Heart Rate Variability Workshop:

 With Professor Richard Gevirtz

@ ST. JAMES'S Church Piccadilly, London

Papers, Power points and Software to look at before the course.

I will be handing out CDs with the Power Points and papers at the workshop, but can you please download and install the Kubios Software on your laptop before you come, as this will take a bit of time and this will give you a chance to play with it before the course. There is also two example files to play with in Kubios.
I will send you all an invitation to a "Dropbox" folder with all the PowerPoint's and papers.
Dropbox is simple; if you don't already have it, all you do is sign up on their website and download a small programme. Then when you accept an invitation, a folder will appear in your Dropbox folder on the desktop with all the files. It is a great way of sharing files that are two big to send by email.
Also below are some links to some papers that Richard has provided.

Kubios is a great piece of free software from the University of Eastern Finland. Just go to this web page and register and you can download the small program.

Most ECG/EKG and heart rate variability hardware/software should be able to export a session file as a list of the "Inter-bit-intervals" in the ASCII format.

Then, just open the Kubios software and open the ASCII "Text" file in Kubios and it will automatically import it into the software and analyse it. Kubios calculates just about every heart rate variability variable you can imagine. (And for sure some but you can't). Then just pushed save and you get a one-page PDF of all the analysis.

Here's a screenshot of one of the three analysis screens:
This is an example of the pdf report Kubios generates:
Below are two text file that you can open in Kubios to test how it all works. (They are 3 KB each) Just right click on the link and select "Save Target As..." or "Save Link As..." then you can save the text file were you want.

Heart Rate Variability Papers:

Standards of heart rate variability
1996 American Heart Association Inc.; European Society of Cardiology

Heart rate variability
Standards of measurement, physiological interpretation, and clinical use

Task Force of The European Society of Cardiology and The North American Society of Pacing and Electrophysiology
The last two decades have witnessed the recognition of a significant relationship between the autonomic nervous system and cardiovascular mortality, including sudden cardiac death[1-4]. Experimental evidence for an association between a propensity for lethal arrhythmias and signs of either increased sympathetic or reduced vagal activity has encouraged the development of quantitative markers of autonomic activity.
Heart rate variability (HRV) represents one of the most promising such markers. The apparently easy derivation of this measure has popularized its use. As many commercial devices now provide automated measurement of HRV, the cardiologist has been provided with a seemingly simple tool for both research and clinical studies[5]. However, the significance and meaning of the many different measures of HRV are more complex than generally appreciated and there is a potential for incorrect conclusions and for excessive or unfounded extrapolations.

A Quantitative Systematic Review of Normal Values for Short-Term Heart Rate Variability in Healthy Adults

From the *Division of Public Health and Primary Health Care, University of Oxford, Oxford, UK; †Centre for Sports and Exercise Science, University of Essex, Colchester, UK; and ‡Research Centre for Society and Health, Buckinghamshire New University, Chalfont St Giles, UK
Heart rate variability (HRV) is a known risk factor for mortality in both healthy and patient populations. There are currently no normative data for short-term measures of HRV. A thorough review of short-termHRV data published since 1996 was therefore performed. Data from studies published after the 1996 Task Force report (i.e., between January 1997 and September 2008) and reporting short-term measuresof HRV obtained in normally healthy individuals were collated and factors underlying discrepant values were identified. Forty-four studies met the pre-set inclusion criteria involving 21,438 participants. Values for short-term HRV measures from the literature were lower than Task Force norms. A degree of homogeneity for common measures of HRV in healthy adults was shown across studies. A number of studies demonstrate large interindividual variations (up to 260,000%), particularly for spectral measures. A number of methodological discrepancies underlined disparate values. These include a systematic failure within the literature (a) to recognize the importance of RR data recognition/editing procedures and (b) to question disparate HRV values observed in normally healthy individuals. A need for largescale population studies and a review of the Task Force recommendations for short-term HRV that covers the full-age spectrum were identified. Data presented should be used to quantify reference ranges for short-term measures of HRV in healthy adult populations but should be undertaken with reference to methodological factors underlying disparate values. Recommendations for the measurement of HRV require updating to include current technologies. (PACE 2010; 33:1407-1417)

The polyvagal theory: phylogenetic substrates of a social nervous system

Stephen W. Porges
Department of Psychiatry, Uni_ersity of Illinois at Chicago, 1601 W. Taylor Street, Chicago, IL 60612-7327, USA
International Journal of Psychophysiology 42 ˇ2001. 123_146
The evolution of the autonomic nervous system provides an organizing principle to interpret the adaptive significance of physiological responses in promoting social behavior. According to the polyvagal theory, the well-documented phylogenetic shift in neural regulation of the autonomic nervous system passes through three global stages, each with an associated behavioral strategy. The first stage is characterized by a primitive unmyelinated visceral vagus that fosters digestion and responds to threat by depressing metabolic activity. Behaviorally, the first stage is associated with immobilization behaviors. The second stage is characterized by the sympathetic nervous system that is capable of increasing metabolic output and inhibiting the visceral vagus to foster mobilization behaviors necessary for 'fight or flight'. The third stage, unique to mammals, is characterized by a myelinated vagus that can rapidly regulate cardiac output to foster engagement and disengagement with the environment. The mammalian vagus is neuroanatomically linked to the cranial nerves that regulate social engagement via facial expression and vocalization. As the autonomic nervous system changed through the process of evolution, so did the interplay between the autonomic nervous system and the other physiological systems that respond to stress, including the cortex, the hypothalamic_pituitary_adrenal axis, the neuropeptides of oxytocin and vasopressin, and the immune system. From this phylogenetic orientation, the polyvagal theory proposes a biological basis for social behavior and an intervention strategy to enhance positive social behavior. © 2001 Elsevier Science B.V. All rights reserved.

Biofeedback treatment increases heart rate variability in patients with known coronary artery disease

Jessica M. Del Pozo, Richard N. Gevirtz, Bret Scher, and Erminia Guarneri.
American Heart Journal, March 2004
Objectives To determine if cardiorespiratory biofeedback increases heart rate variability (HRV) in patients with documented coronary artery disease (CAD).
Background Diminished HRV has been associated with increased cardiac morbidity and mortality. Evidence suggests that various lifestyle changes and pharmacologic therapies can improve HRV. The objective of this study was to determine if biofeedback increases HRV in patients with CAD.
Methods Patients with established CAD (n _ 63; mean age, 67 years) were randomly assigned to conventional therapy or to 6 biofeedback sessions consisting of abdominal breath training, heart and respiratory physiologic feedback, and daily breathing practice. HRV was measured by the standard deviation of normal-to-normal QRS complexes (SDNN) at week 1 (pretreatment), week 6 (after treatment), and week 18 (follow-up).
Results Baseline characteristics were similar for the treatment and control groups. The SDNN for the biofeedback and control groups did not differ at baseline or at week 6 but were significantly different at week 18. The biofeedback group showed a significant increase in SDNN from baseline to week 6 (P _ .001) and to week 18 (P _ .003). The control subjects had no change from baseline to week 6 (P _ .214) and week 18 (P _ .27).
Conclusions Biofeedback increases HRV in patients with CAD and therefore may be an integral tool for improving cardiac morbidity and mortality rates. (Am Heart J 2004;147:e11.)
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