Heart Rate - Science topic

From the SDK docs provided by Polar: "Electrocardiography (ECG) data in µV with sample rate 130Hz" Source: https://github.com/polarofficial/polar-ble-sdk

Ivabradine affects heart rate primarily by selectively inhibiting the If (funny current) channels in the sinoatrial node of the heart. These channels play a crucial role in regulating the heart's pacemaker activity by allowing the influx of sodium and potassium ions during the diastolic phase, which contributes to the spontaneous depolarization of cardiac pacemaker cells.

By blocking the If channels, ivabradine slows down the rate at which these pacemaker cells depolarize, effectively prolonging the duration of the diastolic phase of the cardiac cycle. This action leads to a reduction in heart rate without directly affecting myocardial contractility or the force of cardiac contraction.

In contrast to beta-blockers, which exert their effects on heart rate and contractility by blocking beta-adrenergic receptors, ivabradine specifically targets the If channels in the sinoatrial node, making it a selective and direct modulator of heart rate. This mechanism of action allows ivabradine to lower heart rate without compromising cardiac function, making it particularly useful in conditions such as heart failure and angina where controlling heart rate is beneficial.

Yes!?

Jochen Wilhelm wrote: If the course over time is relevant, then I'd expect some time-course analysis, that is, some kind of regression models, not ANOVAs or t-tests.

I agree. In that case, I think you would want a multilevel model, with horses as level 2 variables and occasions of measurement at level 1 (i.e., clustered within horses). Here are a couple of places where you might find some helpful resources:

Q. How many cardiovascular features will be recorded? And are they all considered equally important? Or do you have a small number of primary outcomes (1 or 2) with the others being considered secondary?

What I am getting at with that question is the so-called multiplicity problem that arises when you have multiple outcomes. Two of my favourite articles on multiplicity are these 2005 Lancet articles by Schulz & Grimes:

Finally, your situation reminds me of the attached excerpt from Frank Harrell's well-known book on regression models. He described a two-group clinical trial with several DVs measured on just one occasion. And in that scenario, he suggested binary logistic regression with group (treatment vs control) as the outcome variable and the multiple DVs (as originally conceived) as the explanatory variables. In your case, given the repeated measurements every 10 minutes, you would have to use a multilevel logistic regression model to account for the clustering of the repeated observations within horses.

Please bear in mind that I have only been thinking about this for 10 minutes, and I never heard Harrell or anyone else extend his suggestion to this multilevel scenario. But on the face of it, it makes sense to me. ;-)

Here are some resources and examples that might help you understand and implement longitudinal analysis with mixed-effects models:

"Data Analysis Using Regression and Multilevel/Hierarchical Models" by Andrew Gelman and Jennifer Hill.

"Mixed-Effects Models in S and S-PLUS" by José C. Pinheiro and Douglas M. Bates.

Longitudinal analysis using mixed-effects models is a statistical approach commonly used to analyze repeated measurements taken on the same subjects over time. Mixed-effects models are also known as hierarchical linear models or multilevel models. Here are some key notes on longitudinal analysis with mixed-effects models:

Longitudinal Data: Data collected from the same subjects at multiple time points.

Mixed-Effects Models: Incorporate fixed effects (population-level effects) and random effects (subject-specific effects).

1.Fixed Effects: Correspond to population-level parameters that are assumed to be constant across all subjects.

Examples include treatment effects, time effects, and covariates.

2.Random Effects: Capture subject-specific variability. Modeled to account for individual differences and correlations within the same subject over time.

Random intercepts for subjects and, if needed, random slopes for time-related variables.

3.Time Variable: Represents the repeated nature of measurements over time.

Allows for modeling the change in outcomes over the course of the study.

1. Model Flexibility: Accounts for individual variability in trajectories over time.

Accommodates unbalanced and missing data.

2. Increased Power: More efficient than traditional repeated measures ANOVA when dealing with unbalanced designs.

3.Handling Correlation: Explicitly models and accounts for correlation among repeated measures within subjects.

