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Zhang T, Dong X, Wang D, Huang C, Zhang XD. RespirAnalyzer: an R package for analyzing data from continuous monitoring of respiratory signals. BIOINFORMATICS ADVANCES 2024; 4:vbae003. [PMID: 38269257 PMCID: PMC10807906 DOI: 10.1093/bioadv/vbae003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/22/2023] [Revised: 11/30/2023] [Accepted: 01/11/2024] [Indexed: 01/26/2024]
Abstract
Motivation The analysis of data obtained from continuous monitoring of respiratory signals (CMRS) holds significant importance in improving patient care, optimizing sports performance, and advancing scientific understanding in the field of respiratory health. Results The R package RespirAnalyzer provides an analytic tool specifically for feature extraction, fractal and complexity analysis for CMRS data. The package covers a wide and comprehensive range of data analysis methods including obtaining inter-breath intervals (IBI) series, plotting time series, obtaining summary statistics of IBI series, conducting power spectral density, multifractal detrended fluctuation analysis (MFDFA) and multiscale sample entropy analysis, fitting the MFDFA results with the extended binomial multifractal model, displaying results using various plots, etc. This package has been developed from our work in directly analyzing CMRS data and is anticipated to assist fellow researchers in computing the related features of their CMRS data, enabling them to delve into the clinical significance inherent in these features. Availability and implementation The package for Windows is available from both Comprehensive R Archive Network (CRAN): https://cran.r-project.org/web/packages/RespirAnalyzer/index.html and GitHub: https://github.com/dongxinzheng/RespirAnalyzer.
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Affiliation(s)
- Teng Zhang
- Department of Public Health and Medicinal Administration, Faculty of Health Sciences, University of Macau, Taipa, Macau 999078, China
| | - Xinzheng Dong
- Zhuhai Laboratory of Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Zhuhai College of Science and Technology, Zhuhai 519041, China
| | - Dandan Wang
- Department of Public Health and Medicinal Administration, Faculty of Health Sciences, University of Macau, Taipa, Macau 999078, China
| | - Chen Huang
- Dr. Neher's Biophysics Laboratory for Innovative Drug Discovery, Macau University of Science and Technology, Taipa, Macau 999078, China
| | - Xiaohua Douglas Zhang
- Department of Biostatistics, University of Kentucky, Lexington, KY 40536, United States
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Li C, Ma X, Lu J, Tao R, Yu X, Mo Y, Lu W, Bao Y, Zhou J, Jia W. Decreasing complexity of glucose time series derived from continuous glucose monitoring is correlated with deteriorating glucose regulation. Front Med 2022; 17:68-74. [PMID: 36562949 DOI: 10.1007/s11684-022-0955-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 07/20/2022] [Indexed: 12/24/2022]
Abstract
Most information used to evaluate diabetic statuses is collected at a special time-point, such as taking fasting plasma glucose test and providing a limited view of individual's health and disease risk. As a new parameter for continuously evaluating personal clinical statuses, the newly developed technique "continuous glucose monitoring" (CGM) can characterize glucose dynamics. By calculating the complexity of glucose time series index (CGI) with refined composite multi-scale entropy analysis of the CGM data, the study showed for the first time that the complexity of glucose time series in subjects decreased gradually from normal glucose tolerance to impaired glucose regulation and then to type 2 diabetes (P for trend < 0.01). Furthermore, CGI was significantly associated with various parameters such as insulin sensitivity/secretion (all P < 0.01), and multiple linear stepwise regression showed that the disposition index, which reflects β-cell function after adjusting for insulin sensitivity, was the only independent factor correlated with CGI (P < 0.01). Our findings indicate that the CGI derived from the CGM data may serve as a novel marker to evaluate glucose homeostasis.
