Pub Date : 2020-02-07DOI: 10.5772/intechopen.89827
Giovanni Minarini
The autonomic nervous system has a huge impact on the cardiac regulatory mechanism, and many markers exist for evaluating it. In this chapter we are going to focus on the RMSSD (Root mean square of successive differences), considered the most precise marker for the parasympathetic effector on the heart. Before is necessary to learn what the Heart Rate Variability is and how it works, which type of range of HRV exists and how we can measure it. Finally, there will be a presenta-tion of how the RMSSD can be used in different field, and how and why the outcome can change and what does it mean.
{"title":"Root Mean Square of the Successive Differences as Marker of the Parasympathetic System and Difference in the Outcome after ANS Stimulation","authors":"Giovanni Minarini","doi":"10.5772/intechopen.89827","DOIUrl":"https://doi.org/10.5772/intechopen.89827","url":null,"abstract":"The autonomic nervous system has a huge impact on the cardiac regulatory mechanism, and many markers exist for evaluating it. In this chapter we are going to focus on the RMSSD (Root mean square of successive differences), considered the most precise marker for the parasympathetic effector on the heart. Before is necessary to learn what the Heart Rate Variability is and how it works, which type of range of HRV exists and how we can measure it. Finally, there will be a presenta-tion of how the RMSSD can be used in different field, and how and why the outcome can change and what does it mean.","PeriodicalId":382562,"journal":{"name":"Autonomic Nervous System Monitoring - Heart Rate Variability","volume":"206 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132403922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-29DOI: 10.5772/intechopen.89901
N. Aimie-Salleh, Nurul Aliaa Abdul Ghani, Nurhafiezah Hasanudin, Siti Nur Shakiroh Shafie
Heart rate variability (HRV) is a physiological measurement that can help to monitor and diagnose chronic diseases such as cardiovascular disease, depression, and psychological stress. HRV measurement is commonly extracted from the electrocardiography (ECG). However, ECG has bulky wires where it needs at least three surface electrodes to be placed on the skin. This may cause distraction during the recording and need longer time to setup. Therefore, photoplethysmography (PPG), a simple optical technique, was suggested to obtain heart rate. This study proposes to investigate the effectiveness of PPG recording and derivation of HRV for feature analysis. The PPG signal was preprocessed to remove all the noise and to extract the HRV. HRV features were collected using time-domain analysis (TA), frequency-domain analysis (FA) and nonlinear time-frequency analysis (TFA). Five out of 22 HRV features, which are HR, RMSSD, LF/HF, LFnu, and HFnu, showed high correlation (rho > 0.6 and prho < 0.05) in comparison to standard 5-min excerpt while producing significant difference (p-value < 0.05) during the stressing condition across all interval HRV excerpts. This simple yet accurate PPG recording system perhaps might useful to assess the HRV signal in a short time, and further can be used for the ANS assessment.
