Sushil Chandra’s scientific contributions

The ability to detect and prevent mental health deterioration has been one of the major achievements of digital psychiatry using artificial intelligence and machine learning. The aim of this paper is to address the issue of preventing the mental health disorders of young generation by developing a system to predict the changes in an individual's states of psychological health. Pre-and post-yoga and Rajyoga meditation states were analyzed for classification of data. Also, the paper investigates if bidirectional long-short-term memory BiLSTM-based ensemble models outperform the CNN-based models in prediction modeling. The EEG data was collected from 69 students for pre-and post-intervention. To determine an objective marker for yoga and meditation, collected data were analyzed using spectrum analysis, and classification. The post meditation group exhibited highest band powers and wavelet coefficients, indicating the differences in meditation and control conditions. Additionally, in this study, an ensemble model classifier has been developed utilizing EEG data that was more accurate (82%) than other models at differentiating between meditation and control situations. To the best of the knowledge of the authors, this is the first research to apply ensemble model-based classifiers to distinguish between states of meditation and non-meditation. The performance of BiLSTM-DT was the highest among all other models in terms of precision, recall, f-measure, and accuracy. Therefore, the BiLSTM-DT ensemble model is a viable objective marker for psychological health states.

The full text of this preprint has been withdrawn by the authors while they make corrections to the work. Therefore, the authors do not wish this work to be cited as a reference. Questions should be directed to the corresponding author.

Background: The current study reported the stigma of mental health issues among young college students, and analysed the effectiveness of Indian therapeutic interventions, Yoga and Rajyoga meditation, in improving the brain dynamics of Yoga college students. This study is the first of its kind that can provide the wavelet decomposition-based EEG features and the cognitive PEBL task features as neurophysiological markers. Methods: Electroencephalographic signals and scores from PEBL battery tasks were recorded during performing PEBL tasks before and after eight weeks of intervention. Results: The post-intervention meditators group demonstrated a substantial improvement in the average scores and memory span in the cognition battery tasks. Additionally, theta, alpha, and beta band powers were higher for post-meditators in frontal, temporal, and parietal regions during CORSI and SIMON tasks. Conclusion: The findings suggest that the combination of Yoga and Rajyoga meditation practice leads to increase in spatial attention, spatial memory, and working memory.

Issues of providing mental health support to people with emerging or current mental health disorders are becoming a significant concern throughout the world. One of the biggest effects of digital psychiatry during COVID-19 is its capacity for early identification and forecasting of a person's mental health decline resulting in chronic mental health issues. Therefore, through this study aims at addressing the hological problems by identifying people who are more likely to acquire mental health issues induced by COVID-19 epidemic. To achieve this goal, this study includes 1) Rajyoga practitioners' perceptions of psychological effects, levels of anxiety, stress, and depression are compared to those of the non practitioners 2) Predictions of mental health disorders such as stress, anxiety and depression using machine learning algorithms using the online survey data collected from Rajyoga meditators and general the population. Decision tree, random forest, naive bayeBayespport vector machine and K nearest neighbor algorithms were used for the prediction as they have been shown to be more accurate for predicting psychological disorders. The support vector machine showed the highest accuracy among all other algorithms. The f1 score was also the highest for support vector machine.

This research work presents a study on the relationship between stress & related events with meditation practice and other socio-demographic variables during COVID 19 pandemic among healthy adults. In this cross-sectional survey design, healthy adults with and without practice of Raja yoga meditation completed stress, anxiety & depression related questions (Depression Anxiety & stress Scale, DASS 21) and its impact (Impact of Event Scale-Revised, IES-R) along with other socio-demographic including COVID infection or contact related information. Data was assessed for difference in DASS 21 scores and IES-R scores between Raja yoga meditators(n=802) & non-meditators(n=357). An analysis was performed to study the predictors of DASS 21 and IES-R scores. We conclude that healthy Raja yoga meditation practitioners differ from non-meditators in terms of stress/anxiety/depression and its impact during COVID 19 pandemic and meditation practice predicts mental health better along with other sociodemographic variables.

The purpose of this study is to present the Hindi translation and validation of the Impact of Event Scale-Revised and to evaluate psychometric qualities of this scale in a sample of regular Rajyoga meditators to examine the psychological impact of Coronavirus on them. The convenience sampling method was used to collect the data from 801 Rajyoga meditators through online survey. Data were analysed using SPSS 26.0. The Hindi version of IES-R demonstrated good internal consistency with the value of alpha coefficient being 0.91 for the scale and ranging between 0.81 to 0.83 for the subscales. The correlations between the subscales varied between 0.55 and 0.66. Principal components analysis using Varimax rotation was run with three-factor solution based on eigen value greater than one. This solution explained 54 percent of the total variance. It generated mainly two factors, an intrusion hyperarousal factor and an avoidance factor and third factor with one item only. Only 4.7 percent of the meditators rated the outbreak's psychological impact as moderate or severe. The mean score of IES-R was 10.01 (with an S.D. of 11.107). Significant positive correlations were found among IES-R scores and the presence of COVID symptoms. Thus, in clinical and research contexts, the scale appears to be a valid measure of post-trauma occurrences. The present study was conducted to generate a validated Hindi version of the IES-R that is easier and more compatible for use in the Indian population.