A machine learning based depression screening framework using temporal domain features of the electroencephalography signals.

Depression is a serious mental health disorder affecting millions of individuals worldwide. Timely and precise recognition of depression is vital for appropriate mediation and effective treatment. Electroencephalography (EEG) has surfaced as a promising tool for inspecting the neural correlates of depression and therefore, has the potential to contribute to the diagnosis of depression effectively. This study presents an EEG-based mental depressive disorder detection mechanism using a publicly available EEG dataset called Multi-modal Open Dataset for Mental-disorder Analysis (MODMA). This study uses EEG data acquired from 55 participants using 3 electrodes in the resting-state condition. Twelve temporal domain features are extracted from the EEG data by creating a non-overlapping window of 10 seconds, which is presented to a novel feature selection mechanism. The feature selection algorithm selects the optimum chunk of attributes with the highest discriminative power to classify the mental depressive disorders patients and healthy controls. The selected EEG attributes are classified using three different classification algorithms i.e., Best- First (BF) Tree, k-nearest neighbor (KNN), and AdaBoost. The highest classification accuracy of 96.36% is achieved using BF-Tree using a feature vector length of 12. The proposed mental depressive classification scheme outperforms the existing state-of-the-art depression classification schemes in terms of the number of electrodes used for EEG recording, feature vector length, and the achieved classification accuracy. The proposed framework could be used in psychiatric settings, providing valuable support to psychiatrists.

The Era of Polypills in the Management of Cardiovascular Diseases: Are We There Yet?

Cardiovascular diseases (CVDs) are the leading cause of mortality globally. Wald and Law proposed the idea of a "polypill"; a fixed dose combination therapy (FDC) in the form of a single pill to curb the CVD epidemic. Such a drug would include the combination of a broad spectrum of drugs including cholesterol lowering drugs, antihypertensive drugs, antiplatelet drugs, anticoagulation drugs, and antiarrhythmic drugs, which are frequently integrated to combat specific CVDs. This "polypill" holds the potential to pose several advantages like increased compliance, improved quality of life, risk factor control, psychological relief, and cost effectiveness along with minimal side effects. Several trials (like TIPS, UMPIRE, PolyIran, etc.) have tested different treatment strategies to test the hypothesis of Wald and Law. Unlike the past, physicians are now highly aware of this new strategy. The future of polypill in the management of CVD lies in a strategy where polypills are treated supplementary to the already existing preventive care, which includes lifestyle modifications and efforts to reduce tobacco use.

A Serendipitous Outcome of COVID-19: Modifications to ICU Management.

Nosocomial infections are common in intensive care units (ICUs) and often cause increased morbidity and mortality rates in ICU patients. With the emergence of the highly infectious COVID-19, the high prevalence of hospital-acquired infections (HAIs) in ICU has caused much more concern because patients admitted to the ICU have a more severe and prolonged form of the disease. These patients are more likely to develop HAIs than non-ICU patients. Medical communities adopted several measures to make ICU management safer during the pandemic all over the world. In this study, we re-examined the challenges faced and the changes made in ICU management during the pandemic to speculate how these changes will be relevant post-pandemic and can be permanently incorporated into the ICU to improve safety, management, and critical care and make critical care better equipped for future disease breakouts.

EEG in game user analysis: A framework for expertise classification during gameplay.

Video games have become a ubiquitous part of demographically diverse cultures. Numerous studies have focused on analyzing the cognitive aspects involved in game playing that could help in providing an optimal gaming experience by improving video game design. To this end, we present a framework for classifying the game player's expertise level using wearable electroencephalography (EEG) headset. We hypothesize that expert and novice players' brain activity is different, which can be classified using frequency domain features extracted from EEG signals of the game player. A systematic channel reduction approach is presented using a correlation-based attribute evaluation method. This approach lead us in identifying two significant EEG channels, i.e., AF3 and P7, among fourteen channels available in Emotiv EPOC headset. In particular, features extracted from these two EEG channels contributed the most to the video game player's expertise level classification. This finding is validated by performing statistical analysis (t-test) over the extracted features. Moreover, among multiple classifiers used, K-nearest neighbor is the best classifier in classifying game player's expertise level with a classification accuracy of up to 98.04% (without data balancing) and 98.33% (with data balancing).

Human stress classification during public speaking using physiological signals.

Public speaking is a common type of social evaluative situation and a significant amount of the population feel uneasy with it. It is of utmost importance to detect public speaking stress so that appropriate action can be taken to minimize its impacts on human health. In this study, a multimodal human stress classification scheme in response to real-life public speaking activity is proposed. Electroencephalography (EEG), galvanic skin response (GSR), and photoplethysmography (PPG) signals of forty participants are acquired in rest-state and during public speaking activity to divide data into a stressed and non-stressed group. Frequency domain features from EEG and time-domain features from GSR and PPG signals are extracted. The selected set of features from all modalities are fused to classify the stress into two classes. Classification is performed via a leave-one-out cross-validation scheme by using five different classifiers. The highest accuracy of 96.25% is achieved using a support vector machine classifier with radial basis function. Statistical analysis is performed to examine the significance of EEG, GSR, and PPG signals between the two phases of the experiment. Statistical significance is found in certain EEG frequency bands as well as GSR and PPG data recorded before and after public speaking supporting the fact that brain activity, skin conductance, and blood volumetric flow are credible measures of human stress during public speaking activity.

Classification of Perceived Mental Stress Using A Commercially Available EEG Headband.

Human stress is a serious health concern, which must be addressed with appropriate actions for a healthy society. This paper presents an experimental study to ascertain the appropriate phase, when electroencephalography (EEG) based data should be recorded for classification of perceived mental stress. The process involves data acquisition, pre-processing, feature extraction and selection, and classification. The stress level of each subject is recorded by using a standard perceived stress scale questionnaire, which is then used to label the EEG data. The data are divided into two (stressed and non-stressed) and three (non-stressed, mildly stressed, and stressed) classes. The EEG data of 28 participants are recorded using a commercially available four channel Muse EEG headband in two phases i.e., pre-activity and post-activity. Five feature groups, which include power spectral density, correlation, differential asymmetry, rational asymmetry, and power spectrum are extracted from five bands of each EEG channel. We propose a new feature selection algorithm, which selects features from appropriate EEG frequency band based on classification accuracy. Three classifiers i.e., support vector machine, the Naive Bayes, and multi-layer perceptron are used to classify stress level of the participants. It is evident from our results that EEG recording during the pre-activity phase is better for classifying the perceived stress. An accuracy of [Formula: see text] and [Formula: see text] is achieved for two- and three-class stress classification, respectively, while utilizing five groups of features from theta band. Our proposed feature selection algorithm is compared with existing algorithms and gives better classification results.

Classification of Perceived Human Stress using Physiological Signals.

In this paper, we present an experimental study for the classification of perceived human stress using non-invasive physiological signals. These include electroencephalography (EEG), galvanic skin response (GSR), and photoplethysmography (PPG). We conducted experiments consisting of steps including data acquisition, feature extraction, and perceived human stress classification. The physiological data of 28 participants are acquired in an open eye condition for a duration of three minutes. Four different features are extracted in time domain from EEG, GSR and PPG signals and classification is performed using multiple classifiers including support vector machine, the Naive Bayes, and multi-layer perceptron (MLP). The best classification accuracy of 75% is achieved by using MLP classifier. Our experimental results have shown that our proposed scheme outperforms existing perceived stress classification methods, where no stress inducers are used.