EEG based Stress Analysis through Feature Extraction

The diagnosis of Stress relies virtually solely on doctor-patient conversation and scale analysis, which includes problems such as patient denial, insensitivity, subjective biases, and inaccuracy. Improving the accuracy of Stress diagnosis and therapy necessitates the development of an objective, computerized system for predicting clinical outcomes. Using the modification of EEG data and machine learning techniques, this study attempts to improve the recognition of Stress. The EEG data of 10 volunteers were acquired using a Narosky device during an experiment, including emotive facial stimuli. Psychiatrists used the EEG signal as the criterion for diagnosis of Stress in patients. The different approaches processed the features: machine learning and deep learning. Significant outcomes are achieved using PCA, ICA & EMD for BCI applications. SVM empowers a developer with several advantages: PCA exhibits excellent generalization properties, with stress & pressure detection using EEG Signals. If the signals are negative, the impact of overtraining is sensitive to the curse-of- dimensionality. These advantages were achieved by using EEG signals to detect Stress. The experimental analysis gives some overview of all different approaches, which depend on frequency domain analysis with 14 fourteen-channel EEG signals with reasonable accuracy

The weighted Hellinger distance in the multivariate kernel density estimation

The kernel multivariate density estimation is an important technique to estimate the multivariate density function. In this investigation we will use Hellinger Distance as a measure of error to evaluate the estimator, we will derive the mean weighted Hellinger distance for the estimator, and we obtain the optimal bandwidth based on Hellinger distance. Also, we propose and study a new technique to select the matrix of bandwidths based on Hellinger distance, and compare the new technique with the plug-in and the least squares techniques

Ensemble Learning using Transformers and Convolutional Networks for Masked Face Recognition

Wearing a face mask is one of the adjustments we had to follow to reduce the spread of the coronavirus. Having our faces covered by masks constantly has driven the need to understand and investigate how this behavior affects the recognition capability of face recognition systems. Current face recognition systems have extremely high accuracy when dealing with unconstrained general face recognition cases but do not generalize well with occluded masked faces. In this work, we propose a system for masked face recognition. The proposed system comprises two Convolutional Neural Network (CNN) models and two Transformer models. The CNN models have been fine-tuned on FaceNet pre-trained model. We ensemble the predictions of the four models using the majority voting technique to identify the person with the mask. The proposed system has been evaluated on a synthetically masked LFW dataset created in this work. The best accuracy is obtained using the ensembled models with an accuracy of 92%. This recognition rate outperformed the accuracy of other models and it shows the correctness and robustness of the proposed model for recognizing masked faces. The code and data are available at https://github.com/Hamzah-Luqman/MFRComment: 6 page

Automated Fidelity Assessment for Strategy Training in Inpatient Rehabilitation using Natural Language Processing

Strategy training is a multidisciplinary rehabilitation approach that teaches skills to reduce disability among those with cognitive impairments following a stroke. Strategy training has been shown in randomized, controlled clinical trials to be a more feasible and efficacious intervention for promoting independence than traditional rehabilitation approaches. A standardized fidelity assessment is used to measure adherence to treatment principles by examining guided and directed verbal cues in video recordings of rehabilitation sessions. Although the fidelity assessment for detecting guided and directed verbal cues is valid and feasible for single-site studies, it can become labor intensive, time consuming, and expensive in large, multi-site pragmatic trials. To address this challenge to widespread strategy training implementation, we leveraged natural language processing (NLP) techniques to automate the strategy training fidelity assessment, i.e., to automatically identify guided and directed verbal cues from video recordings of rehabilitation sessions. We developed a rule-based NLP algorithm, a long-short term memory (LSTM) model, and a bidirectional encoder representation from transformers (BERT) model for this task. The best performance was achieved by the BERT model with a 0.8075 F1-score. This BERT model was verified on an external validation dataset collected from a separate major regional health system and achieved an F1 score of 0.8259, which shows that the BERT model generalizes well. The findings from this study hold widespread promise in psychology and rehabilitation intervention research and practice.Comment: Accepted at the AMIA Informatics Summit 202

Effectiveness of Antibiotic Prophylaxis for Leptospirosis among Adults: A Systematic Review

Leptospirosis is one of the most widespread re-emerging zoonoses in the world. Malaysia is known to be an endemic country for human leptospirosis, with a case fatality rate of 2.11%, and an average annual incidence rate of 7.80 cases per 100,000 individuals. This systematic review is conducted to determine the effectiveness of antibiotic prophylaxis for leptospirosis among the adult populations who are highly at risk of getting infected. A systematic search was performed for the relevant titles, abstracts and keywords on PubMed, Scopus, Cochrane and Google Scholar from inception to November 2017 based on the PICO strategy; which returned 126 studies. Screening of abstracts had shortlisted 19 studies and data extraction was conducted for 8 studies which had been accepted after review of the full text. For the evaluation of antibiotics prophylaxis effectiveness against leptospirosis, only trials and cohort studies with risk ratio (RR) were selected. The articles were analyzed from the viewpoint of the dosage, adverse effects, study settings and effectiveness of the antibiotic prophylaxis. Using fixed effects model, pooled RR showed protective association between antibiotic prophylaxis use against the incidence of leptospirosis (RR = 0.31; 95% CI: 0.20, 0.48). Antibiotic prophylaxis for leptospirosis had been shown to be effective in preventing the incidence of the disease among high-risk populations and carries minimal adverse effects. It is recommended that the practice of antibiotic prophylaxis for leptospirosis is included in the standard protocol for leptospirosis prevention among people at high-risk, including disaster response teams and patrons of eco-sports tourism activities; with the drug of choice being doxycycline, either as a single 200 mg dose or weekly dose of 200 mg for the duration of exposure, based on the setting, duration of event and resources available

