用于定量评估心脏自律神经对正压力反应的计算模型。

IF 2.3 4区 医学 Q3 BIOPHYSICS Physiological measurement Pub Date : 2024-07-29 DOI:10.1088/1361-6579/ad63ee
Tao Wang, JianKang Wu, Fei Qin, Hong Jiang, Xiang Xiao, ZhiPei Huang
{"title":"用于定量评估心脏自律神经对正压力反应的计算模型。","authors":"Tao Wang, JianKang Wu, Fei Qin, Hong Jiang, Xiang Xiao, ZhiPei Huang","doi":"10.1088/1361-6579/ad63ee","DOIUrl":null,"url":null,"abstract":"<p><p><i>Objective.</i>The autonomic nervous system (ANS) plays a critical role in regulating not only cardiac functions but also various other physiological processes, such as respiratory rate, digestion, and metabolic activities. The ANS is divided into the sympathetic and parasympathetic nervous systems, each of which has distinct but complementary roles in maintaining homeostasis across multiple organ systems in response to internal and external stimuli. Early detection of ANS dysfunctions, such as imbalances between the sympathetic and parasympathetic branches or impairments in the autonomic regulation of bodily functions, is crucial for preventing or slowing the progression of cardiovascular diseases. These dysfunctions can manifest as irregularities in heart rate, blood pressure regulation, and other autonomic responses essential for maintaining cardiovascular health. Traditional methods for analyzing ANS activity, such as heart rate variability (HRV) analysis and muscle sympathetic nerve activity recording, have been in use for several decades. Despite their long history, these techniques face challenges such as poor temporal resolution, invasiveness, and insufficient sensitivity to individual physiological variations, which limit their effectiveness in personalized health assessments.<i>Approach.</i>This study aims to introduce the open-loop Mathematical Model of Autonomic Regulation of the Cardiac System under Supine-to-stand Maneuver (MMARCS) to overcome the limitations of existing ANS analysis methods. The MMARCS model is designed to offer a balance between physiological fidelity and simplicity, focusing on the ANS cardiac control subsystems' input-output curve. The MMARCS model simplifies the complex internal dynamics of ANS cardiac control by emphasizing input-output relationships and utilizing sensitivity analysis and parameter subset selection to increase model specificity and eliminate redundant parameters. This approach aims to enhance the model's capacity for personalized health assessments.<i>Main results.</i>The application of the MMARCS model revealed significant differences in ANS regulation between healthy (14 females and 19 males, age: 42 ± 18) and diabetic subjects (8 females and 6 males, age: 47 ± 14). Parameters indicated heightened sympathetic activity and diminished parasympathetic response in diabetic subjects compared to healthy subjects (<i>p</i> < 0.05). Additionally, the data suggested a more sensitive and potentially more reactive sympathetic response among diabetic subjects (<i>p</i> < 0.05), characterized by increased responsiveness and intensity of the sympathetic nervous system to stimuli, i.e. fluctuations in blood pressure, leading to more pronounced changes in heart rate, these phenomena can be directly reflected by gain parameters and time response parameters of the model.<i>Significance.</i>The MMARCS model represents an innovative computational approach for quantifying ANS functionality. This model guarantees the accuracy of physiological modeling while reducing mathematical complexity, offering an easy-to-implement and widely applicable tool for clinical measurements of cardiovascular health, disease progression monitoring, and home health monitoring through wearable technology.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":null,"pages":null},"PeriodicalIF":2.3000,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Computational modeling for the quantitative assessment of cardiac autonomic response to orthostatic stress.\",\"authors\":\"Tao Wang, JianKang Wu, Fei Qin, Hong Jiang, Xiang Xiao, ZhiPei Huang\",\"doi\":\"10.1088/1361-6579/ad63ee\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p><i>Objective.