{"title":"Heart Rate Variability Recording System Using Photoplethysmography Sensor","authors":"N. Aimie-Salleh, Nurul Aliaa Abdul Ghani, Nurhafiezah Hasanudin, Siti Nur Shakiroh Shafie","doi":"10.5772/intechopen.89901","DOIUrl":"https://doi.org/10.5772/intechopen.89901","url":null,"abstract":"Heart rate variability (HRV) is a physiological measurement that can help to monitor and diagnose chronic diseases such as cardiovascular disease, depression, and psychological stress. HRV measurement is commonly extracted from the electrocardiography (ECG). However, ECG has bulky wires where it needs at least three surface electrodes to be placed on the skin. This may cause distraction during the recording and need longer time to setup. Therefore, photoplethysmography (PPG), a simple optical technique, was suggested to obtain heart rate. This study proposes to investigate the effectiveness of PPG recording and derivation of HRV for feature analysis. The PPG signal was preprocessed to remove all the noise and to extract the HRV. HRV features were collected using time-domain analysis (TA), frequency-domain analysis (FA) and nonlinear time-frequency analysis (TFA). Five out of 22 HRV features, which are HR, RMSSD, LF/HF, LFnu, and HFnu, showed high correlation (rho > 0.6 and prho < 0.05) in comparison to standard 5-min excerpt while producing significant difference (p-value < 0.05) during the stressing condition across all interval HRV excerpts. This simple yet accurate PPG recording system perhaps might useful to assess the HRV signal in a short time, and further can be used for the ANS assessment.","PeriodicalId":382562,"journal":{"name":"Autonomic Nervous System Monitoring - Heart Rate Variability","volume":"249 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128467692","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-24DOI: 10.5772/intechopen.89042
Robert L. Drury
Heart rate variability (HRV) is increasingly recognized as a central variable of interest in health maintenance, disease prevention and performance optimization. It is also a sensitive biomarker of health status, disease presence and functional abilities, acquiring and processing high fidelity inter beat interval data, along with other psychophysiological parameters that can assist in clinical assessment and intervention, population health studies/digital epidemiology and positive performance optimization. We describe a system using high-throughput artificial intelligence based on the KUBIOS platform to combine time, frequency and nonlinear data domains acquired by wearable or implanted biosensors to guide in clinical assessment, decision support and intervention, population health monitoring and individual self-regulation and performance enhancement, including the use of HRV biofeedback. This approach follows the iP4 health model which emphasizes an integral, personalized, predictive, preventive and participatory approach to human health and well-being. It therefore includes psychological, biological, genomic, sociocultural, evolutionary and spiritual variables as mutually interactive elements in embodying complex systems adaptation.
{"title":"HRV in an Integrated Hardware/Software System Using Artificial Intelligence to Provide Assessment, Intervention and Performance Optimization","authors":"Robert L. Drury","doi":"10.5772/intechopen.89042","DOIUrl":"https://doi.org/10.5772/intechopen.89042","url":null,"abstract":"Heart rate variability (HRV) is increasingly recognized as a central variable of interest in health maintenance, disease prevention and performance optimization. It is also a sensitive biomarker of health status, disease presence and functional abilities, acquiring and processing high fidelity inter beat interval data, along with other psychophysiological parameters that can assist in clinical assessment and intervention, population health studies/digital epidemiology and positive performance optimization. We describe a system using high-throughput artificial intelligence based on the KUBIOS platform to combine time, frequency and nonlinear data domains acquired by wearable or implanted biosensors to guide in clinical assessment, decision support and intervention, population health monitoring and individual self-regulation and performance enhancement, including the use of HRV biofeedback. This approach follows the iP4 health model which emphasizes an integral, personalized, predictive, preventive and participatory approach to human health and well-being. It therefore includes psychological, biological, genomic, sociocultural, evolutionary and spiritual variables as mutually interactive elements in embodying complex systems adaptation.","PeriodicalId":382562,"journal":{"name":"Autonomic Nervous System Monitoring - Heart Rate Variability","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125147529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-15DOI: 10.5772/intechopen.89593
Dr. Yousif Mohamed Yousif Abdallah, N. Abuhadi
Medical imaging of the nervous system is the methodology used to achieve pictures of parts of the nervous system for therapeutic uses to recognize the ail-ments. Magnetic resonance imaging (MRI) is a kind of medical imaging tool that utilizes solid magnetic fields and radio waves to deliver point-by-point pictures of the inside of the body. There are large number of imaging methodologies done each week around the world. Medical imaging is developing rapidly due to developments in image acquisition tools including functional MRI and hybrid imaging modalities. This chapter abridged the role of magnetic resonance imaging (MRI) in autonomic nervous system monitoring. This chapter also summarizes the image interpretation challenges in diagnosing autonomic nervous system disorders.
