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Heart Rate Variability - Science topic

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i want to extract/use ecg data from edf file , that is not supported by software for measurement of heart rate variability
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Hi Ayesha Ikhlaq !
There is also the free software EDFbrowser, which provides us with several other tools.
You can download it at: https://gitlab.com/Teuniz/EDFbrowser
The website of the developer is: https://www.teuniz.net/edfbrowser
You can also try the free version of Kubios.
Best wishes
Lars Brechtel
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  • Heart rate variability (HRV) & Emotion recognition
  1. How to classify different emotions using Heart rate variability (HRV)?
  2. What is your recommendation for above-mentioned purpose?
  3. Which statistical tool/software(s) is (are) preferable for classifying emotions?
Thanks in advance,
Subhankar Banerjee
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I am sorry that I am also offering a rather critical comment concerning the project itself instead of recommending the tools you asked for.
Heart rate will vary with the degree of arousal, but emotion space is three-dimensional. Therefore, you will fail in distinguishing between emotions that vary on the other dimensions, especially between good and bad emotions. What is more, the emotions need to be fairly extreme to show up in the heartrate. A high heartrate can occur with joy or with fear, and a low heartrate can appear with sadness as well as with silent contentedness, as we all know from experience.
Ever since the idea of emotional computing has come up, people have tried to infer emotions from physiological or behavioural parameters (e.g. facial expressions) or both. No viable solution has ever been demonstrated in all these years, even if skin conductance, respiration or whatever has been included in the measurements. So better forget the whole idea.
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Hello!
I'd like to measure HRV from a group of athletes with polar H10 monitors. Is there any solution to do it via PC/MAC without mobile apps?
PS: The only thing I want to record is R-R interval values - I use Kubios to analyze HRV data.
Thanks for help.
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as am I - I want to measure 3 staff in simulation at same time to one device
Andrew
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Hello, I am looking for a good (affordable with scientific evidence) device to measure heart rate variability (HRV) by Electrocardiograph (ECG) or Photoplethysmography (PPG), and I will use it to compare a new smartphone-based HRV measuring App. May I have some recommendations?
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Dear Qi,
I recently saw a review, which might be interesting for you. They have campared 32 devices, which are able to measure HRV.
Good luck,
Milad
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Specifically, how does the App calculate heart rate (beats per minute (bpm))? Is heart rate calculated from an average number of beats over an interval (if so what is the duration of this interval). Or is heart rate calculated from the R-R (inter-beat) interval immediately preceding the beat. The latter does not seem to be correct as heart rate values from the App are not equivalent to R-R values from the App (that said, R-R values do not have a time-stamp so it is also unclear to me when these values are taken).
App details: Heart Rate Variability Logger - app details (marcoaltini.com)
Any advice would be greatly appreciated,
Thank you!
Ellie
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you may find our article on heart rate variability on PUBMED.COM UNDER AUTHOR IOSA D.
SINCERELY YOUR DANIEL IOSA MD PhD FACA FICA Michael C. Meyers
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Hello. I am developing a project during which I would like to track HRV (as my dependent variable) over 18 weeks. I am thinking, based upon the existing literature, that I should be able to do this in recordings of five minutes each, twice a week (at the same time of day each time) over the course of the 18 weeks so that I can analyze overall trend in HRV during that time. I am hoping someone who has experience working with HRV can let me know if this makes sense or if I need to adjust my recording periods. This is a little outside my usual research area, so I want to be sure I am implementing appropriately.
Thank you for any thoughts you can offer!!!
Erin
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Very interesting discussion!
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We are preparing to run an experiment measuring heart rate variability and cardiac impedance using pre-gelled electrodes. We will be running both males and females and are concerned about good dermal contact. I have not seen this issue addressed in the methods sections of the research articles that I have read.
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Commonly used electrodes are adhesive type integrated with gel. If the subject is male, shave the hair and clean with isopropyl alcohol and paste the electrodes.
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There seems to be a lot of controversy about the validity of HRV as a measure of vagal tone. Specifically, Marmerstein, McCallum, & Durand (2021) published a paper suggesting the lack of correlation between HRV and vagal tone. Are there better, non-invasive ways to clearly and accurately measure vagal tone? So much of the literature over the past few decades focuses entirely on HRV in some way or another. Is this still an accurate way to measure vagal tone?
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It's counter-intuitive and nonproductive to think of vagal "tone" as a static index.
The vagus nerve play a key role in hemostasis, circulation and blood clotting. Vital cardiac activity is both a cause and effect of hemodynamic "relativity" (if you will).
An improved measure of Vagal Regulatory Activity (VRA) and/or Vagal Regulatory Capacity (VRC) would reflect VRA at rest and VRC on challenge (exercise and exercise recovery).
Both VRA and VRC could be ratios of pulse volume and/or pulse velocity against selected (e.g., LF) parasympathetic indices.
At least Vagal Regulatory Capacity (VRC) gives meaning to the term "tone" -- notwithstanding that "capacity" is a clinically more useful term.
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We are doing a project on how to screen mdd patients based on heart rate variability and EEG and the data required is for this purpose.
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Occasionally authors share their data together with publications. This is still an uncommon practice but you may get lucky.
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I am preparing a grant and would like to collect 24-hour HRV at multiple time points. I'm looking for affordable, validated devices that would be ideal for my study- and importantly, they cannot have GPS capabilities. I cannot use any of the devices I am familiar with, and I am not having much luck with identifying other possibilities. Any suggestions would be much appreciated!
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Design some novel device based on photoplethysmography (PPG) and record the data in a data storage chip. and then access the recorded data as per your convenience.
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The physiological interpretation of the nonlinear results are difficult. Some Studies have shown that reduction of IBI (RR) signal complexity (Thuraisingham 2006;Papaioannou et al. 2006) may be a feature of cardiac pathology. On the other hand, for respiration signal Caldirola et al. (2004) found that greater respiratory entropy could be a factor in vulnerability to panic attacks. Whereas other studies have found that 0.1Hz breathing as the most dynamic state which is characterized by a specific complexity pattern and is potentially beneficial for cardiopulmonary rehabilitation and conditioning(Matić et al. 2020 ). So, its confusing how the reduction in complexity via slow rhythmic breathing is actually potentially beneficial where as it is also a a feature of cardiac pathology? Can anyone solve this dilemma?
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Recently, Frontiers gave me the opportunity to express my thoughts on Heart Rate Variability and the future of its use. (Open Access: Interpretation of Heart Rate Variability: The Art of Looking Through a Keyhole, DOI: 10.3389/fnins.2020.609570). In that paper I stress, like Joseph Arpaia in the earlier part of this thread, the multicausality of HRV, which makes it an unreliable source of diagnostic knowledge, if measured for just a short period of time. The case of 0.1 Hz breathing is a case in point: the usual jumpiness of successive intervals, where many systems at the same time influence the start of the next breath, the height of the systolic pressure etc., is now replaced by a steady, self-repeating wave in all of these systems. That destroys the assumptions of the entropy-calculations, resulting in very low numbers, whatever these numbers mean under normal circumstances. (This only works for someone who is not hyper- or hypoventilating). All in all: if one feels comfortable and relaxed breathing for some time at 0.1 Hz, just do it, don’t mind the HRV-numbers.
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I am first year doctoral student in Counselor Education and I'm interested in studying the mind/body connection to Covid-19 through the use of Heart Rate Variability. Thank you!