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Affiliation(s)
- Cheng Li
- Department of Endocrinology and Metabolism, Shanghai Jiao Tong University School of Medicine Affiliated Sixth People's Hospital, Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai, 200233, China
| | - Xiaojing Ma
- Department of Endocrinology and Metabolism, Shanghai Jiao Tong University School of Medicine Affiliated Sixth People's Hospital, Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai, 200233, China
| | - Jingyi Lu
- Department of Endocrinology and Metabolism, Shanghai Jiao Tong University School of Medicine Affiliated Sixth People's Hospital, Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai, 200233, China
| | - Rui Tao
- College of Information Science and Engineering, Northeastern University, Shenyang, 110819, China
| | - Xia Yu
- College of Information Science and Engineering, Northeastern University, Shenyang, 110819, China
| | - Yifei Mo
- Department of Endocrinology and Metabolism, Shanghai Jiao Tong University School of Medicine Affiliated Sixth People's Hospital, Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai, 200233, China
| | - Wei Lu
- Department of Endocrinology and Metabolism, Shanghai Jiao Tong University School of Medicine Affiliated Sixth People's Hospital, Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai, 200233, China
| | - Yuqian Bao
- Department of Endocrinology and Metabolism, Shanghai Jiao Tong University School of Medicine Affiliated Sixth People's Hospital, Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai, 200233, China
| | - Jian Zhou
- Department of Endocrinology and Metabolism, Shanghai Jiao Tong University School of Medicine Affiliated Sixth People's Hospital, Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai, 200233, China.
| | - Weiping Jia
- Department of Endocrinology and Metabolism, Shanghai Jiao Tong University School of Medicine Affiliated Sixth People's Hospital, Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai, 200233, China.
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Dynamic properties of glucose complexity during the course of critical illness: a pilot study. J Clin Monit Comput 2020; 34:361-370. [PMID: 30888595 DOI: 10.1007/s10877-019-00299-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2018] [Accepted: 03/13/2019] [Indexed: 10/27/2022]
Abstract
Methods to control the blood glucose (BG) levels of patients in intensive care units (ICU) improve the outcomes. The development of continuous BG levels monitoring devices has also permitted to optimize these processes. Recently it was shown that a complexity loss of the BG signal is linked to poor clinical outcomes. Thus, it becomes essential to decipher this relation to design efficient BG level control methods. In previous studies the BG signal complexity was calculated as a single index for the whole ICU stay. Although, these approaches did not grasp the potential variability of the BG signal complexity. Therefore, we setup this pilot study using a continuous monitoring of central venous BG levels in ten critically ill patients (EIRUS platform, Maquet Critical CARE AB, Solna, Sweden). Data were processed and the complexity was assessed by the detrended fluctuation analysis and multiscale entropy (MSE) methods. Finally, recordings were split into 24 h overlapping intervals and a MSE analysis was applied to each of them. The MSE analysis on time intervals revealed an entropy variation and allowed periodic BG signal complexity assessments. To highlight differences of MSE between each time interval we calculated the MSE complexity index defined as the area under the curve. This new approach could pave the way to future studies exploring new strategies aimed at restoring blood glucose complexity during the ICU stay.
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Chen C, Sun S, Cao Z, Shi Y, Sun B, Zhang XD. A comprehensive comparison and overview of R packages for calculating sample entropy. Biol Methods Protoc 2019; 4:bpz016. [PMID: 32161808 PMCID: PMC6994089 DOI: 10.1093/biomethods/bpz016] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2019] [Revised: 11/06/2019] [Accepted: 11/17/2019] [Indexed: 11/21/2022] Open
Abstract
Sample entropy is a powerful tool for analyzing the complexity and irregularity of physiology signals which may be associated with human health. Nevertheless, the sophistication of its calculation hinders its universal application. As of today, the R language provides multiple open-source packages for calculating sample entropy. All of which, however, are designed for different scenarios. Therefore, when searching for a proper package, the investigators would be confused on the parameter setting and selection of algorithms. To ease their selection, we have explored the functions of five existing R packages for calculating sample entropy and have compared their computing capability in several dimensions. We used four published datasets on respiratory and heart rate to study their input parameters, types of entropy, and program running time. In summary, NonlinearTseries and CGManalyzer can provide the analysis of sample entropy with different embedding dimensions and similarity thresholds. CGManalyzer is a good choice for calculating multiscale sample entropy of physiological signal because it not only shows sample entropy of all scales simultaneously but also provides various visualization plots. MSMVSampEn is the only package that can calculate multivariate multiscale entropies. In terms of computing time, NonlinearTseries, CGManalyzer, and MSMVSampEn run significantly faster than the other two packages. Moreover, we identify the issues in MVMSampEn package. This article provides guidelines for researchers to find a suitable R package for their analysis and applications using sample entropy.