{"title":"The Role of Magnetic Resonance Imaging (MRI) in Autonomic Nervous System Monitoring","authors":"Dr. Yousif Mohamed Yousif Abdallah, N. Abuhadi","doi":"10.5772/intechopen.89593","DOIUrl":"https://doi.org/10.5772/intechopen.89593","url":null,"abstract":"Medical imaging of the nervous system is the methodology used to achieve pictures of parts of the nervous system for therapeutic uses to recognize the ail-ments. Magnetic resonance imaging (MRI) is a kind of medical imaging tool that utilizes solid magnetic fields and radio waves to deliver point-by-point pictures of the inside of the body. There are large number of imaging methodologies done each week around the world. Medical imaging is developing rapidly due to developments in image acquisition tools including functional MRI and hybrid imaging modalities. This chapter abridged the role of magnetic resonance imaging (MRI) in autonomic nervous system monitoring. This chapter also summarizes the image interpretation challenges in diagnosing autonomic nervous system disorders.","PeriodicalId":382562,"journal":{"name":"Autonomic Nervous System Monitoring - Heart Rate Variability","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122854150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-10-04DOI: 10.5772/intechopen.89456
M. Godoy, M. Gregório
Based on the largest data set ever available for analysis of heart rate variability (HRV) variables, in healthy individuals, it was possible to determine the evolutionary behavior of three representative components of parasympathetic nervous system function (RMSSD, PNN50, and HF ms 2 ), in different age groups of the life cycle: newborns, children and adolescents, young adults, and, finally, middle-aged adults. A near-parabolic and nonsynchronous behavior was observed among the different variables evaluated, with low values at first, then progressive elevation, and later fall, approximating the values of the newborns to the values of middle-aged adults and suggesting that the autonomic nervous system, at least relatively to its parasympathetic component, undergoes a growing maturation that is completed in the young adult and later suffers a progressive degeneration, completing the life cycle. This fact should be considered when comparing the analysis between healthy individuals and those with different states of pathological impairment.
基于迄今为止可用于分析心率变异性(HRV)变量的最大数据集，在健康个体中，有可能确定副交感神经系统功能的三个代表性组成部分(RMSSD, PNN50和HF ms 2)在生命周期的不同年龄组中的进化行为:新生儿，儿童和青少年，年轻人，最后是中年人。在评估的不同变量中观察到一个接近抛物线和非同步的行为，首先是低值，然后逐渐升高，然后下降，接近新生儿的值与中年人的值，这表明自主神经系统，至少相对于其副交感神经成分，经历了一个逐渐成熟的过程，这个过程在年轻人中完成，后来经历了一个渐进的变性，完成了生命周期。在比较健康个体和具有不同病理损害状态的个体之间的分析时，应考虑到这一事实。
{"title":"Evolution of Parasympathetic Modulation throughout the Life Cycle","authors":"M. Godoy, M. Gregório","doi":"10.5772/intechopen.89456","DOIUrl":"https://doi.org/10.5772/intechopen.89456","url":null,"abstract":"Based on the largest data set ever available for analysis of heart rate variability (HRV) variables, in healthy individuals, it was possible to determine the evolutionary behavior of three representative components of parasympathetic nervous system function (RMSSD, PNN50, and HF ms 2 ), in different age groups of the life cycle: newborns, children and adolescents, young adults, and, finally, middle-aged adults. A near-parabolic and nonsynchronous behavior was observed among the different variables evaluated, with low values at first, then progressive elevation, and later fall, approximating the values of the newborns to the values of middle-aged adults and suggesting that the autonomic nervous system, at least relatively to its parasympathetic component, undergoes a growing maturation that is completed in the young adult and later suffers a progressive degeneration, completing the life cycle. This fact should be considered when comparing the analysis between healthy individuals and those with different states of pathological impairment.","PeriodicalId":382562,"journal":{"name":"Autonomic Nervous System Monitoring - Heart Rate Variability","volume":"371 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120870086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-03DOI: 10.5772/intechopen.88766
Alondra Albarado-Ibañez, R. E. Arroyo-Carmona, D. Bernabé-Sánchez, Marissa Limón-Cantú, Benjamín López-Silva, Martha Lucía Ita-Amador, J. Torres-Jácome
Lifestyle emerging diseases like obesity, metabolic syndrome (MeS), and diabetes mellitus are considered high-risk factors for lethal arrhythmias and side effects. A Poincaré plot is constructed with the time series of RR and PP electrocardiogram (ECG) intervals, using two stages: the new phase and the old phase. We proposed this diagram of two dimensions, a way to quantify and observe the regularity of events in space and time. Therefore, the heart rate variability (HRV) can be used as a biomarker for early prognostic and diagnostic of several metabolic diseases; additionally, this biomarker is obtained by a noninvasive tool like the electrocardiogram.
