Resting state EEG assisted imagined vowel phonemes recognition by native and non-native speakers using brain connectivity measures.

Communication is challenging for disabled individuals, but with advancement of brain-computer interface (BCI) systems, alternative communication systems can be developed. Current BCI spellers, such as P300, SSVEP, and MI, have drawbacks like reliance on external stimuli or conversation irrelevant mental tasks. In contrast to these systems, Imagined speech based BCI systems rely on directly decoding the vowels/words user is thinking, making them more intuitive, user friendly and highly popular among Brain-Computer-Interface (BCI) researchers. However, more research needs to be conducted on how subject-specific characteristics such as mental state, age, handedness, nativeness and resting state activity affects the brain's output during imagined speech. In an overt speech, it is evident that native and non-native speakers' brains function differently. Therefore, this paper explores how nativeness to language affects EEG signals while imagining vowel phonemes, using brain-map analysis and scalogram and also investigates the inclusion of features extracted from resting state EEG with imagined state EEG. The Fourteen-channel EEG for Imagined Speech (FEIS) dataset was used to analyse the EEG signals recorded while imagining vowel phonemes for 16 subjects (nine native English and seven non-native Chinese). For the classification of vowel phonemes, different connectivity measures such as covariance, coherence, and Phase Synchronous Index-PSI were extracted and analysed using statistics based Multivariate Analysis of Variance (MANOVA) approach. Different fusion strategies (difference, concatenation, Common Spatial Pattern-CSP and Canonical Correlation Analysis-CCA) were carried out to incorporate resting state EEG connectivity measures with imagined state connectivity measures for enhancing the accuracy of imagined vowel phoneme recognition. Simulation results revealed that concatenating imagined state and rest state covariance and PSI features provided the maximum accuracy of 92.78% for native speakers and 94.07% for non-native speakers.

Digital Well-being Through the Use of Technology-A Perspective.

"No man is an island unto himself" - John Donne According to the World Health Organization, health is "a state of complete physical, mental and social well-being and not merely the absence of disease and infirmity." Our healthcare industry, public behaviors, and environment have grown exponentially with digital technologies in the era of the 4th industrial revolution. Due to rapid digitalization and easy availability of the internet, we are now online round the clock on our digital devices, leaving behind digital traces/information. These digital footprints serve as an increasingly fruitful data source for social scientists, including those interested in demographic research. The collection and use of digital data (quantitative and qualitative) also present numerous statistical and computational opportunities, further motivating the development of new research approaches to address health issues. In this paper, we have described the concept of digital well-being and proposed how we can use digital information for good health.

Evaluation of existing Home Based Newborn Care (HBNC) services and training for improving performance of Accredited Social Health Activists (ASHA) in rural India: A multiple observation study.

BACKGROUND: One-fourth of global neonatal deaths occur in India alone. Accredited Social Health Activists (ASHA) was launched with the purpose of improving healthcare services, including neonatal survival primarily in rural areas. The aim of this study is to determine the status of ASHA's knowledge, practices, and attitude regarding Home Based Newborn Care (HBNC) services, as well as to provide necessary trainings for improvement of their performance. METHODS: For this study, 102 ASHA working in Doiwala were recruited at random, and Quasi Experimental Design - Multiple Observation Method (single group time series design) was adopted. The data were collected using pretested tools consisting of knowledge questionnaires, attitude scale, and practices and skill-based questionnaires on various domains of HBNC. The data from the ASHA were collected 4 times at a regular interval of 30 days. Each time, the assessment of ASHA was accompanied by re-education and training on HBNC. RESULTS: Even though, about 90% of ASHA had been working for more than 5 years, they possessed average knowledge regarding HBNC before the training. Less than 50% of them were aware of mandatory vaccines and infection care services for newborns. About 70% of them were uninformed about the potential risk of hypothermia in neonates and also lacked knowledge regarding its preventive measures. Their knowledge, practices and attitude regarding HBNC was significantly improved after the training (p ˂ 0.05). About 54% of ASHA became aware of the avoidance of pre-lacteal feeding in newborns. Their practices score regarding prevention of hypothermia was increased from 80% to 95%. The number of ASHA who understood the importance of Kangaroo Mother Care (KMC) was also increased from 56% to 87%. About 95% of the ASHA understood the significance of feeding breast milk to newborns. Moreover, the attitude of ASHA towards the traditional way of newborn care such as early bathing, giving pre-lacteal feed, application of turmeric and ghee to the umbilicus of baby etc. was significant improved. CONCLUSION: ASHA must be assessed regularly in order to identify their basic needs, knowledge gaps, challenges and difficulties to quality HBNC services. Proper training on HBNC at regular interval significantly improved their knowledge, practices, and attitude toward their responsibilities, which is crucial for improving newborn health status.

SNOAR: a new regression approach for the removal of ocular artifact from multi-channel electroencephalogram signals.