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Affiliation(s)
- Chang Chen
- Faculty of Health Sciences, University of Macau, Taipa, Macau, China
| | - Shixue Sun
- Faculty of Health Sciences, University of Macau, Taipa, Macau, China
| | - Zhixin Cao
- Department of Respiratory and Critical Care Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
- Beijing Institute of Respiratory Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
- Beijing Engineering Research Center of Respiratory and Critical Care Medicine, Beijing, China
| | - Yan Shi
- School of Automation Science and Electrical Engineering, Beihang University, Beijing, China
| | - Baoqing Sun
- State Key Laboratory of Respiratory Disease, The 1st Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Xiaohua Douglas Zhang
- Faculty of Health Sciences, University of Macau, Taipa, Macau, China
- Department of Biostatistics, Yale University, New Haven, CT06511, USA
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Chen X, Wang D, Lin J, Zhang T, Deng S, Huang L, Jin Y, Chen C, Zhang Z, Zheng J, Sun B, Bogdan P, Zhang XD. Analyzing Complexity and Fractality of Glucose Dynamics in a Pregnant Woman with Type 2 Diabetes under Treatment. Int J Biol Sci 2019; 15:2373-2380. [PMID: 31595155 PMCID: PMC6775315 DOI: 10.7150/ijbs.33825] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2019] [Accepted: 05/27/2019] [Indexed: 11/05/2022] Open
Abstract
Currently, the rapid development of continuous glucose monitoring (CGM) device brings new insights into the treatment of diabetic patients including those during pregnancy. Complexity and fractality have recently under fast development for extracting information embodied in glucose dynamics measured using CGM. Although scientists have investigated the difference of complexity in glucose dynamics between diabetes and non-diabetes in order to discover better approaches for diabetes care, no one has analyzed the complexity and fractality of glucose dynamics during the process of adopting CGM to successfully treat pregnant women with type 2 diabetes. Thus, we analyzed the complexity and fractality using power spectral density (PSD), multi-scale sample entropy (MSE) and multifractal detrended fluctuation analysis (MF-DFA) in a clinical case. Our results show that (i) there exists multifractal behavior in blood glucose dynamics; (ii) the alpha stable distribution fits to the glucose increment data better than the Gaussian distribution; and (iii) the "global" complexity indicated by multiscale entropy, spectrum exponent and Hurst exponent increase and the "local" complexity indicated by multifractal spectrum decrease after the successful therapy. Our results offer findings that may bring value to health care providers for managing glucose levels of pregnant women with type 2 diabetes as well as provide scientists a reference on applying complexity and fractality in the clinical practice of treating diabetes.
