamforming based spectrum sharing scheme to achieve peaceful coexistence between automotive multiple-input multiple-out (MIMO) radar and communication systems. Our objective is to maximize the signal-to-interference-plus-noise ratio (SINR) of the automotive radar receiver subject to the communication capacity and the transmitted power budget constraints to optimize both the communication covariance matrix and the radar transmit precoder. The formulated optimization problem is non-convex, which is converted to convex by introducing a new slack variable and then solving it using the block coordinate descent, also called alternation optimization, approach. Additionally, the ellipsoid sub-gradient method is then employed to reduce the computational complexity. Simulation results demonstrate that our proposed scheme outperforms the conventional schemes.

Deep-learning-based seizure detection and prediction from electroencephalography signals.

Electroencephalography (EEG) is among the main tools used for analyzing and diagnosing epilepsy. The manual analysis of EEG must be conducted by highly trained clinicians or neuro-physiologists; a process that is considered to have a comparatively low inter-rater agreement. Furthermore, the new data interpretation consumes an excessive amount of time and resources. Hence, an automatic seizure detection and prediction system can improve the quality of patient care in terms of shortening the diagnosis period, reducing manual errors, and automatically detecting debilitating events. Moreover, for patient treatment, it is important to alert the patients of epilepsy seizures prior to seizure occurrence. Various distinguished studies presented good solutions for two-class seizure detection problems with binary classification scenarios. To deal with these challenges, this paper puts forward effective approaches for EEG signal classification for normal, pre-ictal, and ictal activities. Three models are presented for the classification task. Two of them are patient-specific, while the third one is patient non-specific, which makes it better for the general classification tasks. The two-class classification is implemented between normal and pre-ictal activities for seizure prediction and between normal and ictal activities for seizure detection. A more generalized three-class classification framework is considered to identify all EEG signal activities. The first model depends on a Convolutional Neural Network (CNN) with residual blocks. It contains thirteen layers with four residual learning blocks. It works on spectrograms of EEG signal segments. The second model depends on a CNN with three layers. It also works on spectrograms. On the other hand, the third model depends on Phase Space Reconstruction (PSR) to eliminate the limitations of the spectrograms used in the first models. A five-layer CNN is used with this strategy. The advantage of the PSR is the direct projection from the time domain, which keeps the main trend of different signal activities. The third model deals with all signal activities, and it was tested for all patients of the CHB-MIT dataset. It has a superior performance compared to the first models and the state-of-the-art models.