1.Normality: Assumes that the residuals are normally distributed.

2.Linearity: Assumes a linear relationship between predictors and the response variable.

3.Independence of Random Effects: Assumes that random effects are independent of predictors.

Fixed Effects: Interpret similarly to standard regression coefficients.

Random Effects: Variability in intercepts (and slopes) reflects subject-specific deviations. Software:

Popular software for fitting mixed-effects models includes R (lme4 package), SAS (PROC MIXED), and Python (statsmodels, mixedlm).

Likelihood Ratio Test: Used for comparing models with and without specific fixed or random effects.

Residual Analysis: Assess the model fit by examining residuals.

Variance Inflation Factors (VIF): Check for multicollinearity among predictors. Considerations:

Sample Size: Adequate sample size is crucial, especially when estimating random effects.

Model Complexity: Avoid overfitting by carefully selecting fixed and random effects. Reporting:Clearly report fixed effects estimates, random effects variances, and any interactions.

Consider incorporating time-related variables (linear, quadratic) to capture trends over time.

Use appropriate methods for handling missing data, such as multiple imputation or maximum likelihood estimation.

Mixed-effects models can be extended to handle non-normal outcomes through appropriate link functions (e.g., logistic for binary outcomes).

Objective: Investigate the effectiveness of a new drug over time.

Data: Measure the health outcome (e.g., blood pressure) at multiple time points for each patient receiving either the new drug or a placebo.

Model: A mixed-effects model can be used to examine how the drug affects the overall trend in health outcomes while accounting for individual variability.

Objective: Explore individual learning trajectories in a longitudinal study.

Data: Assess students' academic performance (e.g., test scores) at different time points throughout their educational journey.

Model: A mixed-effects model can be employed to model how students' learning trajectories vary across individuals and how they are influenced by fixed factors like teaching methods.

Objective: Investigate how mood changes over the course of a psychotherapy intervention.

Data: Collect mood ratings from participants at multiple time points before, during, and after a therapeutic intervention.

Model: A mixed-effects model can be used to examine the overall trend in mood changes across participants, while considering the individual variations in response to therapy.

Objective: Analyze income growth trajectories over a period of years for individuals in a population.

Data: Gather annual income data for a sample of individuals over several years.

Model: A mixed-effects model can help identify common trends in income growth while accounting for individual-specific factors that may influence income trajectories.

Objective: Study changes in biodiversity over time in a specific ecosystem.

Data: Collect ecological measurements (e.g., species abundance) at multiple time points in the same location.

Model: A mixed-effects model can be used to assess how ecological factors contribute to changes in biodiversity while accounting for site-specific variations.

Objective: Evaluate the performance improvement of athletes over a training period.

Data: Record performance metrics (e.g., running times, strength measurements) at regular intervals during a training program.

Model: A mixed-effects model can help identify overall trends in performance improvement while considering individual athlete variations.

HeartMath recommended.

Heart rate alone is not useful. There is a ring, called Aura, that measures heart rate variability (HRV). While that is a marginally valuable parameter, it is a start. That part of the nervous system is called "Autonomic", not autonomous.

This site is the UK research integrity office and it has advice for people in your situation, whistleblowers etc..

U use national physical fitness awards

REM sleep ia about 1/4 time of NREM sleep. HR , BP RR are usually increased during REM sleep is due to increased Noradrenaline level .Most of dreams usually comes in REM sleep g

Gives the sense of daily Birth

MATLAB soft ware is good option .other software app is also available

Hi Manon, maybe the Nowatch is also an option? It includes heart rate and skin conductance, and most of the raw data is available for the user: https://nowatch.com/

Best, Peter

What are you looking for when you expose the participant to sound? Is it a loud sound? A pleasant sound?

One thing that may be important is whether the subject is taking any medication that affects heart rate or vagal tone.