</i>The autonomic nervous system (ANS) plays a critical role in regulating not only cardiac functions but also various other physiological processes, such as respiratory rate, digestion, and metabolic activities. The ANS is divided into the sympathetic and parasympathetic nervous systems, each of which has distinct but complementary roles in maintaining homeostasis across multiple organ systems in response to internal and external stimuli. Early detection of ANS dysfunctions, such as imbalances between the sympathetic and parasympathetic branches or impairments in the autonomic regulation of bodily functions, is crucial for preventing or slowing the progression of cardiovascular diseases. These dysfunctions can manifest as irregularities in heart rate, blood pressure regulation, and other autonomic responses essential for maintaining cardiovascular health. Traditional methods for analyzing ANS activity, such as heart rate variability (HRV) analysis and muscle sympathetic nerve activity recording, have been in use for several decades. Despite their long history, these techniques face challenges such as poor temporal resolution, invasiveness, and insufficient sensitivity to individual physiological variations, which limit their effectiveness in personalized health assessments.<i>Approach.</i>This study aims to introduce the open-loop Mathematical Model of Autonomic Regulation of the Cardiac System under Supine-to-stand Maneuver (MMARCS) to overcome the limitations of existing ANS analysis methods. The MMARCS model is designed to offer a balance between physiological fidelity and simplicity, focusing on the ANS cardiac control subsystems' input-output curve. The MMARCS model simplifies the complex internal dynamics of ANS cardiac control by emphasizing input-output relationships and utilizing sensitivity analysis and parameter subset selection to increase model specificity and eliminate redundant parameters. This approach aims to enhance the model's capacity for personalized health assessments.<i>Main results.</i>The application of the MMARCS model revealed significant differences in ANS regulation between healthy (14 females and 19 males, age: 42 ± 18) and diabetic subjects (8 females and 6 males, age: 47 ± 14). Parameters indicated heightened sympathetic activity and diminished parasympathetic response in diabetic subjects compared to healthy subjects (<i>p</i> < 0.05). Additionally, the data suggested a more sensitive and potentially more reactive sympathetic response among diabetic subjects (<i>p</i> < 0.05), characterized by increased responsiveness and intensity of the sympathetic nervous system to stimuli, i.e. fluctuations in blood pressure, leading to more pronounced changes in heart rate, these phenomena can be directly reflected by gain parameters and time response parameters of the model.<i>Significance.</i>The MMARCS model represents an innovative computational approach for quantifying ANS functionality. This model guarantees the accuracy of physiological modeling while reducing mathematical complexity, offering an easy-to-implement and widely applicable tool for clinical measurements of cardiovascular health, disease progression monitoring, and home health monitoring through wearable technology.</p>\",\"PeriodicalId\":20047,\"journal\":{\"name\":\"Physiological measurement\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2024-07-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Physiological measurement\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1088/1361-6579/ad63ee\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"BIOPHYSICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Physiological measurement","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1088/1361-6579/ad63ee","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"BIOPHYSICS","Score":null,"Total":0}
引用次数: 0

摘要

自律神经系统(ANS)在调节心脏功能方面起着至关重要的作用。早期发现自律神经系统功能障碍对于预防或减缓心血管疾病的发展至关重要。目前分析 ANS 活动的方法,如心率变异性分析和肌肉交感神经活动记录,面临着时间分辨率低、侵入性强、对个体生理变化不够敏感等挑战,从而限制了个性化健康评估。本研究旨在引入 "仰卧起坐动作下心脏系统自主神经调节开环数学模型"(MMARCS),以克服现有 ANS 分析方法的局限性。MMARCS 模型的设计兼顾了生理逼真性和简便性,重点关注自律神经系统心脏控制子系统的输入-输出曲线。MMARCS 模型通过强调输入输出关系、利用灵敏度分析和参数子集选择来提高模型的特异性并消除冗余参数,从而简化了自律神经系统心脏控制的复杂内部动态。这种方法旨在提高模型的个性化健康评估能力。MMARCS 模型的应用揭示了健康受试者(14 名女性和 19 名男性)与糖尿病受试者(8 名女性和 6 名男性)在自律神经系统调节方面的显著差异。参数显示,与健康受试者相比,糖尿病受试者的交感神经活动增强,副交感神经反应减弱(p
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Computational modeling for the quantitative assessment of cardiac autonomic response to orthostatic stress.

