Autonomic nervous system (ANS) plays an important role in the regulation of the physiological processes of the human organism during normal and pathological conditions. Among the techniques used in its evaluation, the heart rate variability (HRV) has arising as a simple and non-invasive measure of the autonomic impulses, representing one of the most promising quantitative markers of the autonomic balance. The HRV describes the oscillations in the interval between consecutive heart beats (RR interval), as well as the oscillations between consecutive instantaneous heart rates. It is a measure that can be used to assess the ANS modulation under physiological conditions, such as wakefulness and sleep conditions, different body positions, physical training and also pathological conditions. Changes in the HRV patterns provide a sensible and advanced indicator of health involvements. Higher HRV is a signal of good adaptation and characterizes a health person with efficient autonomic mechanisms, while lower HRV is frequently an indicator of abnormal and insufficient adaptation of the autonomic nervous system, provoking poor patient's physiological function. Because of its importance as a marker that reflects the ANS activity on the sinus node and as a clinical instrument to assess and identify health involvements, this study reviews conceptual aspects of the HRV, measurement devices, filtering methods, indexes used in the HRV analyses, limitations in the use and clinical applications of the HRV.

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Basic notions of heart rate variability and its clinical applicability

Complex networks are an important paradigm of modern complex systems sciences which allows quantitatively assessing the structural properties of systems composed of different interacting entities. During the last years, intensive efforts have been spent on applying network-based concepts also for the analysis of dynamically relevant higher-order statistical properties of time series. Notably, many corresponding approaches are closely related with the concept of recurrence in phase space. In this paper, we review recent methodological advances in time series analysis based on complex networks, with a special emphasis on methods founded on recurrence plots. The potentials and limitations of the individual methods are discussed and illustrated for paradigmatic examples of dynamical systems as well as for real-world time series. Complex network measures are shown to provide information about structural features of dynamical systems that are complementary to those characterized by other methods of time series analysis and, hence, substantially enrich the knowledge gathered from other existing (linear as well as nonlinear) approaches.

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Recurrence-based time series analysis by means of complex network methods

Complex networks are an important paradigm of modern complex systems sciences which allows quantitatively assessing the structural properties of systems composed of different interacting entities. During the last years, intensive efforts have been spent on applying network-based concepts also for the analysis of dynamically relevant higher-order statistical properties of time series. Notably, many corresponding approaches are closely related to the concept of recurrence in phase space. In this paper, we review recent methodological advances in time series analysis based on complex networks, with a special emphasis on methods founded on recurrence plots. The potentials and limitations of the individual methods are discussed and illustrated for paradigmatic examples of dynamical systems as well as for real-world time series. Complex network measures are shown to provide information about structural features of dynamical systems that are complementary to those characterized by other methods of time series an...

/pdf/recurrence-based-time-series-analysis-by-means-of-complex-3lp274r1dh.pdf

With the upcoming reform of the healthcare system and the greater emphasis on care in the home and other living environments, geriatric providers will need alternate ways of monitoring disease, activity, response to therapy, and patient safety. Current understanding of the dynamic nature of chronic illnesses, their effects on health over time, and the ability to manage them in the community are limited to measuring a set of variables at discrete points in time, which does not account for the dynamic interactions between physiological systems and the environments of daily life. Recent developments of sensors, data recorders, and communication networks allow the unprecedented measurements of physiological and sociological data for use in geriatrics care. This article identifies and discusses the important issues regarding the use of monitoring technologies in elderly patients. The goals are fourfold. First, some emerging technology that may improve the lives of older adults and improve care are highlighted. Second, the possible applications of technology in geriatrics settings are discussed, with a focus on acute falls, dementia, and cardiac conditions. Third, real and perceived concerns in using monitoring technology are identified and addressed, including technology adoption by elderly people; stigma; and the reduction in social contact; ethical concerns of privacy, autonomy, and consent; concerns of clinicians, including information overload, licensure, and liability; current reimbursement schemes for using technology; and the reliability and infrastructure needed for monitoring technology. Fourth, future approaches to make monitoring technology useful and available in geriatrics are recommended.

In Situ Monitoring of Health in Older Adults: Technologies and Issues

Heart rate variability (HRV) is the temporal variation between sequences of consecutive heartbeats. On a standard electrocardiogram (ECG), the maximum upwards deflection of a normal QRS complex is at the peak of the R wave (Figure 1), and the duration between two adjacent R wave peaks is termed the R-R interval. The ECG signal requires editing before HRV analysis can be performed, a process requiring the removal of all non-sinus-node-originating beats. The resulting period between adjacent QRS complexes resulting from sinus node depolarizations is termed the N-N (normal-normal) interval.1 HRV is the measurement of the variability of the N-N intervals. Figure 1. 
The normal electrocardiogram with component waves labelled.



Although counter-intuitive, it is possible that HRV confers a survival advantage. Any system exhibiting intrinsic variability is primed to respond rapidly and appropriately to demands placed upon it.