Chargeâ Transport Properties of F6TNAPâ Based Chargeâ Transfer Cocrystals

The crystal structures of the chargeâ transfer (CT) cocrystals formed by the Ï â electron acceptor 1,3,4,5,7,8â hexafluoroâ 11,11,12,12â tetracyanonaphthoâ 2,6â quinodimethane (F6TNAP) with the planar Ï â electronâ donor molecules triphenylene (TP), benzo[b]benzo[4,5]thieno[2,3â d]thiophene (BTBT), benzo[1,2â b:4,5â bâ ²]dithiophene (BDT), pyrene (PY), anthracene (ANT), and carbazole (CBZ) have been determined using singleâ crystal Xâ ray diffraction (SCXRD), along with those of two polymorphs of F6TNAP. All six cocrystals exhibit 1:1 donor/acceptor stoichiometry and adopt mixedâ stacking motifs. Cocrystals based on BTBT and CBZ Ï â electron donor molecules exhibit brickwork packing, while the other four CT cocrystals show herringboneâ type crystal packing. Infrared spectroscopy, molecular geometries determined by SCXRD, and electronic structure calculations indicate that the extent of groundâ state CT in each cocrystal is small. Density functional theory calculations predict large conduction bandwidths and, consequently, low effective masses for electrons for all six CT cocrystals, while the TPâ , BDTâ , and PYâ based cocrystals are also predicted to have large valence bandwidths and low effective masses for holes. Chargeâ carrier mobility values are obtained from spaceâ charge limited current (SCLC) measurements and fieldâ effect transistor measurements, with values exceeding 1 cm2 Vâ 1 s1 being estimated from SCLC measurements for BTBT:F6TNAP and CBZ:F6TNAP cocrystals.Structural, electronic band structure, and electrical properties of a series of chargeâ transfer cocrystals based on F6TNAP and six planar donors are presented. Density functional theory calculations afford large conduction bandwidths and low effective masses for all six cocrystals. A few cocrystals exhibit chargeâ carrier mobilities in excess of 1 cm2 Vâ 1 sâ 1, as estimated from spaceâ charge limited current measurements.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/153248/1/adfm201904858-sup-0001-S1.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/153248/2/adfm201904858.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/153248/3/adfm201904858_am.pd

Computationally Aided Design of a High-Performance Organic Semiconductor: The Development of a Universal Crystal Engineering Core

Herein, we describe the design and synthesis of a suite of molecules based on a benzodithiophene “universal crystal engineering core”. After computationally screening derivatives, a trialkylsilylethyne-based crystal engineering strategy was employed to tailor the crystal packing for use as the active material in an organic field-effect transistor. Electronic structure calculations were undertaken to reveal derivatives that exhibit exceptional potential for high-efficiency hole transport. The promising theoretical properties are reflected in the preliminary device results, with the computationally optimized material showing simple solution processing, enhanced stability, and a maximum hole mobility of 1.6 cm2 V−1 s−1

International Veterinary Epilepsy Task Force recommendations for a veterinary epilepsy-specific MRI protocol

Epilepsy is one of the most common chronic neurological diseases in veterinary practice. Magnetic resonance imaging (MRI) is regarded as an important diagnostic test to reach the diagnosis of idiopathic epilepsy. However, given that the diagnosis requires the exclusion of other differentials for seizures, the parameters for MRI examination should allow the detection of subtle lesions which may not be obvious with existing techniques. In addition, there are several differentials for idiopathic epilepsy in humans, for example some focal cortical dysplasias, which may only apparent with special sequences, imaging planes and/or particular techniques used in performing the MRI scan. As a result, there is a need to standardize MRI examination in veterinary patients with techniques that reliably diagnose subtle lesions, identify post-seizure changes, and which will allow for future identification of underlying causes of seizures not yet apparent in the veterinary literature. There is a need for a standardized veterinary epilepsy-specific MRI protocol which will facilitate more detailed examination of areas susceptible to generating and perpetuating seizures, is cost efficient, simple to perform and can be adapted for both low and high field scanners. Standardisation of imaging will improve clinical communication and uniformity of case definition between research studies. A 6–7 sequence epilepsy-specific MRI protocol for veterinary patients is proposed and further advanced MR and functional imaging is reviewed