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Affiliation(s)
- Xiaoyan Chen
- Department of Endocrinology, First Affiliated Hospital of Guangzhou Medical University, Guangzhou 510000, China
| | - Dandan Wang
- Faculty of Health Sciences, University of Macau, Taipa 999078, Macau
| | - Jinxiang Lin
- Department of Endocrinology, First Affiliated Hospital of Guangzhou Medical University, Guangzhou 510000, China
| | - Teng Zhang
- Faculty of Health Sciences, University of Macau, Taipa 999078, Macau
| | - Shunyou Deng
- Department of Endocrinology, First Affiliated Hospital of Guangzhou Medical University, Guangzhou 510000, China
| | - Lianyi Huang
- Department of Endocrinology, First Affiliated Hospital of Guangzhou Medical University, Guangzhou 510000, China
| | - Yu Jin
- Faculty of Health Sciences, University of Macau, Taipa 999078, Macau
| | - Chang Chen
- Faculty of Health Sciences, University of Macau, Taipa 999078, Macau
| | - Zhaozhi Zhang
- Department of Statistical Science, Duke University, Durham, NC 27708, USA
| | - Jun Zheng
- Faculty of Health Sciences, University of Macau, Taipa 999078, Macau
| | - Baoqing Sun
- Department of Allergy and Clinical Immunology, Guangzhou Institute of Respiratory Diseases, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, First Affiliated Hospital of Guangzhou Medical University, Guangzhou 510000, China
| | - Paul Bogdan
- Department of Electrical Engineering - Systems, University of Southern California, CA 90089, USA
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Dong X, Chen C, Geng Q, Cao Z, Chen X, Lin J, Jin Y, Zhang Z, Shi Y, Zhang XD. An Improved Method of Handling Missing Values in the Analysis of Sample Entropy for Continuous Monitoring of Physiological Signals. ENTROPY 2019; 21:e21030274. [PMID: 33266989 PMCID: PMC7514754 DOI: 10.3390/e21030274] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/11/2019] [Revised: 03/08/2019] [Accepted: 03/09/2019] [Indexed: 11/17/2022]
Abstract
Medical devices generate huge amounts of continuous time series data. However, missing values commonly found in these data can prevent us from directly using analytic methods such as sample entropy to reveal the information contained in these data. To minimize the influence of missing points on the calculation of sample entropy, we propose a new method to handle missing values in continuous time series data. We use both experimental and simulated datasets to compare the performance (in percentage error) of our proposed method with three currently used methods: skipping the missing values, linear interpolation, and bootstrapping. Unlike the methods that involve modifying the input data, our method modifies the calculation process. This keeps the data unchanged which is less intrusive to the structure of the data. The results demonstrate that our method has a consistent lower average percentage error than other three commonly used methods in multiple common physiological signals. For missing values in common physiological signal type, different data size and generating mechanism, our method can more accurately extract the information contained in continuously monitored data than traditional methods. So it may serve as an effective tool for handling missing values and may have broad utility in analyzing sample entropy for common physiological signals. This could help develop new tools for disease diagnosis and evaluation of treatment effects.
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Affiliation(s)
- Xinzheng Dong
- School of Software Engineering, South China University of Technology, Guangzhou 510006, China;
- Zhuhai Laboratory of Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Zhuhai College of Jilin University, Zhuhai 519041, China
| | - Chang Chen
- Faculty of Health Sciences, University of Macau, Taipa, Macau 999078, China; (C.C.); (Y.J.)
| | - Qingshan Geng
- Guangdong General Hospital, Guangdong Academy of Medical Science, Guangzhou 510080, China;
| | - Zhixin Cao
- Beijing Engineering Research Center of Diagnosis and Treatment of Respiratory and Critical Care Medicine, Beijing Chaoyang Hospital, Beijing 100043, China; (Z.C.); (Y.S.)
| | - Xiaoyan Chen
- Department of Endocrinology, First Affiliated Hospital of Guangzhou Medical University, Guangzhou 510120, China; (X.C.); (J.L.)
| | - Jinxiang Lin
- Department of Endocrinology, First Affiliated Hospital of Guangzhou Medical University, Guangzhou 510120, China; (X.C.); (J.L.)
| | - Yu Jin
- Faculty of Health Sciences, University of Macau, Taipa, Macau 999078, China; (C.C.); (Y.J.)
| | - Zhaozhi Zhang
- School of Law, Washington University, St. Louis, MO 63130, USA;
| | - Yan Shi
- Beijing Engineering Research Center of Diagnosis and Treatment of Respiratory and Critical Care Medicine, Beijing Chaoyang Hospital, Beijing 100043, China; (Z.C.); (Y.S.)
