Vocal tasks-based EEG and speech signal analysis in children with neurodevelopmental disorders: a multimodal investigation.

Neurodevelopmental disorders (NDs) often hamper multiple functional prints of a child brain. Despite several studies on their neural and speech responses, multimodal researches on NDs are extremely rare. The present work examined the electroencephalography (EEG) and speech signals of the ND and control children, who performed "Hindi language" vocal tasks (V) of seven different categories, viz.

Analysis of reading-task-based brain connectivity in dyslexic children using EEG signals.

Developmental dyslexia, a neurodevelopment reading disorder, can impact even children with average intelligence. The present study examined the brain connectivity in dyslexic and control children during the reading task using graph theory. 19-channel electroencephalogram (EEG) signals were recorded from 15 dyslexic children and 15 control children.

Heart-brain Rhythmic Synchronization during Meditation: A Nonlinear Signal Analysis.

Background: Heart-brain synchronization is the integration of mind, body, and spirit. It occurs when the electrical activity of the heart and brain is synchronized. In recent years, there has been mounting curiosity to investigate the effects of meditation on heart-brain synchronization with respect to mental and emotional health and well-being.

EEG Based Functional Brain Network Analysis and Classification of Dyslexic Children During Sustained Attention Task.

Reading is a complex cognitive skill that involves visual, attention, and linguistic skills. Because attention is one of the most important cognitive skills for reading and learning, the current study intends to examine the functional brain network connectivity implicated during sustained attention in dyslexic children. 15 dyslexic children (mean age 9.

Heartfulness Meditation Alters Electroencephalogram Oscillations: An Electroencephalogram Study.

Background: Heartfulness meditation (HM) has been shown to have positive impacts on cognition and well-being, which makes it important to look into the neurophysiological mechanisms underlying the phenomenon.

Aim: A cross-sectional study was conducted on HM meditators and nonmeditators to assess frontal electrical activities of the brain and self-reported anxiety and mindfulness.

Settings And Design: The present study employed a cross-sectional design.

Effect of Heartfulness Meditation Among Long-Term, Short-Term and Non-meditators on Prefrontal Cortex Activity of Brain Using Machine Learning Classification: A Cross-Sectional Study.

Background Meditation is a mental practice with health benefits and may increase activity in the prefrontal cortex of the brain. Heartfulness meditation (HM) is a modified form of rajyoga meditation supported by a unique feature called "yogic transmission." This feasibility study aimed to explore the effect of HM on electroencephalogram (EEG) connectivity parameters of long-term meditators (LTM), short-term meditators (STM), and non-meditators (NM) with an application of machine learning models and determining classifier methods that can effectively discriminate between the groups.

EEG based functional brain networks analysis in dyslexic children during arithmetic task.

Developmental Dyslexia is a neuro-developmental disorder that often refers to a phonological processing deficit regardless of average IQ. The present study investigated the distinct functional changes in brain networks of dyslexic children during arithmetic task performance using an electroencephalogram. Fifteen dyslexic children and fifteen normally developing children (NDC) were recruited and performed an arithmetic task.

Integrating patient symptoms, clinical readings, and radiologist feedback with computer-aided diagnosis system for detection of infectious pulmonary disease: a feasibility study.

Automatic computer-aided diagnosis (CAD) system has been widely used as an assisting tool for mass screening and risk assessment of infectious pulmonary diseases (PDs). However, such a system still lacks clinical acceptability and trust due to the integration gap between the patient's metadata, radiologist feedback, and the CAD system. This paper proposed three integration frameworks, namely-direct integration (DI), rule-based integration (RBI), and weight-based integration (WBI).

Disease Localization and Severity Assessment in Chest X-Ray Images using Multi-Stage Superpixels Classification.

Background And Objectives: Chest X-ray (CXR) is a non-invasive imaging modality used in the prognosis and management of chronic lung disorders like tuberculosis (TB), pneumonia, coronavirus disease (COVID-19), etc. The radiomic features associated with different disease manifestations assist in detection, localization, and grading the severity of infected lung regions. The majority of the existing computer-aided diagnosis (CAD) system used these features for the classification task, and only a few works have been dedicated to disease-localization and severity scoring.

One-dimensional convolutional neural network and hybrid deep-learning paradigm for classification of specific language impaired children using their speech.

Background And Objective: Screening children for communicational disorders such as specific language impairment (SLI) is always challenging as it requires clinicians to follow a series of steps to evaluate the subjects. Artificial intelligence and computer-aided diagnosis have supported health professionals in making swift and error-free decisions about the neurodevelopmental state of children vis-à-vis language comprehension and production. Past studies have claimed that typical developing (TD) and SLI children show distinct vocal characteristics that can serve as discriminating facets between them.

Coronavirus disease (COVID-19) detection in Chest X-Ray images using majority voting based classifier ensemble.

Novel coronavirus disease (nCOVID-19) is the most challenging problem for the world. The disease is caused by severe acute respiratory syndrome coronavirus-2 (SARS-COV-2), leading to high morbidity and mortality worldwide. The study reveals that infected patients exhibit distinct radiographic visual characteristics along with fever, dry cough, fatigue, dyspnea, etc.

Artificial intelligence-based classification of schizophrenia: A high density electroencephalographic and support vector machine study.

Background: Interview-based schizophrenia (SCZ) diagnostic methods are not completely valid. Moreover, SCZ-the disease entity is very heterogeneous. Supervised-Machine-Learning (sML) application of Artificial-Intelligence holds a tremendous promise in solving these issues.

Respiratory Sound Based Classification of Chronic Obstructive Pulmonary Disease: a Risk Stratification Approach in Machine Learning Paradigm.

This article investigates the classification of normal and COPD subjects on the basis of respiratory sound analysis using machine learning techniques. Thirty COPD and 25 healthy subject data are recorded. Total of 39 lung sound features and 3 spirometry features are extracted and evaluated.

Use of Prosocial Word Enhances the Processing of Language: Frequency Domain Analysis of Human EEG.

Use of prosocial language enhances human cooperation and harmony. Previous research has shown that talking about helping, sharing and giving to others creates positive impression on others, by which individuals and governments gain public approval. So far, the value judgement of approval and disapproval in terms of prosocial or antisocial has not been investigated in the domain of neuroscience of language.

Plaque Tissue Morphology-Based Stroke Risk Stratification Using Carotid Ultrasound: A Polling-Based PCA Learning Paradigm.

Severe atherosclerosis disease in carotid arteries causes stenosis which in turn leads to stroke. Machine learning systems have been previously developed for plaque wall risk assessment using morphology-based characterization. The fundamental assumption in such systems is the extraction of the grayscale features of the plaque region.

Enhanced cryopreservation of MSCs in microfluidic bioreactor by regulated shear flow.

Cell-matrix systems can be stored for longer period of time by means of cryopreservation. Cell-matrix and cell-cell interaction has been found to be critical in a number of basic biological processes. Tissue structure maintenance, cell secretary activity, cellular migration, and cell-cell communication all exist because of the presence of cell interactions.