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I have data of ICU Covid patients. I can share
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We are Master Students in Decision Science that just did a study to explore the relationship between heart rate variability (HRV) and procrastination (moderated by emotion regulated- ERQ), in a within-subject design.
The participants measured their morning baseline HRV and filled out the academic procrastination questionnaire in the evening for 7 days.
We are unsure how to analyze the data and which tests to report.
Thank you in advance for your help.
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I am not sure what can happen when we measure morning baseline. HRV can for example being used as a predictor for ventricular tachycardia, specially when the patient has got ICD implanted. It might be possible to find some risk stratification, just prior to VT or VF.The socalled multipole method extracts information both in the frequency domain as well as time domain. The multipole method perform better than in traditional methods
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I am currently trying to analyze pre-recorded exercise data, recorded using polar H10 monitor on polar beat, and transferred to polar flow. To analyze on Kubios. Currently, I am able to extract a csv. file, however, I do not think there it contains any R-R information and does not work in Kubios.
Does anyone know how to extract raw unfiltered R-R data that is pre-recorded on a polar H10 that can be analyzed using Kubios? or any other third-party program?
Many thanks.
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Hi Eric,
when you export the file from polar flow, it is necessary to select the HVR.csv file; in this case, it is possible to import in Kubios like RR.
To my knowledge, this option is available only if you have saved the session with a polar watch and not with the polar beat or flow app by your smartphone; otherwise, it is not possible to save the RR data.
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Slow breathing, usually at frequency of 0.1 Hz, is a method of relaxation and supposed to increase parasympathetic activity. Heart rate variability is used as a marker for autonomic response to such intervention. In the frequency domain of HRV analysis, the HF power is considered as a marker of vagal modulation of cardiac activity, the LF power for sympathetic influence with parasympathetic component, and LF:HF ratio as sympatho-vagal balance, though with controversy.
There is a concern about how to interpret frequency domain of the HRV analysis if respiration frequency is variable (not controlled). For example, as showed in the attached file, while time domain analysis of HRV indicates increased vagal activity in response to slow breathing (increase in SDNN, RMSSD, NN50, and pNN50), the frequency domain is complex; LF (and LFnu) and the LF:HF ratio are higher in slow breathing compared with normal breathing and also compared with breathing at frequency of 0.2 Hz. Also, HF is not significantly different between slow breathing and breathing with frequency of 0.2 Hz while HFnu is lower in slow breathing.
How frequency domain of HRV must be interpreted when the intervention itself is changing the frequency of respiration? Is the time domain a better analysis of autonomic response in such case?
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1- Let's read these papers more carefully. Laborde et al. has mentioned in the introduction that "we refer to parasympathetic activity as vagal tone" that means that they are mixing all types of PNS activities together and call it vagal tone. So when reading this review keep this in mind. Also, they have mentioned that ""in this paper we refer to cardiac vagal tone as assessed by HRV measurement" well we cannot define a concept by a surrogate measure and use it as a method of validation. We need to always look at the gold standards; neural blockade studies and pharmacological blockade studies.
2- Grossman et al indeed have mentioned that "RSA has been shown to often provide a reasonable reflection of cardiac vagal tone when the above-mentioned complexities are considered", but also have listed 6 issues above of this that "can lead to misinterpretation of cardiovascular autonomic mechanisms" and one is respiratory parameters, please also see others. These are mostly overlooked by many researchers in psychophysiology.
3- HF, LF, or SDNN or RMSSD interpretation as a measure of cardiac vagal modulation requires paying attention to the issues raised by Grossman et al. When respiratory frequency changes from HF to LF, HF no longer represents the respiratory modulation of cardiac vagal activity, but LF does, as was shown by pharmacological blockade study. This doesn't mean that under resting state and with respiratory frequency being in HF band, LF still can represent vagal activity, this also has been shown by pharmacological blockade studies.
4- To interpret HRV, you need to first look at the physiology of HR modulation by ANS at the level of the heart and at cell level, as well as interaction between SNS and PNS at central and peripheral levels. As mentioned by Laborde et al "ease of access [and of measuring HR] should not obscure the difficulty of interpretation of HRV findings that can be easily misconstrued". We have too many studies using HRV to measure ANS activity but too little studies on the validity of such measures.
Finally, that many researchers repeatedly mentioned something does not mean that is correct. This is the problem with HRV interpretation in psychophysiology literature, unfortunately, and we continue repeating that without asking ourselves based on what evidence and based on what valid or gold-standard measure? If you look at cardiovascular physiology literature you usually don't see such misinterpretations. Same with 'resonant breathing', nice phrase but no strong evidence.
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I am doing a project about calculating human stress level using Electrodormal activity. For that purpose I am trying to use simple methods like, equation to map EDA values to stress without using machine learning approach. I have seen some researches have used a formula known as Bavesky Stress Index to calculate Stress value using Heart Rate Variability values. I would like to know if there a some kind of equation/method to calculate stress using EDA
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I recommend this book that gives an excellent overview of what EDA can be uses for: https://www.springer.com/de/book/9781461411253
I think an important question is if you want to compare the stress level within or between individuals. Compared to heart rate, EDA varies quite a lot from person to person. If you have established a baseline for one participant, you will probably be able to tell when he or she is stressed quite reliably by comparing measures that are discussed in detail in the publication I mentioned, for example the frequency of nonspecific electrodermal responses (NS.EDR). But I think it will be much more difficult to obtain something like an objektive stress-score that is comparable between individuals.
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Vagal activity has been shown to reduce inflammatory activity ( )and modulate immune responses. In COVID-19 infections young patients usually experience mild symptoms, whereas in some elder patients fatal interstitial pneumonias are observed.
Vagal activity, as seen from respiratory modulation of heart rate, is strong in childhood and dimishes with aging .
Is there any observation, that vagal activity might protect against too strong immune reaction as suspected in pneumonia?
Would it make sense to strengthen vagal activity as a preventive measure in the population before the big wave of infection arrives?
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In 8579 cases from 30 provinces out of Wuhan, “the median age of cases was 44 years (33-56)... “ though “schools in China were closed for most of the epidemic because of the 2020 Chinese New Year holidays” https://www.thelancet.com/pdfs/journals/laninf/PIIS1473-3099(20)30230-9.pdf
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Hello. I want to measure HRV before activity, during activity and after activity. Each measurement will take 5 minutes. I have limited budget and i m searching for a portable device. And it must be reliable for scientific research. I've found some alternatives like shimmer ear clip, polar watches, polar chest strap and some finger and you may offer me different devices. What is the best option for me?
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To study HRV in a scientific research environment it's important to record the ECG. The ECG is the basis for heart beat detection and once its saved by the device you are able to check, whether all heart beats are correctly identified.
We made good expierence with Hexoskin Hx1 and Faros 180°/360°.
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Would you recommend Kubios?
I work with pigs and considering using Polar H10.
Thank you
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You can use PhysioNet Cardiovascular Signal Toolbox for Matlab described by
this paper:
Vest A, Da Poian G, Li Q, Liu C, Nemati S, Shah A, Clifford GD, "An Open Source Benchmarked Toolbox for Cardiovascular Waveform and Interval Analysis", Physiological measurement 39, no. 10 (2018): 105004. DOI:10.1088/1361-6579/aae021
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In my study I have exposed the volunteers to 4 separate 45 second stressors that are a mixture of modalities (audio/visual) and task types (emotion-evoking/cognitive) with 3 minute baselines in between. A continuous ECG is trace is taken throughout the experiment, from the gross heart rate I hope to work out heart rate variability. My aim is to test the validity of heart rate variability as an objective stress assessment method for psychophysiological stress. My question is at what point on the ECG trace for each volunteer would I analyse the gross heart rate to work out Heart Rate variability for each of the four stressors i.e. pre-stressor, post-stressor and why ?