- Department of Mechanical and Electronic Engineering, Beihang University, Beijing 100191, China
| | - Xiaohua Douglas Zhang
- Faculty of Health Sciences, University of Macau, Taipa, Macau 999078, China; (C.C.); (Y.J.)
- Correspondence: ; Tel: +853-8822-4813
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7
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Decreased complexity of glucose dynamics in diabetes in rhesus monkeys. Sci Rep 2019; 9:1438. [PMID: 30723274 PMCID: PMC6363759 DOI: 10.1038/s41598-018-36776-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2018] [Accepted: 11/26/2018] [Indexed: 11/08/2022] Open
Abstract
Until recently, preclinical and clinical work on diabetes has focused on the understanding of blood glucose elevation and its detrimental metabolic sequelae. The advent of continuous glucose monitoring (CGM) technology now allows real time monitoring of blood glucose levels as a time series, and thus the exploration of glucose dynamics at short time scales. Previous work has shown decreases in the complexity of glucose dynamics, as measured by multiscale entropy (MSE) analysis, in diabetes in humans, mice, and rats. Analyses for non-human primates (NHP) have not been reported, nor is it known if anti-diabetes compounds affect complexity of glucose dynamics. We instrumented four healthy and six diabetic rhesus monkeys with CGM probes in the carotid artery and collected glucose values at a frequency of one data point per second for the duration of the sensors' life span. Sensors lasted between 45 and 78 days. Five of the diabetic rhesus monkeys were also administered the anti-diabetic drug liraglutide daily beginning at day 39 of the CGM monitoring period. Glucose levels fluctuated during the day in both healthy and diabetic rhesus monkeys, peaking between 12 noon - 6 pm. MSE analysis showed reduced complexity of glucose dynamics in diabetic monkeys compared to healthy animals. Although liraglutide decreased glucose levels, it did not restore complexity in diabetic monkeys consistently. Complexity varied by time of day, more strongly for healthy animals than for diabetic animals. And by dividing the monitoring period into 3-day or 1-week subperiods, we were able to estimate within-animal variability of MSE curves. Our data reveal that decreased complexity of glucose dynamics is a conserved feature of diabetes from rodents to NHPs to man.
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Huising MO, van der Meulen T, Huang JL, Pourhosseinzadeh MS, Noguchi GM. The Difference δ-Cells Make in Glucose Control. Physiology (Bethesda) 2018; 33:403-411. [PMID: 30303773 PMCID: PMC6347098 DOI: 10.1152/physiol.00029.2018] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2018] [Revised: 08/13/2018] [Accepted: 08/13/2018] [Indexed: 12/17/2022] Open
Abstract
The role of beta and α-cells to glucose control are established, but the physiological role of δ-cells is poorly understood. Delta-cells are ideally positioned within pancreatic islets to modulate insulin and glucagon secretion at their source. We review the evidence for a negative feedback loop between delta and β-cells that determines the blood glucose set point and suggest that local δ-cell-mediated feedback stabilizes glycemic control.