Electroencephalogram (EEG) signals are often corrupted by undesirable sources like electrooculogram (EOG) artifacts, which have a substantial impact on the performance of EEG-based systems. This study proposes a new singular spectrum analysis (SSA)-non-negative matrix factorization (NMF)-based ocular artifact removal (SNOAR) method to suppress ocular artifacts from multi-channel EEG signals. First, SSA was used to estimate EOG artifacts using a small subset of frontal electrodes. Then, NMF was applied to decompose the estimated EOG artifacts into vertical EOG (VEOG) and horizontal EOG (HEOG) signals. Finally, a simple linear regression with estimated VEOG and HEOG signals was used to remove artifacts from multi-channel EEG signals. EEG recordings from two EEG datasets (Klados dataset and KARA ONE) were used to evaluate the efficiency of the proposed method. From the simulation results, it was observed that the proposed method achieved betters results in terms of low root-mean-square error (RMSE), low delta band energy ratio, and less power spectral density (PSD) difference between the original clean EEG signal and its filtered version of contaminated EEG signal compared to selected EOG artifact removal methods (independent component analysis (ICA), wavelet-enhanced ICA (wICA), improved wICA, and multivariate empirical mode decomposition (MEMD)).

Attention deficit hyperactivity disorder and associated learning difficulties among primary school children in district Dehradun, Uttarakhand, India.

BACKGROUND: Attention deficit hyperactivity disorder (ADHD) is a considerably common childhood-onset neurodevelopmental disorder, often associated with socio-behavioral and academic difficulties. There is an increased risk for development of a range of future problems such as psychiatric disorders, difficulties in employment, and relationships. The aim of this study was to know the prevalence and determinants of ADHD among primary school children in district Dehradun and to assess the learning difficulties and classroom behavior in these students. MATERIALS AND METHODS: In this cross-sectional study, overall, 228 students (aged 6-12 years) studying in a primary school were assessed for ADHD after seeking the written informed consent from their parents. The data were analyzed using Excel sheet and SPSS software (22.0 version). For all qualitative measures, frequency and percentages were calculated, and for quantitative measures, mean and standard deviation were calculated. For analysis of categorical values, Chi-square test was used. P < 0.05 was considered statistically significant. RESULTS: The prevalence of ADHD was found to be 11.8% based on the teacher tool only and 1.75% based on the parent and teacher tools combined. ADHD was found to be significantly more in males. Inattention was the most prevalent subtype of ADHD, and children screened positive for ADHD had significant learning difficulties at various levels. CONCLUSION: The possibility of ADHD in students with academic difficulties should not be ignored as children with ADHD usually face significant problems at school such as learning difficulties and have been shown to be at increased risk for a broad range of negative outcomes. Early identification and treatment of ADHD can significantly reduce the rates of some of these poor outcomes. Screening these children in the early years will help the parents, children, teachers, and community at large.

COVID-19 and childhood obesity (CO-BESITY) in the era of new normal life: A need for a policy research.

BACKGROUND: In the era of new normal life after Coronavirus Disease 2019 (COVID-19), our children are experiencing the double threat of COVID-19 and Childhood Obesity (CO-BESITY). The rate of childhood obesity has been rapidly increasing in developed as well as low middle-income countries during the pandemic. DESIGN AND METHODS: The current paper aims to identify the probable reasons of increase in childhood obesity during this pandemic and offers suggestions to reduce the burden of it. Literature search was done using PubMed, Google Scholar, and Scopus databases for the key terms "childhood obesity," "obesity," "pandemic," and/or childhood obesity. All the relevant articles were included to support the argument for this viewpoint. RESULTS: Childhood obesity is a complicated disorder having diverse outcomes. The incidence of childhood obesity is analysed from Bronfenbrenner's model of child development. The model examines an overabundance of bio-psycho-social backgrounds, risks, and probable outcomes on the development of a child. COVID-19 pandemic has disrupted the ecosystem of this dynamic model and has created an economic and social-cultural crisis that has ignited a chain reaction of stressors upon children and their families. In this paper, we have described how this Bronfenbrenner's model of child development also known as the Bioecological Model can be effective for the estimation and prevention of childhood obesity. CONCLUSION: We propose that this Bioecological Model will help the children and their families further understand and manage the problem of childhood obesity during this pandemic on their own.

Role of Public Health Ethics for Responsible Use of Artificial Intelligence Technologies.

Recent advancements in artificial intelligence (AI) technologies have shown promising success in optimizing health-care processes and improvising health services research and practice leading to better health outcomes. However, the role of public health ethics in the era of AI is not widely evaluated. This article aims to describe the responsible approach to AI design, development, and use from a public health perspective. This responsible approach should focus on the collective well-being of humankind and incorporate ethical principles and societal values. Such approaches are important because AI concerns and impacts the health and well-being of all of us collectively. Rather than limiting such discourses at the individual level, ethical considerations regarding AI systems should be analyzed enlarge, considering the complex socio-technological reality around the world.