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The body's should develop compensatory response to stress with 20seconds in which the effect could be felt by the brain although it depends most times on the intensity of the stressor
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One month data. According to WHO the recommended dose of physical activity (PA) is 8000 steps per day. Are these true levels of PA or a reflexion of job burnout? And how this PA is related to Heart Rate Variability (HRV)? Is it useful to set limits of PA?
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Dear M.Dimitros,
I don't find any recommandations of steps per day by the WHO, but some people try to translate physical activity's recommandations in steps per day. The recommanded number of steps per day is still under discussion, in particular depending on the public health objective.
Did you know this review by Bassett Jr on step counting history, information about step/day and health ?
Below, some data about cardiac autonomic variation after walking
I don't know if there is a need to set limits in physical activity because I think that this limits are personal, dependant on medical condition.
I hope this could help,
kind regards
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I am a PhD student with a psychology background. My supervisor and I are thinking to investigate how HRV changes while phobic participants exposed to a phobic image over time. We are quite new to HRV research. I wonder if anyone can recommend some readings on HRV research methodology and analysis for complete beginner?
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Hi Mark,
I may recommand to read Shaffer et al. 10.3389/fpubh.2017.00258
and Laborde 10.3389/fpsyg.2017.00213
Hope it helps.
Pascal
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Currently I am working on a project about measuring stress using EDA. I have seen a formula known as Baevsky Stress Index to calculate stress using Heart Rate Variability. Does anyone know some kind of formula to calculate stress using EDA value?
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Thank you
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Below is the project title:
This project will investigate how psychological stress and mental workload impact physiology. Using Heart Rate Variability in comparison to an array of ‘gold standard’ physiological measures to investigate how task interaction impacts on psychophysiological stress.
Physiometric analysis methods -
•Stethography
•Galvanometry
•Blood pressure
•Core Body Temperature
Which of one of the physiological measures stated above would be best to use to measure acute stress during a driving simulation and why ?
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cortisol, heart rate, blood pressure
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I am searching for a toolbox that can analyze changes in different physiological states using simultaneously recorded signals with trigger markers including EEG, Heart rate variations, galvanic skin response, and respiration rate.
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The FieldTrip toolbox has functions for heart rate and respiration, but I'm not sure about the others. I would look into it.
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Hi colleagues. I'm seeing alot of papers published saying "low HRV" "high HRV" but one thing I noticed is that the indice of measurment (say SDNN, RMSSD or HF) are parasympathetic measures.
Is it correct to use this indices and report as low or high HRV after all? Isn't just low or high parasympathetic predominance? Is it possible to presume a low or high balance between sympathetic and parasympathetic on those cases without having control of sympathetic variation?
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Dear all,
I am using the pulse sensor, a low cost device for PPG detection in the index finger
Although I am quite still, the detected signals are heavily corrupted with low-frequency and high-frequency noise . Which simple and robust signal processing technique do you recommend for
solving this case? If it works in real time, much better.
I need the clean PPG on order to measure accurately the heart rate (HR) and the heart rate variability (HRV).
Thank you all!
Fernando
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An optimal filter for short photoplethysmogram signals (https://www.nature.com/articles/sdata201876) with Matlab source code.
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From reading the literature on Heart Rate Variability (HRV) use and interpretation, we might be at the stage where we could add the sensors for HRV in healthcare units where unconscious patients are cared for. Thoughts?
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I agree with what you said. I interviewed over 100 patients who previously were reported as once being unconscious. This link, which is a free to view course, has the descriptions of what they experienced. http://www.rnceus.com/course_frame.asp?exam_id=15&directory=uncon.
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By this question i want to know that how control system "classical or modern" can be used in the analysis of heart variability in any method of analysis linear or nonlinear, mathematical model preferred.
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Hello, I am in the starting blocks to write my bachelor thesis. The thesis is supposed to be an empirical study, concerning heart rate variability and well-being and if HRV it might be a reliable measurement for (subjective) well-being. What I want with this discussion is to get input from you, what do you know about the two topics?
Thanks for your help!
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We have done work in this field for 30 years now. Heart rate variability (HRV) actually is a term that was not chosen well, as it implies an instability of regulation and was treated like this for a long time eg. by cardiologists. In a popular cardiology textbook, HRV is called ' a not-treatworthy heart rhythm disturbance' which is thought to disappear with age.
Actually, a better term would be 'heart rhythm flexibility' (HRF), as the phenomenon is an expression of the regulatory ability of the autonomic nervous system to cope with different ambient situations and especially to interact with other body rhythms like respiration, peripheral blood perfusion and blood pressure rhythmicity. So let me use this term HRF.
Low HRF is known to predict bad outcome in several cardiovascular diseases and even total mortality. HRF is reducing with age but unusually high values can be found in centenarians, implying that HRF i good for life expectancy. Healthy interventions like moderate running, omega 3 fatty acids, stop smoking, meditation increase HRF in the aftermath. On the other had, many bad things for health life like fine particulate matter, heavy metals, stress, reduce HRF in a matter of minutes.
See some considerations on HRF in our papers:
and advanced methods to disentangle HRF components in these papers
I wish you good luck for your thesis!
Max
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HRV normative data
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Thank you, David.
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In an experimental study we tested various breathing exercises and monitored respiratory flow, beat-to-beat blood pressure, and heart rate. We developed MATLAB script for baroreflex analysis based on the sequence method and the result was interesting.
Now, we want to compare breathing exercises regarding the magnitude and phase of transfer between respiration -> blood pressure, respiration -> heart rate, and blood pressure -> heart rate using transfer function analysis.
I appreciate if someone with experience in such analysis can help us with a MATLAB script.
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dear Ali
you can find some suggestions of SAP-HP phase and gain analysis in A. Porta et al, Am. J. Physiol. 300, R378-R386, 2011 and J.C. Milan-Mattos et al, J. Appl. Physiol. 124, 791-804, 2018.
All the best, Alberto
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Looking for a device to analyse a large sample during 5min.
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Maybe Aidlab (229$)? Here's the article how can you connect Aidlab's ECG readings with Kubios: https://medium.com/@Aidlab/exporting-your-ecg-readings-with-aidlab-4dd5c049078a
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We are currently using a Polar H7 and H10 chest sensor in combination with a V800 watch to register heart rate variability (HRV). Through Polar Flow, we are able to export RR data from the V800, at a sampling rate of 1 Hz.
We aim to export raw data at a sampling rate of 1000 Hz.
Does anyone know how you can adjust the sampling rate of the Polar devices to be more precise?
In the export file, there is only information at a frequency of 1 Hz, so we assume that other programs to perform more in depth analysis, will not produce more precise results if we are not exporting precise data?
Thank you very much.
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Hello Lisa,
Your reasoning holds, but only for the 'standard mode'. If it is your intention to do HRV-analysis, then this standard signal is unusable. You should use the RR-recording mode for that purpose. Then you will get RR-interval series that are generally OK, if I understand the article by Giles et al. correctly (I only have scanned it quickly).