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Affiliation(s)
- Mark O Huising
- Department of Neurobiology, Physiology & Behavior, College of Biological Sciences, University of California , Davis, California
- Department of Physiology and Membrane Biology, School of Medicine, University of California , Davis, California
| | - Talitha van der Meulen
- Department of Neurobiology, Physiology & Behavior, College of Biological Sciences, University of California , Davis, California
| | - Jessica L Huang
- Department of Neurobiology, Physiology & Behavior, College of Biological Sciences, University of California , Davis, California
| | - Mohammad S Pourhosseinzadeh
- Department of Neurobiology, Physiology & Behavior, College of Biological Sciences, University of California , Davis, California
| | - Glyn M Noguchi
- Department of Neurobiology, Physiology & Behavior, College of Biological Sciences, University of California , Davis, California
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Costa MD, Redline S, Davis RB, Heckbert SR, Soliman EZ, Goldberger AL. Heart Rate Fragmentation as a Novel Biomarker of Adverse Cardiovascular Events: The Multi-Ethnic Study of Atherosclerosis. Front Physiol 2018; 9:1117. [PMID: 30233384 PMCID: PMC6129761 DOI: 10.3389/fphys.2018.01117] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2018] [Accepted: 07/25/2018] [Indexed: 02/04/2023] Open
Abstract
Background: A major objective of precision medicine is the elucidation of non-invasive biomarkers of cardiovascular (CV) risk. Recently, we introduced a new dynamical marker of sino-atrial instability, termed heart rate fragmentation (HRF), which outperformed traditional and nonlinear heart rate variability metrics in separating ostensibly healthy subjects from patients with coronary artery disease. Accordingly, we hypothesized that HRF may be a dynamical biomarker of adverse cardiovascular events (CVEs). Methods: This study employed data from a cohort of participants in the Multi-Ethnic Study of Atherosclerosis (MESA), a prospective study of sub-clinical heart disease. Interbeat interval time series (n = 1963), derived from the electrocardiographic channel of the polysomnogram study, were analyzed using the newly introduced metrics of fragmentation, as well as traditional heart rate variability (HRV) indices and the short-term detrended fluctuation analysis exponent. Cox regression analysis was used to assess the association between HR dynamic indices and CV outcomes in unadjusted and adjusted models. Results: The mean (± SD) follow-up time was 2.97 ± 0.63 years. In adjusted models, higher fragmentation was significantly associated with incident CVEs (number of events; hazard ratio [95% confidence interval]: n = 72, 1.43 [1.16-1.76]) and CV death (n = 21; 1.65 [1.15-2.36]). The traditional HRV and the fractal indices were not associated with CVEs or CV death. The most discriminatory fragmentation indices added significant value to Framingham and MESA CV risk indices in all analyses. Conclusion: Our findings show that HRF has promise as a non-invasive, automatable biomarker of CV risk. The basic mechanisms underlying fragmentation remain to be delineated. Its association with incident outcomes raises the possibility of connections to degenerative changes in the multisystem network controlling SAN function.
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Affiliation(s)
- Madalena D. Costa
- Margret and H. A. Rey Institute for Nonlinear Dynamics in Medicine, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
| | - Susan Redline
- Division of Sleep and Circadian Disorders, Departments of Medicine and Neurology, Brigham and Women's Hospital, Boston, MA, United States
- Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
| | - Roger B. Davis
- Division of General Medicine and Primary Care, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
| | - Susan R. Heckbert
- Department of Epidemiology, University of Washington, Seattle, WA, United States
| | - Elsayed Z. Soliman
- Department of Epidemiology and Prevention, Epidemiological Cardiology Research Center, Winston-Salem, NC, United States
- Section on Cardiology, Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC, United States
| | - Ary L. Goldberger
- Margret and H. A. Rey Institute for Nonlinear Dynamics in Medicine, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
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Xue M, Wang D, Zhang Z, Cao Z, Luo Z, Zheng Y, Lu J, Zhao Q, Zhang XD. Demonstrating the Potential of Using Transcutaneous Oxygen and Carbon Dioxide Tensions to Assess the Risk of Pressure Injuries. Int J Biol Sci 2018; 14:1466-1471. [PMID: 30262998 PMCID: PMC6158733 DOI: 10.7150/ijbs.26987] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2018] [Accepted: 06/22/2018] [Indexed: 02/04/2023] Open
Abstract
Pressure injuries have a high incidence in elderly and critically ill patients, and can endanger lives in severe cases. The key to reducing the incidence of pressure injuries is to find an objective, noninvasive, automatic and consistent scientific method for assessing pressure injuries. To serve this need, we conducted a clinical study to investigate the potential of using transcutaneous oxygen tension (TcPO2) and transcutaneous carbon dioxide tension (TcPCO2) for assessing pressure injuries. From the results of the study we found that first, the values of TcPO2 and TcPCO2 are sensitive to the change of pressure imposed on the measured region and to the risk status of a pressure injury when a pressure is imposed. Second, the magnitude of change in TcPO2 and TcPCO2 is higher in patients with a high risk of a pressure injury compared with those who have a low risk. Third, TcPO2 and TcPCO2 are both significantly correlated with the Braden score, the widely used score for assessing the risk of a pressure injury. Therefore, TcPO2 and TcPCO2 have a potential to be an effective and convenient scientific tool for assessing the risk of pressure injuries.