Non-communicable diseases viewed as "collateral damage" of our decisions: Fixing accountabilities and finding sloutions in primary care settings.

In the changing global socio-economic and epidemiological landscapes, non communicable diseases (NCDs) are affecting the health and wellbeing of populations. The burden is worse among people in low- and middle-income countries with more than 32 million deaths attributable to NCDs each year. This scenario can be explained through the concept of collateral damage, where intentional actions often lead to adverse consequences alongside the primary outcomes. Thus, NCDs can be viewed as collateral damage of unplanned urbanization, rapid globalization, fast pace of life etc., In addition, a lack of appropriate public health approaches has aggravated the situation. It is essential to build a collaborative approach engaging public health agencies to ensure that the developmental initiatives are without the threat of collateral damages and are people-friendly. This will help in reducing the burden of NCDs in primary care settings.

Generalized Anxiety disorder but not depression is associated with insomnia: a population based study.

BACKGROUND: Insomnia is a common problem, however, its prevalence has never been examined in Indian population. Moreover, a number of psychiatric disorders have been found to be associated with insomnia in clinical population, but this association has scarcely been examined in general population. METHODS: This epidemiological study was done in an urban and a rural population. Subjects were selected using Kish method. After obtaining informed consent, psychiatric disorders were diagnosed using Hindi version of Mini International Neuropsychiatric Interview. Hindi version of Insomnia Severity Index was used to diagnose insomnia. RESULTS: 1700 subjects were included in this study. In this study, prevalence of insomnia was 10.3%. Its prevalence increased with increasing years of education (p=0.009). Insomnia was more frequent in subjects living in joint families (p<0.001), having higher education (p=0.009), those who were separated (p<0.001), among subjects belonging to middle socio-economic status (p<0.001) and in urban population compared to semi-urban and rural population (p<0.001). Insomnia was also more frequent among subjects with major depressive disorder, generalized anxiety disorder, alcohol dependence, cannabis dependence and tobacco use. However, binary logistic regression analysis showed that only higher education, unemployment, generalized anxiety disorder and tobacco use were associated with insomnia. CONCLUSION: Insomnia in general population is associated with higher education, unemployment, generalized anxiety disorders and tobacco use.

Five-year trend of acquired antitubercular drug resistance in patients attending a tertiary care hospital at Dehradun (Uttarakhand).

BACKGROUND/AIM: To study the prevalence and trend of acquired drug resistance to the first line antitubercular drugs. MATERIALS AND METHODS: Sputum of 215 previously treated adult pulmonary tuberculosis (TB) patients over a period of 2002-2006 were subjected to culture and sensitivity testing against common antitubercular drugs. RESULT: Growth of Mycobacterium tuberculosis was obtained from sputum specimen of 184 (85.58%) of the 215 patients who were studied; Overall, 113 (62.77%) of these were resistant to at least one antitubercular drug. Resistance to isoniazid was most common (62.22%) followed by rifampicin (57.22%). Multidrug resistance (MDR) was observed in 103 (57.22%) cases. During the five-year study period, an increasing trend in drug resistance including MDR-TB was observed. CONCLUSION: This study showed increasing trend in drug resistance including MDR-TB in five years.

Clinico-radiological profile of new smear positive pulmonary tuberculosis cases among young adult and elderly people in a tertiary care hospital at Deheradun (Uttarakhand).

SETTING: Patients of pulmonary tuberculosis (TB) attending the out and in patient department of pulmonary medicine, Himalayan Institute of Medical Sciences (HIMS), a post graduate institute and a large tertiary care center in Dehradun. OBJECTIVE: To compare the clinico-radiological pattern of pulmonary tuberculosis in the young adult (18-59 years) and elderly (> or = 60 years) patients. DESIGN: Prospective observational study of pulmonary and associated extra pulmonary tuberculosis cases, diagnosed between October 2005 to September 2006 in pulmonary medicine department of HIMS. RESULT: Mean age of young adult and elderly patients was 35.71 +/- 5.7 years and 68.57 +/- 3.03 years respectively. Elderly patients had a higher number of co-morbidities like diabetes mellitus, hypertension, and malignancy. Tuberculin positivity was less among elderly patients (36.0%) as compared to young adults (65.9%). Hemoptysis (29.5% vs. 6%), fever (95.4% vs. 76%) and night sweats (54.5% vs. 18.0%) were significantly higher in the young adult patients than the elderly. As for roentgenographic abnormalities, a higher involvement of lower zone (24.0% vs. 7.9%) and far advanced lesions (32.0% vs. 14.7%) were seen in the elderly patients as compared to young adults. The elderly showed a higher frequency of TB related mortality (8% vs. 1.1%) and associated extra pulmonary involvement (40% vs. 7%). CONCLUSION: Young adults are more likely to have hemoptysis, night sweats and positive PPD response while lower lung field involvement is more common in elderly.