Under no circumstance will the Polar give you the raw ECG; it is not an ECG-recorder, but a Heart Rate Monitor, and that is what it gives you: heart rate.
If you have more questions, maybe you should write to me directly?
Good luck,
John
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I would like to measure HRV in mice. Is it doable with following setup: ECG with sampling rate of 1000Hz and in lightly anesthetized mouse 2% isoflurane? If yes, what are your experiences with analysis software? Thanks.
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If this question is still relevant to some we have released a software specifically designed for that. You can find it at this URL:
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We have used the equivital SEM-EQ-02-SEM-007 in our lab to record ECG in order to calculate heart rate variabilty. So far we have had massive artifact problems (mainly caused by movement) which we never got rid of completely. What is more, these artifacts occur eventhough our participants do not really move a lot, because they are sedentary throughout all of our experiments.
I would like to know wether this is a common problem with Equivital products or if this is just a malfunction of some sort in our equipment.
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Dear Ingmar, thank you for your opinion. This matches more or less with my experience. You say that the belt is the main problem, but is there an alternative? I was sure way that you could only use Equivital belts with the Life Monitor or am I wrong?
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Is it enough to analyze heart rate variability(including Time domain and Frequency Domain)in short-term measurements (2.5 min) ?
Because I want to do a research on how the microclimate in different space of urban square that influence the physiology in summer. But it is too hot outdoor so I need to reduce the measurement time to the least that would not let subjects be exhausted.
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Yes, it is possible to analyze (some) indices of HRV in such a short period. For time domain measurements I guess this 2.5min provides enough time for a reliable estimation of most HRV indices. For frequency domain, you could analyze the HF index, but I'd be a bit concerned about LF and VLF estimations using such a small time window. In this condition, estimation of "low-frequency variabilities" are less reliable since you don't have enough time for the ocurrence of too much LF and VLF events. In other words, you don't have enough frequency resolution to analyze these "slower components". In this respect, some evidence suggest that the duration of the segment must be 10 times greater than the smallest wave period to be analyzed. This means that for the LF (i.e. smallest wave period is 0.04), you'd have to have at least 250 seconds, and for the VLF (i.e. smallest wave period is 0.003), you'd have to have at least 3333 s.
We discussed this issue a little bit in our paper listed below:
Peçanha T, Bartels R, Brito LC, Paula-Ribeiro M, Oliveira RS, Goldberger JJ.
Methods of assessment of the post-exercise cardiac autonomic recovery: A methodological review. Int J Cardiol. 2017 Jan 15;227:795-802. doi:
10.1016/j.ijcard.2016.10.057. Epub 2016 Oct 23. Review. PubMed PMID: 27836300.
Cheers
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Slow breathing increases fluctuation of blood pressure over the breathing cycle which is followed by the larger amplitude of respiratory-sinus arrhythmia. One main mechanism is the baroreflex. In our study on slow breathing we found that although blood pressure variability stays more or less the same over 3 minutes of slow breathing, RSA decreases over time.
Can this be due to habituation of the baroreflex?
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Dear All,
I think, your data was collected from participants that breathed 6 times/min (6P breathing) first time.
We used 6P breathing (HRV biofeedback) for treatment asthma, depression, and fibromyalgia. Our patients (n > 100) received 10 sessions during 10 weeks. The session included four 5-minute tasks. We taught them to find individual breathing rate and trained to breathe not so deeply. Treatment effect was received after 4 procedure when patients caught the right skill of the breathing. Each patient found individual breathing frequency in range (0.076- 0.107 Hz) and individual tidal volume. After 4 sessions amplitude RRI oscillation was stable for 5 minute and usually HR decreased. Sometime HR increased if in base lane it was low (50-60 BPM).
Evgeny
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Hello.
I use Brainvision analyzer to analyze ERP data, and lately I've started to collect ECG in parallel to the EEG recording, in order to see the correlation between some ERP components and HR related measures (Specifically - the heart rate deceleration seems like a good candidate, as it is event related, but I'm also thinking about using HRV in an event-related style, if it's possible). I have some experience with using Analyzer for ERP analyses, and I was wondering whether anyone has any advise on how to use Analyzer to detect R-R intervals, and to analyze related measures.
Thanks a lot,
Isaac.
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Dear Johanna.
We use a Biosemi Activetwo system for EEG recording. To record simultaneous ECG we simply added an additional single external electrode and connected it in proximity to the heart (a bit to the left). When referenced to the right mastoid (which was used as a reference anyway) it provides ECG measurement (enough for HRV or HRD analysis, but probably not enough for cardiologists...).
The issue of analyzing it was a bigger problem. What we did (but haven't tested yet), is to simply extract the ECG signal in Analyzer II, export it to Kubios (software for the analysis of HRV), where we did some cleaning (without changing the time course), and then exported it to Matlab. We also exported the triggers from Analyzer to matlab, and tried to calculate event-related RRs in Matlab.
As I said - we haven't tested it yet for precision, so not sure about it. If you have any better method - I'll be glad to know.
Best,
Issac.
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This is for an intervention study with older adults with MCI and cardiovascular risk factors for dementia.
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We use Biopac in our lab for ECG and respiration. The ECG files from the acknowledge software are converted to Kubios for HRV analysis. Very user friendly. If you are collecting data in field settings, there are a number of validated heart rate monitors and smart phone devices. If this is the route you're taking, have you tried a literature search?
Best of luck!
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Hi guys, recently some of my data are presenting the following error message ""Invalid RR interval values (RR zero or negative!). File cannot be opened" error" . I'm collecting HR and HRV through Polar RS 800 CX and analyzing with Kubios HRV. Does anyone faced some similar problem or know how to fix it? (This problem appears while opening the data txt files with Kubios)
Thanks for your attention all! Greetings from Brazil.
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Hi,
I had exactly the same problem with Kubios HRV. Kubios Standard supports just RR data. In the premium version I could change the data type to ECG and it worked.
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I have to compare very short episodes for 4 experimental conditions. They have not the same duration (30, 40, 100 seconds).
I wonder which index in time and frequency domain I may use to make these comparisons and if possible, to have some references of studies doing these manipulations.
thanks
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Hi Mathieu,
in general one should not compare HRV indices from intervals of different lengths because they might not be comparable. In your case this would mean that you have torestrict your analysis to the shortest time interval (at least if you plan to compare the experiments against each other).
The short duration obviously restricts the indices you can use. From frequency domain LF and HF might produce usable results (just considering the frequency ranges which are covered by such indices), VLF not. However, 30 seconds are very short considering what is done and recommended in other works (see the standard paper:
"Heart rate variability: standards of measurement, physiological interpretation and clinical use." ).
Greetings, Sebastian
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I am looking to purchase a portable device that has the capability to measure heart rate variability along with brain stem evoked potentials. The device output should be compatible with commonly available HRV analysis softwares (e.g Kubios).
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Thank You so much.
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Hello everyone,
is there any recommendation of window width and overlapping rate in frequency analysis (LF and HF, excluding VLF) using Welch's periodogram in short-term HRV measurements of 5 minutes duration? As I know, often a window width of 256s and overlapping of 50% is used, but mainly for long-term HRV analysis (24h). I know that a bigger window lead to a higher frequency resolution. But a window size of over 80% data length (like 256s windows) seems to me a little inappropriate. In an example of "Kubios HRV software user guide" there is a window width of 150s and 50% overlapping used for analysing 5min measurements.
