高级检索
当前位置: 首页 > 详情页

Model-based assessment of cardiopulmonary autonomic regulation in paced deep breathing

文献详情

资源类型:
WOS体系:

收录情况: ◇ SCIE

单位: [1]Univ Chinese Acad Sci, Sensor Networks & Applicat Res Ctr, Beijing 101408, Peoples R China [2]CAS Inst Healthcare Technol, Nanjing 210046, Peoples R China [3]China Japan Friendship Hosp, Dept Cardiol Integrated Chinese & Western Med, Beijing 100029, Peoples R China [4]Univ Leeds, Sch Elect & Elect Engn, Leeds LS2 9JT, England
出处:
ISSN:

关键词: Autonomic nerve Respiratory sinus arrhythmia Cardiopulmonary differential equation model Cardiac measurement

摘要:
Autonomic dysfunction can lead to many physical and psychological diseases. The assessment of autonomic regulation plays an important role in the prevention, diagnosis, and treatment of these diseases. A physiopathological mathematical model for cardiopulmonary autonomic regulation, namely Respiratory-AutonomicSinus (RSA) regulation Model, is proposed in this study. A series of differential equations are used to simulate the whole process of RSA phenomenon. Based on this model, with respiration signal and ECG signal simultaneously acquired in paced deep breathing scenario, we manage to obtain the cardiopulmonary autonomic regulation parameters (CARP), including the sensitivity of respiratory-sympathetic nerves and respiratoryparasympathetic nerves, the time delay of sympathetic, the sensitivity of norepinephrine and acetylcholine receptor, as well as cardiac remodeling factor by optimization algorithm. An experimental study has been conducted in healthy subjects, along with subjects with hypertension and coronary heart disease. CARP obtained in the experiment have shown their clinical significance.

语种:
WOS:
中科院(CAS)分区:
出版当年[2021]版:
大类 | 3 区 生物学
小类 | 3 区 生化研究方法 3 区 生化与分子生物学
最新[2025]版:
大类 | 3 区 生物学
小类 | 2 区 生化研究方法 3 区 生化与分子生物学
JCR分区:
出版当年[2020]版:
Q2 BIOCHEMICAL RESEARCH METHODS Q3 BIOCHEMISTRY & MOLECULAR BIOLOGY
最新[2023]版:
Q1 BIOCHEMICAL RESEARCH METHODS Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY

影响因子: 最新[2023版] 最新五年平均[2021-2025] 出版当年[2020版] 出版当年五年平均[2016-2020] 出版前一年[2019版] 出版后一年[2021版]

第一作者:
第一作者单位: [1]Univ Chinese Acad Sci, Sensor Networks & Applicat Res Ctr, Beijing 101408, Peoples R China
通讯作者:
推荐引用方式(GB/T 7714):
APA:
MLA:

资源点击量:1320 今日访问量:0 总访问量:816 更新日期:2025-04-01 建议使用谷歌、火狐浏览器 常见问题

版权所有:重庆聚合科技有限公司 渝ICP备12007440号-3 地址:重庆市两江新区泰山大道西段8号坤恩国际商务中心16层(401121)