HRV is a measure of the balance between sympathetic mediators of heart rate (HR) (i.e. the effect of epinephrine and norepinephrine, released from sympathetic nerve fibres, acting on the sino-atrial and atrio-ventricular nodes), which increase the rate of cardiac contraction and facilitate conduction at the atrio-ventricular node, and parasympathetic mediators of HR (i.e. the influence of acetylcholine, released by the parasympathetic nerve fibres, acting on the sino-atrial and atrio-ventricular nodes), leading to a decrease in the HR and a slowing of conduction at the atrio-ventricular node. Sympathetic mediators appear to exert their influence over longer time periods and are reflected in the low frequency power (LFP) of the HRV spectrum (between 0.04 Hz and 0.15 Hz.2,,3 Vagal mediators exert their influence more quickly on the heart, and principally affect the high frequency power (HFP) of the HRV spectrum (between 0.15 Hz and 0.4 Hz).4 Thus, at any point in time, the LFP:HFP ratio is a proxy for the sympatho-vagal balance.

Physiological and pathological process … 

Address correspondence to Dr M.J. Reed, Emergency Department, Royal Infirmary of Edinburgh, 51 Little France Crescent, Edinburgh EH16 4SA. e-mail: mattreed1{at}hotmail.com

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Heart rate variability measurements and the prediction of ventricular arrhythmias.

https://www.pik-potsdam.de/members/kurths/publikationen/2002/h-eart-rate-variability-before-the-onset-of-ventricular-tachycardia-differences-between-slow-and-fast-arrhythmias

Heart rate variability before the onset of ventricular tachycardia: differences between slow and fast arrhythmias.

Detection of patients with coronary artery disease using cardiac magnetic field mapping at rest.

Hypertrophic cardiomyopathy (HCM) is a common primary inherited cardiac muscle disorder, defined clinically by the presence of unexplained left ventricular hypertrophy. The detection of affected patients remains challenging. Genetic testing is limited because only in 50%-60% of all HCM diagnoses an underlying mutation can be found. Furthermore, the disease has a varied clinical course and outcome, with many patients having little or no discernible cardiovascular symptoms, whereas others develop profound exercise limitation and recurrent arrhythmias or sudden cardiac death. Therefore prospective screening of HCM family members is strongly recommended. According to the current guidelines this includes serial echocardiographic and electrocardiographic examinations. In this study we investigated the capability of cardiac magnetic field mapping (CMFM) to detect patients suffering from HCM. We introduce for the first time a combined diagnostic approach based on map topology quantification using Kullback-Leibler (KL) entropy and regional magnetic field strength parameters. The cardiac magnetic field was recorded over the anterior chest wall using a multichannel-LT-SQUID system. CMFM was calculated based on a regular 36 point grid. We analyzed CMFM in patients with confirmed diagnosis of HCM (HCM, n=33, 43.8+/-13 years, 13 women, 20 men), a control group of healthy subjects (NORMAL, n=57, 39.6+/-8.9 years; 22 women and 35 men), and patients with confirmed cardiac hypertrophy due to arterial hypertension (HYP, n=42, 49.7+/-7.9 years, 15 women and 27 men). A subgroup analysis was performed between HCM patients suffering from the obstructive (HOCM, n=19) and nonobstructive (HNCM, n=14) form of the disease. KL entropy based map topology quantification alone identified HCM patients with a sensitivity of 78.8% and specificity of 86.9% (overall classification rate 84.8%). The combination of the KL parameters with a regional field strength parameter improved the overall classification rate to 87.9% (sensitivity: 84.8%, specificity: 88.9%, area under ROC curve: 0.94). KL measures applied to discriminate between HOCM and HNCM patients showed a correct classification of 78.8%. The combination of one KL and one regional parameter again improved the overall classification rate to 97%. A preliminary prospective analysis in two HCM families showed the feasibility of this diagnostic approach with a correct diagnosis of all 22 screened family members (1 HOCM, 4 HNCM, 17 normal). In conclusion, Cardiac Magnetic Field Mapping including KL entropy based topology quantifications is a suitable tool for HCM screening.

Cardiac Magnetic Field Map Topology Quantified by Kullback-Leibler Entropy Identifies Patients with Hypertrophic Cardiomyopathy.

Cardiac magnetic field map topology quantified by Kullback-Leibler entropy identifies patients with hypertrophic cardiomyopathy.

Acid–base disorders seldom kill; however, the mechanisms and associated complications certainly do. We recently encountered a patient with a mysterious lactic acidosis. The patient proved to be a most capable teacher of important lessons.

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Henry Schütt

Papers

Heart rate variability before the onset of ventricular tachycardia: differences between slow and fast arrhythmias.

Detection of patients with coronary artery disease using cardiac magnetic field mapping at rest.

Cardiac Magnetic Field Map Topology Quantified by Kullback-Leibler Entropy Identifies Patients with Hypertrophic Cardiomyopathy.

Cardiac magnetic field map topology quantified by Kullback-Leibler entropy identifies patients with hypertrophic cardiomyopathy.

Lactate in a Laubenpieper