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Affiliation(s)
- Mei Xue
- Department of Respiratory and Critical Care Medicine, Beijing Chao-Yang Hospital, Capital Medical University; Beijing Institute of Respiratory Medicine, Beijing Engineering Research Center of Respiratory and Critical Care Medicine, Engineering Research Center of Diagnosis and Treatment of Respiratory and Critical Care Medicine, Beijing Chaoyang Hospital, Beijing 100043, China
| | - Dandan Wang
- Faculty of Health Sciences, University of Macau, Taipa 999078, Macau
| | - Zhaozhi Zhang
- Department of Statistical Science, Duke University, Durham, NC 27708, USA
| | - Zhixin Cao
- Department of Respiratory and Critical Care Medicine, Beijing Chao-Yang Hospital, Capital Medical University; Beijing Institute of Respiratory Medicine, Beijing Engineering Research Center of Respiratory and Critical Care Medicine, Engineering Research Center of Diagnosis and Treatment of Respiratory and Critical Care Medicine, Beijing Chaoyang Hospital, Beijing 100043, China
| | - Zujin Luo
- Department of Respiratory and Critical Care Medicine, Beijing Chao-Yang Hospital, Capital Medical University; Beijing Institute of Respiratory Medicine, Beijing Engineering Research Center of Respiratory and Critical Care Medicine, Engineering Research Center of Diagnosis and Treatment of Respiratory and Critical Care Medicine, Beijing Chaoyang Hospital, Beijing 100043, China
| | - Yingying Zheng
- Department of Respiratory and Critical Care Medicine, Beijing Chao-Yang Hospital, Capital Medical University; Beijing Institute of Respiratory Medicine, Beijing Engineering Research Center of Respiratory and Critical Care Medicine, Engineering Research Center of Diagnosis and Treatment of Respiratory and Critical Care Medicine, Beijing Chaoyang Hospital, Beijing 100043, China
| | - Jingjing Lu
- Department of Respiratory and Critical Care Medicine, Beijing Chao-Yang Hospital, Capital Medical University; Beijing Institute of Respiratory Medicine, Beijing Engineering Research Center of Respiratory and Critical Care Medicine, Engineering Research Center of Diagnosis and Treatment of Respiratory and Critical Care Medicine, Beijing Chaoyang Hospital, Beijing 100043, China
| | - Qi Zhao
- Faculty of Health Sciences, University of Macau, Taipa 999078, Macau
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Abstract
Glycemic variability (GV) is a major consideration when evaluating quality of glycemic control. GV increases progressively from prediabetes through advanced T2D and is still higher in T1D. GV is correlated with risk of hypoglycemia. The most popular metrics for GV are the %Coefficient of Variation (%CV) and standard deviation (SD). The %CV is correlated with risk of hypoglycemia. Graphical display of glucose by date, time of day, and day of the week, and display of simplified glucose distributions showing % of time in several ranges, provide clinically useful indicators of GV. SD is highly correlated with most other measures of GV, including interquartile range, mean amplitude of glycemic excursion, mean of daily differences, and average daily risk range. Some metrics are sensitive to the frequency, periodicity, and complexity of glycemic fluctuations, including Fourier analysis, periodograms, frequency spectrum, multiscale entropy (MSE), and Glucose Variability Percentage (GVP). Fourier analysis indicates progressive changes from normal subjects to children and adults with T1D, and from prediabetes to T2D. The GVP identifies novel characteristics for children, adolescents, and adults with type 1 diabetes and for adults with type 2. GVP also demonstrated small rapid glycemic fluctuations in people with T1D when using a dual-hormone closed-loop control. MSE demonstrated systematic changes from normal subjects to people with T2D at various stages of duration, intensity of therapy, and quality of glycemic control. We describe new metrics to characterize postprandial excursions, day-to-day stability of glucose patterns, and systematic changes of patterns by day of the week. Metrics for GV should be interpreted in terms of percentiles and z-scores relative to identified reference populations. There is a need for large accessible databases for reference populations to provide a basis for automated interpretation of GV and other features of continuous glucose monitoring records.
