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Deka B, Deka D. Nonlinear analysis of heart rate variability signals in meditative state: a review and perspective. Biomed Eng Online 2023; 22:35. [PMID: 37055770 PMCID: PMC10103447 DOI: 10.1186/s12938-023-01100-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Accepted: 04/03/2023] [Indexed: 04/15/2023] Open
Abstract
INTRODUCTION In recent times, an upsurge in the investigation related to the effects of meditation in reconditioning various cardiovascular and psychological disorders is seen. In majority of these studies, heart rate variability (HRV) signal is used, probably for its ease of acquisition and low cost. Although understanding the dynamical complexity of HRV is not an easy task, the advances in nonlinear analysis has significantly helped in analyzing the impact of meditation of heart regulations. In this review, we intend to present the various nonlinear approaches, scientific findings and their limitations to develop deeper insights to carry out further research on this topic. RESULTS Literature have shown that research focus on nonlinear domain is mainly concentrated on assessing predictability, fractality, and entropy-based dynamical complexity of HRV signal. Although there were some conflicting results, most of the studies observed a reduced dynamical complexity, reduced fractal dimension, and decimated long-range correlation behavior during meditation. However, techniques, such as multiscale entropy (MSE) and multifractal analysis (MFA) of HRV can be more effective in analyzing non-stationary HRV signal, which were hardly used in the existing research works on meditation. CONCLUSIONS After going through the literature, it is realized that there is a requirement of a more rigorous research to get consistent and new findings about the changes in HRV dynamics due to the practice of meditation. The lack of adequate standard open access database is a concern in drawing statistically reliable results. Albeit, data augmentation technique is an alternative option to deal with this problem, data from adequate number of subjects can be more effective. Multiscale entropy analysis is scantily employed in studying the effect of meditation, which probably need more attention along with multifractal analysis. METHODS Scientific databases, namely PubMed, Google Scholar, Web of Science, Scopus were searched to obtain the literature on "HRV analysis during meditation by nonlinear methods". Following an exclusion criteria, 26 articles were selected to carry out this scientific analysis.
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Affiliation(s)
- Bhabesh Deka
- Department of ECE, School of Engineering, Tezpur University, Assam, India.
| | - Dipen Deka
- Department of ECE, School of Engineering, Tezpur University, Assam, India
- Department of Instrumentation Engineering, Central Institute of Technology, Kokrajhar, India
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Zhang Q, Tian X, Chen G, Yu Z, Zhang X, Lu J, Zhang J, Wang P, Hao X, Huang Y, Wang Z, Gao F, Yang J. A Prediction Model for Tacrolimus Daily Dose in Kidney Transplant Recipients With Machine Learning and Deep Learning Techniques. Front Med (Lausanne) 2022; 9:813117. [PMID: 35712101 PMCID: PMC9197124 DOI: 10.3389/fmed.2022.813117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Accepted: 04/22/2022] [Indexed: 11/13/2022] Open
Abstract
Tacrolimus is a major immunosuppressor against post-transplant rejection in kidney transplant recipients. However, the narrow therapeutic index of tacrolimus and considerable variability among individuals are challenges for therapeutic outcomes. The aim of this study was to compare different machine learning and deep learning algorithms and establish individualized dose prediction models by using the best performing algorithm. Therefore, among the 10 commonly used algorithms we compared, the TabNet algorithm outperformed other algorithms with the highest R2 (0.824), the lowest prediction error [mean absolute error (MAE) 0.468, mean square error (MSE) 0.558, and root mean square error (RMSE) 0.745], and good performance of overestimated (5.29%) or underestimated dose percentage (8.52%). In the final prediction model, the last tacrolimus daily dose, the last tacrolimus therapeutic drug monitoring value, time after transplantation, hematocrit, serum creatinine, aspartate aminotransferase, weight, CYP3A5, body mass index, and uric acid were the most influential variables on tacrolimus daily dose. Our study provides a reference for the application of deep learning technique in tacrolimus dose estimation, and the TabNet model with desirable predictive performance is expected to be expanded and applied in future clinical practice.
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Affiliation(s)
- Qiwen Zhang
- Department of Pharmacy, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Henan Key Laboratory of Precision Clinical Pharmacy, Zhengzhou University, Zhengzhou, China
| | - Xueke Tian
- Department of Pharmacy, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Henan Key Laboratory of Precision Clinical Pharmacy, Zhengzhou University, Zhengzhou, China
| | - Guang Chen
- Department of Pharmacy, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Henan Key Laboratory of Precision Clinical Pharmacy, Zhengzhou University, Zhengzhou, China
| | - Ze Yu
- Beijing Medicinovo Technology Co. Ltd, Beijing, China
| | - Xiaojian Zhang
- Department of Pharmacy, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Henan Key Laboratory of Precision Clinical Pharmacy, Zhengzhou University, Zhengzhou, China
| | - Jingli Lu
- Department of Pharmacy, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Henan Key Laboratory of Precision Clinical Pharmacy, Zhengzhou University, Zhengzhou, China
| | - Jinyuan Zhang
- Beijing Medicinovo Technology Co. Ltd, Beijing, China
| | - Peile Wang
- Department of Pharmacy, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Henan Key Laboratory of Precision Clinical Pharmacy, Zhengzhou University, Zhengzhou, China
| | - Xin Hao
- Dalian Medicinovo Technology Co. Ltd, Dalian, China
| | - Yining Huang
- McCormick School of Engineering, Northwestern University, Evanston, IL, United States
| | - Zeyuan Wang
- Beijing Medicinovo Technology Co. Ltd, Beijing, China
| | - Fei Gao
- Beijing Medicinovo Technology Co. Ltd, Beijing, China
| | - Jing Yang
- Department of Pharmacy, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Henan Key Laboratory of Precision Clinical Pharmacy, Zhengzhou University, Zhengzhou, China
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