Thank you in advance!
Kind regards
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Dear Alexander,
as you know, the window width, T, defines the frequency resolution (capability to distinguish a couple of harmonics as two separate spectral peaks). Frequency resolution of the rectangular window is =1/T, and the resolution is lower for the popular Hann window (about 1.6/T; see https://goo.gl/UeWcb1 ). Since the lower limit of the LF band is 0.04 Hz, you should select T > 1.6/0.02 =80 s for a frequency resolution of at least 0.02 Hz with the Hann window. The Kubios example proposes a longer window (T=150 s) to properly estimate also the VLF power.
You will probably use the Hann window (often erroneously dubbed "hanning") because it efficiently decreases power leakage from the main lobe to the side lobes. This, however, is obtained at the cost not only of a lower frequency resolution, as already mentioned, but also of a greater variability of the spectral estimate. In fact, the Hann window multiplies a block of N samples with a cosine wave, and the equivalent number of degree of freedoms of the statistical estimate is substantially lower than N. Therefore, the variance of the spectral estimate increases. With a 50% overlapping, most of the degrees of freedom that are lost in the upper half of the window “i” are recovered in the lower half of the successive window “i+1”. You could increase the overlapping (e.g., 90%) but in this case you also increase the computation time with only a little increase in the equivalent number of degree of freedoms. You could find further information on this report:
In conclusion, if you have to estimate LF and HF powers only from 5-minute long recordings, I would suggest to use Hann data windows of length T=120 s, with 75% overlapping. In this way, the resulting periodogram is the average of 7 FFT spectra, each with frequency resolution around 0.014 Hz.
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I would like to measure Heart Rate variability with the use of a watch in a research study. Can you tell me what watch I can use.
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Hi Keith
I recommend you the use of Polar V800 with H7 or H10 chest strap.
See link below, the watch is on cyberweek sale at the moment.
RR-Interval measured by V800 has been scientifically validated and showed an error rate of 0.086% and an ICC > 0.999 towards ECG.
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Premise 1: Neurogenic bradycardia and RSA are mediated by different branches of the vagus and need not respond in concert.
Premise 2: Neurogenic bradycardia associated with orienting is a phylogenetic vestigial relic of the reptilian brain and is mediated by the dorsal motor nucleus (DMNX).
Premise 3: Withdrawal of cardiac vagal tone through Nucleus Ambiguus (NA) mechanisms is a mammalian adaptation to select novelty in the environment while coping with the need to maintain metabolic output and continuous social communication.
(From Porges SW (2013) Polvagal Theory. NY: Norton)
The current evolutionary vagal evidence indicates that neither Premises 2 nor 3 are accurate. Also 1) there is a confluence of evidence regarding Premise 1 showing that the DMNX  may only manifest vagal effects upon heart rate under conditions of severe physiological respiratory distress (and even this is not very well documented), 2) Porges provides  merely very indirect findings to support his hypothesis (and his Figure 2.3 of  the time course of putative DMNX-stimulated bradycardia in a single anesthetized rabbit shows much too rapid onset and offset for the heart rate drop to be a response of the unmyelinated DMNX vagal fibers [which should have a much more gradual onset and offset than shown because slow conduction time of these fibers prevent sudden changes]), and 3) no mention is made by Porges of earlier findings that indicate that the DMNX is not implicated in normal vagal control of heart rate.
Nevertheless, perhaps there are strands of direct evidence of which I am unaware? In any case, polvagal conjectures have become very popular in psychology, psychophysiology and therapy literature. It seems, therefore, high time to critically assess the value of Stephen Porges' ideas in this area.
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Dear Emily,
If you mean that myths can affect people's lives, I have no problem with that. I also agree that is probably what has inflated the popularity of the polyvagal speculations. Wampold, in his work, makes a very convincing case that improvements in psychological wellbeing don't rely very much at all upon the method (or, perhaps, ritual would be a better word) chosen: improvement has more to do with having some ritual one believes in, a practitioner of it who seems competent to the client, a joint plan and goals, and maybe most importantly an atmosphere of trust and compassion.
However, my problem is that the ritual becomes conflated with science in this case, and the scientific aspect is used to sell the approach, when all the scientific evidence speaks against the speculations. There are, I hope you will agree, multiple myths that could be invoked to explain the various conditions to which you allude. Why not construct an explanation (myth) more consistent with what we know? That would provide, in my opinion, a much healthier approach for the therapy (i.e. ritual), as well as the societal acceptance of it, in the long run. Right now, what might happen to a client who has gone through a therapy with a polyvagal explanation (and who believes in "science" as the new religion) when they eventually read that in the New York Times that the polyvagal ideas have been thoroughly debunked? What happens to the credibility among scientists of a potentially helpful therapy (ritual) when the underlying scientific premises are thoroughly falsified, as seems to be happening. I don't think that is good for anyone. And I strongly believe one could adjust the vagal myth, employing autonomic explanations that have been around for at least a century before the polyvagal speculations were suggested. That is all I am trying to get at.
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Which is the most used parameter characterizing the HRV? If dk is the duration of the k-th R-R interval, the standard deviation of these dk values is a first possibility. Moreover, using dk, one can derive the corresponding Heart Rate value HRk = 60/dk (beats/min). The standard deviation of these HRk values could be another measure of HRV. Other propositions could also be considered. But which is the most used parameter?
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Dear Dr. Zaharia,
It is not my habit to promote my own work in these question/answer discussions. But I did write a short, comprehensive review of what one should know when entering the HRV-measurement field. It can be found on the pages 101-106 of my recent review in Physiological Measurement, to be found 'Open Access' on: . In the subsequent pages I explain how these variations are coupled to autonomic nervous activity and the interplay, among others, with blood pressure control.
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I have 300+ participants to analyze in Kubios for heart rate variability. Currently, the only option I know of, is to save each data file one by one, which is very time consuming. Is there a way to run a batch in Kubios, or MATLAB scripts to speed the process up?
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Dear Shai Porat
Well because I don't know about your research. But I think on Matlab you maybe can create a model-builder or some scripts to speed the process up. Anyway, it is a challenge.  Additionally, maybe just my imagination but,
can you use Matlab instead of Kubios for your data process ? Or you can input your raw data together (maybe one excel file is possible) If possible, Matlab can do them all and save each data file automatically and  qucikly.
Hope my answer can help you.
Best wishes.
Kai Liu
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In order to validate a Doppler radar used for Heartbeat Rate and Heart Rate Variability extraction, an ECG (electrocardiograph) is often used as reference. Which is the accuracy of an ECG? Can be the Doppler radar more accurate than the ECG? How the accuracy of an ECG could be determined?
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maybe a fft to estimate the high low frequency ratio as another indicator?
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Dear Colleagues,
I am about to start working with some aversive learning paradigms in humans and wanted to ask for some suggestions on measuring fear responses to threat/mild pain stimuli. 
Specifically, I wonder if you could share some tricks and tips on how to improve quality and reliability of galvanic skin response (which, I know, is inherently noisy and can be tricky to collect in some subjects) or maybe recommend some good ways/techniques to complement it (e.g. by combining with heart rate variability, etc).
Many thanks in advance!