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Affiliation(s)
- David Rodbard
- Biomedical Informatics Consultants LLC , Potomac, Maryland
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Niu J, Shi Y, Cai M, Cao Z, Wang D, Zhang Z, Zhang XD. Detection of sputum by interpreting the time-frequency distribution of respiratory sound signal using image processing techniques. Bioinformatics 2018; 34:820-827. [PMID: 29040453 PMCID: PMC6192228 DOI: 10.1093/bioinformatics/btx652] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2017] [Revised: 07/25/2017] [Accepted: 10/12/2017] [Indexed: 11/14/2022] Open
Abstract
Motivation Sputum in the trachea is hard to expectorate and detect directly for the patients who are unconscious, especially those in Intensive Care Unit. Medical staff should always check the condition of sputum in the trachea. This is time-consuming and the necessary skills are difficult to acquire. Currently, there are few automatic approaches to serve as alternatives to this manual approach. Results We develop an automatic approach to diagnose the condition of the sputum. Our approach utilizes a system involving a medical device and quantitative analytic methods. In this approach, the time-frequency distribution of respiratory sound signals, determined from the spectrum, is treated as an image. The sputum detection is performed by interpreting the patterns in the image through the procedure of preprocessing and feature extraction. In this study, 272 respiratory sound samples (145 sputum sound and 127 non-sputum sound samples) are collected from 12 patients. We apply the method of leave-one out cross-validation to the 12 patients to assess the performance of our approach. That is, out of the 12 patients, 11 are randomly selected and their sound samples are used to predict the sound samples in the remaining one patient. The results show that our automatic approach can classify the sputum condition at an accuracy rate of 83.5%. Availability and implementation The matlab codes and examples of datasets explored in this work are available at Bioinformatics online. Contact yesoyou@gmail.com or douglaszhang@umac.mo. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jinglong Niu
- School of Automation Science and Electrical Engineering, Beihang
University, Beijing, China
- Beijing Engineering Research Center of Diagnosis and Treatment of
Respiratory and Critical Care Medicine, Beijing Chaoyang Hospital, Beijing, China
| | - Yan Shi
- School of Automation Science and Electrical Engineering, Beihang
University, Beijing, China
- Beijing Engineering Research Center of Diagnosis and Treatment of
Respiratory and Critical Care Medicine, Beijing Chaoyang Hospital, Beijing, China
- Faculty of Health Sciences, University of Macau, Taipa, Macau,
China
- The State Key Laboratory of Fluid Power Transmission and Control,
Zhejiang University, Hangzhou, China
| | - Maolin Cai
- School of Automation Science and Electrical Engineering, Beihang
University, Beijing, China
- Beijing Engineering Research Center of Diagnosis and Treatment of
Respiratory and Critical Care Medicine, Beijing Chaoyang Hospital, Beijing, China
| | - Zhixin Cao
- Beijing Engineering Research Center of Diagnosis and Treatment of
Respiratory and Critical Care Medicine, Beijing Chaoyang Hospital, Beijing, China
| | - Dandan Wang
- Faculty of Health Sciences, University of Macau, Taipa, Macau,
China
| | - Zhaozhi Zhang
- Department of Statistical Science, Duke University, Durham, NC,
USA
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