{"title":"Heart Rate Variability as Biomarker for Prognostic of Metabolic Disease","authors":"Alondra Albarado-Ibañez, R. E. Arroyo-Carmona, D. Bernabé-Sánchez, Marissa Limón-Cantú, Benjamín López-Silva, Martha Lucía Ita-Amador, J. Torres-Jácome","doi":"10.5772/intechopen.88766","DOIUrl":"https://doi.org/10.5772/intechopen.88766","url":null,"abstract":"Lifestyle emerging diseases like obesity, metabolic syndrome (MeS), and diabetes mellitus are considered high-risk factors for lethal arrhythmias and side effects. A Poincaré plot is constructed with the time series of RR and PP electrocardiogram (ECG) intervals, using two stages: the new phase and the old phase. We proposed this diagram of two dimensions, a way to quantify and observe the regularity of events in space and time. Therefore, the heart rate variability (HRV) can be used as a biomarker for early prognostic and diagnostic of several metabolic diseases; additionally, this biomarker is obtained by a noninvasive tool like the electrocardiogram.","PeriodicalId":382562,"journal":{"name":"Autonomic Nervous System Monitoring - Heart Rate Variability","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130906146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-11-05DOI: 10.5772/INTECHOPEN.81238
Adam W. Potter, David P. Looney, Xiaojiang Xu, W. Santee, S. Srinivasan
The ability to model and simulate the rise and fall of core body temperature is of significant interest to a broad spectrum of organizations. These organiza-tions include the military, as well as both public and private health and medical groups. To effectively use cold models, it is useful to understand the first principles of heat transfer within a given environment as well as have an understanding of the underlying physiology, including the thermoregulatory responses to various conditions and activities. The combination of both rational or first principles and empirical approaches to modeling allow for the development of practical models that can predict and simulate core body temperature changes for a given individual and ultimately provide protection from injury or death. The ability to predict these maximal potentials within complex and extreme environments is difficult. The present work outlines biomedical modeling techniques to simulate and predict cold-related injuries, and discusses current and legacy models and methods.
{"title":"Modeling Thermoregulatory Responses to Cold Environments","authors":"Adam W. Potter, David P. Looney, Xiaojiang Xu, W. Santee, S. Srinivasan","doi":"10.5772/INTECHOPEN.81238","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.81238","url":null,"abstract":"The ability to model and simulate the rise and fall of core body temperature is of significant interest to a broad spectrum of organizations. These organiza-tions include the military, as well as both public and private health and medical groups. To effectively use cold models, it is useful to understand the first principles of heat transfer within a given environment as well as have an understanding of the underlying physiology, including the thermoregulatory responses to various conditions and activities. The combination of both rational or first principles and empirical approaches to modeling allow for the development of practical models that can predict and simulate core body temperature changes for a given individual and ultimately provide protection from injury or death. The ability to predict these maximal potentials within complex and extreme environments is difficult. The present work outlines biomedical modeling techniques to simulate and predict cold-related injuries, and discusses current and legacy models and methods.","PeriodicalId":382562,"journal":{"name":"Autonomic Nervous System Monitoring - Heart Rate Variability","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132809895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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