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I am trying to analyze PPG data for HRV. I am curious what software are available for automated cleaning of raw PPG data to then be used in Kubios (or other suggested GUI) for HRV analysis?
thanks
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Hi Shai, with ppg signal you should filter high frequency noise before detecting peak and valley for HRV calculations. Still have low frequency noise like base line and motion artifacts in ppg, please refer to some my articles.
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I am intending to explore the use of heart rate variability (HRV) for occupational settings. Real-time monitoring of physical and mental stress could be beneficial to for occupational safety and health. While going through literature, I have seen that HRV is sensitive to the sympathetic and parasympathetic activity of the autonomic system. But are there any HRV metrics available which could differentiate occupational physical stress and mental stress? Or measure them separately (mutually exclusive)? 
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Hi Dr. Umer,
Unfortunately, HRV indices by themselves don't yield this kind of information. They can tell us a lot about what's going on in the autonomic nervous system, but not what factors are behind what we observe. That's because HRV is affected by so many different endogenous and exogenous factors.
That said, sometimes we have an a priori reason to expect to see reduced HRV, such as in individuals with certain psychological disorders compared to healthy controls. For instance, people with alcohol use disorder often have reduced HRV, but this is probably caused by a combination of psychological stress and the physiological effects alcohol.
A way to start to separate this all out it to utilize complementary biomarkers/measures. For you that might be a combination of urine or blood for physical stress biomarkers, and subjective mental stress measures.
David 
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I would like to know if some parameters of heart rate variability such as SDNN, rMSSD, pNN50, VLF, LF, HF and LF/HF have a value of normality?
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A recent publication on 'Reference values for time- and frequency-domain heart rate variability measures' by S. Sammito et al. 2016:
However, bear in mind the criticism on this article by Bauer A et al. (and other experts on this topic like M. Malik, H. Huikuri, P.K. Stein):
As a response of S. Sammito they published new reference values:
Hope this sheds some light on the ongoing discussion on reference values of HRV parameters.
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I need comments of experts. Most of my publications in Russian. Not many people involved in the study of heart rate variability in the methodological level in Russia. Therefore, I almost did not have feedback on my results.
To formulate short, I discovered that:
1. In addition to the classic two phenomena of modulation of heart rate (respiratory sinus arrhythmia, low frequency waves associated with Mayer waves and thermoregulation) there is at least one more with a period of 3 cardiac intervals. I discovered this when I performed factor analysis of periodogram of heart rate tachogram and found out a wave of factor loadings with the peak on this period. A breaf publication in English is here https://arxiv.org/abs/1005.0776v2
2. Amplitude of heart rate modulations with the period of 3 cardiac intervals is associated with mental vigor and mental work capacity. I found positive correlations with a scale of psychological questionnaire and with results of computer game Tetris.
3. Amplitude of the 3 cardiac intervals heart rate modulations is associated with the frontal cortex activity. There is positive correlation with EEG defined level of cortex activity. This is published here http://link.springer.com/article/10.1007/s11055-013-9805-1
I have attached my presintations with extended information on this.
Please, answer me what do you think on these results?
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Фёдор! Спасибо за ответ.
Текст любой статьи могу прислать. Может быть он есть в  RG.
В ответ на Ваши вопросы могу написать, что спектральный анализ делаю методом прямого дискретного преобразования Фурье своей программой. Алгоритм БПФ не применяю. Анализирую тахограмму кардиоинтервалов -- зависимость их от номера кардиоинтервала, а не от времени, как делают многие. Поэтому период периодических модуляций сердечного ритма выражается в числе кардиоинтервалов, а частота в числе обратом, то есть 1/число кардиоинтервалов, то есть сколько колебаний приходится на один кардиоинтервал. Раз период составляет несколько кардиоинтервалов, то частота -- доли периода, приходящиеся на один кардиоинтервал. Этот подход используется гораздо реже, но возможность его использования упоминается в "Стандартах" 1996 года. Анализ зависимости оот времени у меня вызывает сомнения, так как получается зависимость времени от времени, то есть время и по оси абсцисс и по оси ординат. То есть одна и та же информация как бы дублируется избыточно по обеим осям, что странно...
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I am not sure if this is possible or not. I am aware that greater than 100 ms is generally considered to be "good" hrv. 
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First, higher HRV does not necessarily mean "good", and second 1000 ms is very unusual for SDNN, and most probably not correct or not in healthy state.
Here is the normative data:
In our study with healthy adult subjects performing slow deep breathing which results in considerable increase in HRV, the highest SDNN that we had was around 300 ms, this is the ceiling effect and I don't expect we could have higher values. This was happened in an athlete with mean HR of 56 (MeanNN of 1069 ms) and RMSSD of 242 during slow breathing. I checked the relevant literature and I couldn't find anything even close.
I wonder if Mohammad Karimi can provide us a reference indicating such case?
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HI everyone 
Any suggestion of linking heart rate variability (HRV -LF/HF) values to mental fatigue directly ?
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That's a good question. I am not familiar with a literature on fatigue and HRV, though admittedly I've never looked.
I can say though that generally there are not set thresholds associated with HRV indices because HRV varies so much across individuals and is affected by so many different factors.
Also, not sure if you're referring to the LF/HF Ratio measure, or LF and HF HRV as distinct measures, but I would caution against utilizing the LF/HF Ratio measure.
This measure, while once thought to reflect the balance between sympathetic and parasympathetic arousal, is now widely eschewed and avoided. There are some nice articles out there explaining why. (e.g., Billman, 2013, The LF/HF Ratio Does not Accurately Measure Cardiac Sympatho-Vagal Balance; Goldstein et al., 2013, LF Power of Heart Rate Variability is not a Measure of Cardiac Sympathetic Tone...)
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There are two groups of patients with equal number of patients on betablockers. Will this effect the difference of heart rate variability in the two groups if both are equally on betablockers? I believe that the difference will not be effected although the absolute HRV will be effected.
Frequency domain parameters are not being tested and only time domain is being analysed.
If you can comment on this with some evidence linked please it will be very helpful.
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Dear Habib,
agreed, there should be no issue with a t-test (precisely because both mean an SD increase at the same time). If the effect of beta-blockers was very strong, it might make the distribution of HRV values non-normal, making it not that suited for a t-test, but it might not be too bad in practise, I suppose. And it shouldn't affect nonparametric tests anyway, I think - it's just the absolute difference that might get affected.
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Looking for someone to suggest the easiest way to get to frequency domain outputs of the HF band measured in 'Hz' rather than 'sec^2/Hz' (or - how to convert? - simple 'Hz' seems to be the more common way to report HF power in the literature).
Also, looking for a get around for the fact that time domain outputs (like RMSSD) seem to require a scripting license in AquKnowledge. Is there free software that can do the same? - or, perhaps better, free software that will give both time and frequency domain outputs in one quick analysis? 
Failing that - suggestions for better analysis methods appreciated!
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Hi all, a belated thank you for your help. I'm now using Kubios, which I have found is much better suited to what I'm trying to do. 
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Aquknowledge 4.1 gives HRV frequency domain statistics in units of "sec^2/Hz":
e.g. "Power in the high frequency band: 0.000374971 sec^2/Hz"
However, the recommended way to report frequency domain stats seems to be "msec^2":
"The measurement of VLF, LF, and HF power components is usually made in absolute values of power (milliseconds squared)". see: http://circ.ahajournals.org/content/93/5/1043.full 
Can someone walk me through what I should do here? 
