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• W3126662834 startingPage "102414" @default.
• W3126662834 abstract "Meditation and yoga have growing popularity in recent times due to its effectiveness in reducing stress, anxiety, and elevating overall well-being. To study its exertion on human being, HRV analysis is found to be an appropriate tool. The results of meditation may vary heterogeneously for different subjects depending on experience, expertise of performance, besides the disparity in internal and external dynamic interactions. It poses the challenge to extract discriminating features and robust classifier for separating the meditative state from the non-meditative state. In this paper, we have proposed a new parameter, named as the standard deviation of second order differences of RR intervals to capture the underlying dynamics of HRV during meditation. Besides, we have selected 8 more discriminating and non-redundant parameters based on significance test and correlation coefficient measures to classify the two states. Considering the small data size, the SVM classification model is cross-validated using leave-one-subject's-one-state-out cross-validation (LOSOSOCV). Hyperparameters of the SVM model are chosen for each subject's each state (meditative/pre-meditative) using the Bayesian optimizer. The proposed approach provides a classification accuracy of 95.31% and F1 score of 0.9523. Furthermore, this study has also demonstrated the analysis of findings from different parameters in capturing the underlying dynamics of HRV during the practice of meditation." @default.
• W3126662834 created "2021-02-15" @default.
• W3126662834 creator A5006524000 @default.
• W3126662834 creator A5079984317 @default.
• W3126662834 date "2021-04-01" @default.
• W3126662834 modified "2023-10-16" @default.
• W3126662834 title "Characterization of heart rate variability signal for distinction of meditative and pre-meditative states" @default.
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• W3126662834 doi "https://doi.org/10.1016/j.bspc.2021.102414" @default.
• W3126662834 hasPublicationYear "2021" @default.
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