BackgroundMeditation 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. Materials and methodsEEG data were collected from 34 participants. The functional connectivity parameters, correlation coefficient, clustering coefficient, shortest path, and phase locking value were utilized as a feature vector for classification. To evaluate the various states of HM practice, the categorization was done between (LTM, NM) and (STM, NM) using a multitude of machine learning classifiers. ResultsThe classifier's performances were evaluated based on accuracy using 10-fold cross-validation. The results showed that the accuracy of machine learning models ranges from 84% to 100% while classifying LTM and NM, and accuracy from 80% to 93% while classifying STM and NM. It was found that decision trees, support vector machines, k-nearest neighbors, and ensemble classifiers performed better than linear discriminant analysis and logistic regression. ConclusionThis is the first study to our knowledge employing machine learning for the classification among HM meditators and NM The results indicated that machine learning classifiers with EEG functional connectivity as a feature vector could be a viable marker for accessing meditation ability.
Introduction: Poor emotion regulation (ER) is linked to diabetes distress and depression that may contribute to uncontrolled glycemic levels among type 2 diabetes mellitus (T2DM) patients. As ER can adversely affect the physiological and psychological health of patients with T2DM, holistic management of the disease is essential. Yoga therapy is one such method that can positively impact both the mental and physical health of T2DM patients. Methods: Individuals with T2DM (n = 54) were recruited for the study and were randomly allocated to the intervention (yoga) group and control (conventional treatment) group. Cognitive reappraisal (CR) and expressive suppression (ES) were assessed as ER skills, and mindfulness was evaluated before and after the intervention. The intervention was provided for 3 months. Results: Participants of the yoga group showed an improved ER ability with increased CR and decreased ES. However, these changes were not statistically significant. ES was significantly reduced (p < 0.05) in the control group. In addition, the yoga group showed significantly increased (p < 0.05) mindfulness and was decreased in the control group. Conclusion: Yoga therapy positively affects the psychological well-being of T2DM patients.
Context: Heartfulness meditation (HM) is a heart-based meditation with its unique feature of transmitting energy which may have an impact on mental health and well-being. The present study intends to compare the mental health-related outcomes in long-term HM meditators (LTM), short-term HM meditators (STM), and control groups (CTL). Materials and Methods: The self-reported measures of mental health and well-being are reported by using State Trait Anxiety Inventory-II, Barratt Impulsive Scale-11, Mindfulness Attention Awareness Scale, Meditation Depth Questionnaire, and World Health Organization Quality of life-BREF. A total of 79 participants (29 females) participated in LTM (n = 28), STM (n = 26), and CTL (n = 25) with age range 30.09 ± 6.3 years. Results: The LTM and STM groups showed higher mindfulness along with the depth of meditation, quality of life, and lower anxiety and impulsivity than to CTL group. Our findings suggest that the HM practice enhances mindfulness, reduces anxiety, and regulates impulsivity. The LTM and STM groups showed significant positive trends of mindfulness as compared to CTL. Conclusion: The results indicated that HM practice could be an effective intervention for reducing anxious and impulsive behavior by subsequently improving mindfulness-related mental health and well-being.
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. Methods: Sixty-one participants were recruited, 28 heartfulness meditators (average age male: 31.54 ± 4.2 years and female: 30.04 ± 7.1 years) and 33 nonmeditators (average age male: 25 ± 8.5 years and female: 23.45 ± 6.5 years). An electroencephalogram (EEG) was employed to assess brain activity during baseline (5 min), meditation (10 min), transmission (10 min) and post (5 min). Self-reported mindfulness and anxiety were also collected in the present study. The EEG power spectral density (PSD) and coherence were processed using MATLAB. The statistical analysis was performed using an independent sample t -test for trait mindfulness and anxiety, repeated measures analysis of variance (ANOVA) for state mindfulness and anxiety, and Two-way multivariate ANOVA for EEG spectral frequency and coherence. Results: The results showed higher state and trait mindfulness, P < 0.05 and P < 0.01, respectively, and lower state and trait anxiety, P < 0.05 and P < 0.05, respectively. The PSD outcomes showed higher theta ( P < 0.001) and alpha ( P < 0.01); lower beta ( P < 0.001) and delta ( P < 0.05) power in HM meditators compared to nonmeditators. Similarly, higher coherence was found in the theta ( P < 0.01), alpha ( P < 0.05), and beta ( P < 0.01) bands in HM meditators. Conclusions: These findings suggest that HM practice may result in wakeful relaxation and internalized attention that can influence cognition and behavior.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.