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Hi all, just wanted to say a belated thank you for your help with this issue. Much appreciated. 
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We recorded ECG (no control for breathing, but with simultaneous EDA recording), and after extracting RR tachograms, I noticed that one participant had what looked to me an unusual RR pattern (I've attached a picture). Whereas most people have very busy (dense and spikey) tachograms, this person had very large waves so that frequency analysis showed almost purely LF power.
Is this pattern unusual, and what does it mean? Is it breathing? Possibly relevant is that the peaks and troughs of the RR seem to roughly correspond to EDRs, does that make sense?
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Hi Adrianne,
Respiratory parameters not only affects frequency domain indexes of HRV but also time domain indexes like RMSSD. Look at the attached picture from our study. Here NB is uncontrolled breathing and PB is paced breathing at 0.23 Hz (similar to breathing rate at baseline). SB0,5,10 are various slow breathing techniques. These conditions (except baseline) were while subjects breathed through a breathing circuit usually leading to higher tidal volume. Both breathing frequency and tidal volume affects HRV. The lower breathing frequency the higher RMSSD. Effects on frequency domain depend on the breathing frequency; BPV and HRV frequency exactly follows the breathing frequency. Therefore, if breathing frequency concentrate at HF you will see a peak in HF band. If breathing frequency concentrate in LF band, you will see a rise in LF power. Pay attention that this effect is more obvious when you look within-subject not between-subject. In some studies there has been no clear association between resting breathing frequency and HRV frequency domain indexes in healthy volunteers when they did between-subject comparisons (https://www.nature.com/articles/srep37212). But if you run a within-subject comparison, any significant change in respiration parameters (rate, depth) would affect all HRV indexes.
Any control over breathing requires using a visual or auditory cue and engagement of the subject with breathing protocol actively and continuously. The effect of this engagement on attention is large enough to reduce pain perception regardless of the breathing frequency (according to our study). Therefore, guided (paced) breathing even at natural individualized breathing frequency causes distraction. Paced breathing at natural breathing frequency does not have major impact on HRV indexes unless subjects do deeper breath with paced breathing. This can be controlled by appropriate instruction and training. Also, in very few subjects paced breathing may cause distress but this is rare.
Now the question is: can you control breathing during your study without interfering with the main task of your study? if yes, my suggestion is to control breathing by paced breathing at frequency of 12-15 breath/minute. Pay attention that even telling subjects to do "normal" breathing will shift their attention to their breathing and usually causes a decrease in respiratory rate. So, if control of breathing is possible, paced breathing is better than just instruction.
But, if control of breathing by paced breathing interferes with your study main task I would suggest to not do any control on breathing and even not explain subject to perform "normal" breathing. Just monitor it and then exclude those with breathing out of the HF band (they are few) and see if that affects your results. 
If you can provide more information about your study aims and steps I can be more helpful.
GL
Ali
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Dear colleagues, I would like to hear from you which sample of RR intervals would be more suitable for HRV analysis: fixed number of RRi or fixed time?
I usually work with fixed number, which has been well accepted, but sometimes reviewers criticize this approach.
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Hello all,
I believe that the choice between a fixed number of samples or a fixed time depends on the method/algorithm that will be use to quantify HRV. The same applies to the decision of how many samples or how much time to chose. For instance, many studies involving the calculation of entropy features (information domain analysis), which consider the probability of certain *events* to occur in the whole set of data points, use a fixed number of samples.
All the best,
Claudia
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In the experiment for my dissertation thesis, I want to measure kinematic, kinetic and physiological parameters (VO2max, heart-rate, breath-rate...) from treadmill running and synchronizing all three of these data streams would be really helpful for future processing. 
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I am not familiar with QTM or Geratherm systems but if they provide analog output signals, you can use an independent A/D acquisition system (along with a computer) to collect these signals. In doing so, the signals will be synchronized. Alternatively, if either the QTM or Geratherm system has an analog input channel and the other an analog output signal, the output signal can be fed into the system with the analog input channel. There are other possibilities but I would need more details (e.g., desired sampling rates and durations).
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This is for research with children.
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If you are able to see all the waves clearly without any movement artefacts, there should be no difference between 3 lead ECG and added chest lead ECG, when looking for heart rate variability. 
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For heart rate variability analysis I'm using a Polar rs800x; anybody know another heart rate monitor with R-R recording more modern that I can use?
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You can use the Firt Beat (https://www.firstbeat.com/en/) equipment, or eMotion sensor (http://www.megaemg.com/products/faros/). But I have had success using the RS800 or V800 from Polar.  You can use Elite HRV app wtith cellphone or using the system from HRV4training (http://www.hrv4training.com/). 
Cheers
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What are the best and most reliable tool box and script that can be used for analysis of heart rate variability during Polysomnography ???
is it better to use the HRVAS or Kubios??
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Thank you very much for your comments and help, can I ask you what tool do you advise to use to split the recording of the EDF file into time chunks?? I do not have the original PSG. And if through the MATLAB, how can it done?? thank you again for help, really appreciated.
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We analyse the respiration trace from a respiratory belt in a study on emotions. We want to calculate the average respiratory rate across a given block (1 minute). An automated analysis with LabChart detects most respiratory cycles correctly. However, some peaks in the respiration trace seem to be abnormal / artifacts (e.g., very short or very shallow breaths). Do you know any guidlines that inform how to include only the meaningful data into the analysis and rule out breathing artifacts?
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Hi Lukasz,
I'm studying about breathing and pain and in my study I record lots of respiratory and other physiological data. I don't know about such guideline but following is my comment based on my little experience.
First you need to distinguish an artifact which is usually due to movements not associated with respiration from a respiratory cycle which is real but largely different from other cycles ("abnormal" as you mentioned). Unfortunately most of the respiratory belts are very prone to movement artifacts. You can manage such artifacts by:
- Filtering of the belt signal with low pass of 0.5 to 1 Hz give you more smooth signal. If the rate is the main outcome this is helpful. If the amplitude is also important make sure that filtering doesn't change the amplitude a lot. You need to play with the filter.
- Looking at EtCO2, I guess you don't have it.
- Looking at other signals like ECG and EMG may help to find movements, if they are not already filtered for movement-related artifacts.
- If you align the belt signal and RR-interval (after interpolation) in parallel and then look at the respiratory sinus arrhythmia that can help since you expect variation in RR-interval associated with respiration.
- The amplitude of the R-wave in the ECG also changes with respiration. If the high pass cut off is set to 0.05-0.1 Hz and not 0.5-1 Hz, then respiration-related changes in R-wave amplitude and also in the baseline in the ECG signal are more visible.
- The peak of the HF band of the HRV is usually corresponding to respiration frequency. You can test its accuracy in a sample of your subjects which you are sure you don't have artifacts. The Kubios software provides ECG-derived respiration data as separate output in addition to HF peak.
But, even with measuring air flow at mouth level (kind of gold standard for respiration) you sometimes see a suspicious respiratory cycle very different from other cycles but not artifact. These cycles either have very low amplitude (tidal volume) or are very short. I don't call them "abnormal". How to manage them depends on the context you are studying. For example, there are usually a period of apnea/hypopnea after a sigh. So, in case that you have sighs you may have few very shallow inspirations right after that. In case you do not expect large variability in respiratory parameters (time, tidal volume) screening for outliers (e.g. based on mean +- 2/3 SD) can help.