Objective.The autonomic nervous system (ANS) plays a critical role in regulating not only cardiac functions but also various other physiological processes, such as respiratory rate, digestion, and metabolic activities. The ANS is divided into the sympathetic and parasympathetic nervous systems, each of which has distinct but complementary roles in maintaining homeostasis across multiple organ systems in response to internal and external stimuli. Early detection of ANS dysfunctions, such as imbalances between the sympathetic and parasympathetic branches or impairments in the autonomic regulation of bodily functions, is crucial for preventing or slowing the progression of cardiovascular diseases. These dysfunctions can manifest as irregularities in heart rate, blood pressure regulation, and other autonomic responses essential for maintaining cardiovascular health. Traditional methods for analyzing ANS activity, such as heart rate variability (HRV) analysis and muscle sympathetic nerve activity recording, have been in use for several decades. Despite their long history, these techniques face challenges such as poor temporal resolution, invasiveness, and insufficient sensitivity to individual physiological variations, which limit their effectiveness in personalized health assessments.Approach.This study aims to introduce the open-loop Mathematical Model of Autonomic Regulation of the Cardiac System under Supine-to-stand Maneuver (MMARCS) to overcome the limitations of existing ANS analysis methods. The MMARCS model is designed to offer a balance between physiological fidelity and simplicity, focusing on the ANS cardiac control subsystems' input-output curve. The MMARCS model simplifies the complex internal dynamics of ANS cardiac control by emphasizing input-output relationships and utilizing sensitivity analysis and parameter subset selection to increase model specificity and eliminate redundant parameters. This approach aims to enhance the model's capacity for personalized health assessments.Main results.The application of the MMARCS model revealed significant differences in ANS regulation between healthy (14 females and 19 males, age: 42 ± 18) and diabetic subjects (8 females and 6 males, age: 47 ± 14). Parameters indicated heightened sympathetic activity and diminished parasympathetic response in diabetic subjects compared to healthy subjects (p < 0.05). Additionally, the data suggested a more sensitive and potentially more reactive sympathetic response among diabetic subjects (p < 0.05), characterized by increased responsiveness and intensity of the sympathetic nervous system to stimuli, i.e. fluctuations in blood pressure, leading to more pronounced changes in heart rate, these phenomena can be directly reflected by gain parameters and time response parameters of the model.Significance.The MMARCS model represents an innovative computational approach for quantifying ANS functionality. This model guarantees the accuracy of physiological modeling while reducing mathematical complexity, offering an easy-to-implement and widely applicable tool for clinical measurements of cardiovascular health, disease progression monitoring, and home health monitoring through wearable technology.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Physiological measurement
Physiological measurement 生物-工程:生物医学
CiteScore
5.50
自引率
9.40%
发文量
124
审稿时长
3 months
期刊介绍: Physiological Measurement publishes papers about the quantitative assessment and visualization of physiological function in clinical research and practice, with an emphasis on the development of new methods of measurement and their validation. Papers are published on topics including: applied physiology in illness and health electrical bioimpedance, optical and acoustic measurement techniques advanced methods of time series and other data analysis biomedical and clinical engineering in-patient and ambulatory monitoring point-of-care technologies novel clinical measurements of cardiovascular, neurological, and musculoskeletal systems. measurements in molecular, cellular and organ physiology and electrophysiology physiological modeling and simulation novel biomedical sensors, instruments, devices and systems measurement standards and guidelines.
期刊最新文献
Interhemispheric asynchrony of NREM EEG at the beginning and end of sleep describes evening vigilance performance in patients undergoing diagnostic polysomnography. Assessment of arteriosclerosis based on lognormal fitting. Sulfur hexafluoride multiple breath washin and washout outcomes in infants are not interchangeable. Fraction of reverse impedance change (FRIC): a quantitative electrical impedance tomography measure of intrapulmonary pendelluft. Corrigendum: Highly comparative time series analysis of oxygen saturation and heart rate to predict respiratory outcomes in extremely preterm infants (2024Physiol. Meas. 45 055025).
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1