At the end, for accurate measure, you need to visually check the signal and correct the data. There is no optimal automatic method to do that (especially with the belt), to my knowledge. Any criteria you chose for excluding "abnormal" cycles make sure that you don't get biased toward your study hypothesis since the probability of those "abnormal" cycles may be affected by the study intervention.
I'm following this question to see if someone can introduce a guideline for this issue.
Good luck with your research
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I need a device for measuring the HRV and the respirationrate from both thorax and abdomen. The test persons are playing piano, so it has to be not very disturbing. I already saw the belts from Polar but they only use ECG. Thank you for any idea!
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You can compare participants if you first perform a calibration procedure using an instrument to directly measure tidal volume. There are different possibilities, and I would examine the inductive plethysmography literature. If you are serious about pursuing this line of research, I would suggest really delving into this literature.  I wrote a paper some years ago that might be a reasonable starting point: https://www.researchgate.net/publication/41531535_Accuracy_of_ventilatory_measurement_employing_ambulatory_inductive_plethysmography_during_tasks_of_everyday_life
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I am currently exploring in how far it is possible to use (combinations of) sensors to measure (electro magnetic) signals (aka EM-Biofield) to collect objective data which can be interpreted to reflect subjective mental states.
I am experimenting with HRV derived from Polar H10 and Kubios, but get stuck with the interpretation of parameters, Spectra and Poincare plots - eventually there is a clever way to combine this with meaningful EEG or other parameters.
Eventually it could make sense to also use tone of voice (FFT) t complement the assessment - which of course is only possible in an active mode - whereas the HRV / EEG would also work passive.
Any other parameters that could be used ? Like Skin conductance, skin temperature?
Thanks!
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Hi Thomas,
HRV parameters change in response to stress (and perhaps other mental states). For example RMSSD (a popular time domain index) decreases in response to stress. Also, you may observe decrease in PSD in various frequencies (HF and LF). The LF/HF ratio is mentioned as sympatho-vagal balance in some literature and as an index for monitoring mental state, but there is a huge controversy on that. Very important to consider that such changes in HRV are not specific enough. As a within-subject factor, HRV has significant diurnal variation, is sensitive to respiratory rate and amplitude, diet, etc. and therefore since these factors are not stable over time any change in HRV over time can not be specifically attributed to mental states, unless you can be sure that other factors are stable over the time of interest.Pay attention that available data are from experimental studies in laboratories and under very precise conditions, and by no means can be generalized to daily life. Data from daily life situation will come in near future hopefully.
Also, pay attention that different subjects have different cardiovascular response to mental stress. In some you see vagal withdrawal while in others you see an increase in vagal activity. So, there are various phenotypes. Therefore, you may not be able to use HRV as a specific between subject comparator for mental state, or you need a very large sample of subjects and several trait and state measures to find those phenotypes.
I don't have info about how you can combine EEG with HRV, nor on the voice. The electrodermal activity is popular in psychological studies for monitoring stress response, though. 
Good luck 
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We have hundreds of HRV datasets of medical staff. We want to create a predictive model of stress.
Is it important that HRV or IBI (Inter Beat Interval) data length should be equal for all datasets? I read, there are two types of HRV study i.e. long (24hour) and short (5min). My datasets are of variable length for example one contains 7132 IBI points, other one contains 5648 IBI points and so on.
I have calculated time domain measures such as SD, SE etc., Would this (variable dataset length) will significantly affect our results?
What should I do to minimize such issues?
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Unequal length is one baseline difference, SD and SE are other individual differences, and ipsative standardization eliminates variation attributable to the collective of baseline differences. It is not implausible that some people will have similar profiles. If the issue under investigation is what are the response patterns ("types"), and their antecedents and outcomes, then eliminating waking hours when stress originates and reactions (longitudinal cognitive, behavioral, and physiological) occur is going in the wrong direction. The papers that I linked address these issues.
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I have a total of 100 datasets that contain data of Inter Beat Interval (R-R Interval), for which I have calculated mean and standard deviation. This resulted in a range of standard deviation, I don't know how should I divide this range in stress score classes such as mild, moderate and severe?
Do I need to use some other measures?
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I think it is very difficult to stablish. You can review works about this topic, but it is quit variable.
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Dear colleagues,
It has been proposed that no single statistical measure can be used to assess the complexity of physiologic systems. Furthermore, it has been demonstrated that many entropy-based measures are "regularity" statistics, not direct indexes of physiologic "complexity". Finally, it has been stressed that increased/decreased irregularity does not imply increased/decreased physiologic complexity. However, it is common to keep finding interpretations such as, "a decreased/increased entropy value of beat-to-beat interval series (RR sequences) reflects a decreased/increased complexity of heart rate variability", and even more as "this reflects a decreased/increased complexity of the cardiovascular control". So, which entropy-based measures actually quantifies time series complexity? Moreover, is it appropriate to interpret that because of a decreased/increased complexity in heart rate variability there is a decreased/increased complexity of the cardiovascular control?
Thanks in advance,
Claudia
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Dear Claudia,
Sorry for posting into your topic belatedly. However, this is a quite interesting topic and I am also looking for deepen the understanding. Here is my humble opinion.
The theory of physiologic complexity, as you stated, is supported by the idea that healthy systems will always be the most complex ones, because the multilevel interactions (crosstalks) and regulating mechanisms are at best performance in such organisms/systems. Any breakdown or function loss in one or more of these regulatory mechanisms causes a decrease in the complexity. Therefore, diseases and aging is associated to complexity loss.
Some studies revealed that entropy can either increase or decrease in pathological situations. Considering the idea of physiologic complexity, entropy cannot be assumed as a complexity index. Moreover, surrogate data tends to increase entropy (compared to original signal) but the original signal is considered more complex than its surrogate, as it contains the information which was destroyed in surrogate data.
So, why some authors still associate entropy to complexity? Because complexity can be interpreted in different ways. If you consider that the complexity of the system can be solely characterized by the signal dynamics you are analyzing, and not by the system itself, you may assume that the higher unpredictable the signal, the higher the complexity. In this case, entropy can be considered a measure of complexity. This is often called "dynamical complexity".
If we refer to proper books and papers, we will find that "complexity" does not have an universal definition. Instead, people describe what properties the complex systems usually show, which basically are:
1) Many interdependent elements, interacting each other through nonlinear rules;
2) Structures over several scales;
3) Emergent behavior;
When we expand it to living organisms, this is clear that our systems (humans, animals) fit as complex systems. But, how could we detect this complexity from time series? From the many measurements proposed to extract information from those time series, e.g. asymmetry, fractals, entropies. And how many and which information are necessary to characterize complexity in time series? I think this is a very good question!
The final point is: can only heart rate variability reflect the system complexity? Although powerful, this is a single variable taken to characterize a very complex organism. It is more or less the same of reducing the dimension used to characterize some object in space. Information will be lost. Multivariate analysis may be more powerful, taking into account more than one variable concomitantly to assess the dynamics of the system. For example, one can record respiration, arterial pressure, ECG and EEG, simultaneously, and use some methodology to characterize the complexity. However, due to several limitations, in many situations it is not possible to collect more than the ECG, from which we can calculate heart rate variability.
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I have been trying to extract data from XML files (originally exported from GE ECG device) for heart rate variability analysis. However, I have yet become successful in doing that. Is there anyone that can help me on this issue? 
Thanks.
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