Hi! Assessing emotional arousal is possible with EDA through peak detection, while heart rate variability could provide some useful information from PPG recordings. If these are compared against a control condition, then you should be able to see which is related to increased emotional arousal. Determining sense of presence can be done by simply asking participants (e.g. as in this article: or ), although it may be possible to corroborate that with biosensor data. The questionairres typically used are from here: or here: although others may exist as well). I've also written a bit about this in a blog post, in case that's of interest: https://imotions.com/blog/insights/research-insights/science-video-games/ I hope that helps!

Thank you

yes I already used the polar v800

I am asking if there is more accruate and most recent one.

doi: 10.2196/18694.. 2020 Sep 8;8(9):e18694.JMIR Mhealth Uhealth

Reliability and Validity of Commercially Available Wearable Devices for Measuring Steps, Energy Expenditure, and Heart Rate: Systematic Review

Daniel Fuller # 1 2 3, Emily Colwell # 1, Jonathan Low # 1, Kassia Orychock 1, Melissa Ann Tobin 4, Bo Simango 1, Richard Buote 4, Desiree Van Heerden 5, Hui Luan 6, Kimberley Cullen 1 3, Logan Slade 4, Nathan G A Taylor 7

Affiliations expand

Jawac device to measure mandibular movements. Read the Bassam Chakar's article.

The Checkme Pro Health Monitor™ (Viatom Technology, Shenzhen, People’s Republic of China) is a newly released Conformité Européene (CE)-approved smart all-in-one device, which measures four of the five MEWS vital signs in less than 25 seconds.

There are a lot of factors other than baby size involved here. Preterm deliveries can be, for example, because of pre-eclampsia, and so the whole haemodynamics involved are different. Other causes of preterm delivery likewise – a preterm delivery isn't a normal delivery that happened early, it's a pregnancy that ended unusually.

Heart rate is also only a proxy for what is going on with the haemodynamics. Cardiac output and peripheral resistance are probably more important in pregnancy than heart rate. Less so blood pressure, as the same pressure can be generated by low output + high resistance and by high output + low resistance.

Hi, I just located a good reference to the work of Jan Penaz, togethet with Burianek. This paper is simply accessible on the Net:

Peňáz, J., Buriánek, P., 1963. Zeitverlauf und Dynamik der durch Atmung ausgelösten Kreislaufänderungen beim Menschen. Pflüger's Archiv für die gesamte Physiologie des Menschen und der Tiere 276, 618-635. DOI: 10.1007/BF00363567

Yes, in German this one, but that is Pflúgers for you.

They did experiments with breath hold and non-invasive blood pressure recording (remember that Jan Penaz later invented and he and Karel Wesseling together developed what has become famous as the Finapres). The pictures are very clear, even if you don't understand German.

They convincingly show that there are underlying strong blood pressure oscillations at a rate of about 0.1 Hz during breath hold.

This should answer a lot of questions,

John M. Karemaker

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.

What is your hypothesis?

Having recorded HR four times, you could be interested in a number of kinetic features : maximum HR, time to maximum HR, time for HR to return to normal, or area under the HR curve for example.

So without knowing the hypothesis, I would be guessing in the dark if I suggested an analytic strategy.

Try this code.I am unable to paste the coding here. So I am sending you a screenshot

If the data collection and measurement is well done, you do not have to do "cons" since the two variables that you cite are usual for such measurement and make up, together with others, those that a POLYGRAPH OFFERS ... perhaps you could use more variables such as Psychogalvanic Response, Arterial and muscular Tension, etc.

Thom Rieck Yes, exactly. They will be pedalling on a stationary bike. The only thing is that we don't usually work with iOS so we don't have the iPad. That is why I was wondering if it could be ran on Windows/Android. I contacted yesterday Polar and Firstbeat to see if I can have somebody on the phone to answer my questions. I will check the Garmin and Zephry proposals as well. Thanks a lot!!

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

Hi,

Open datasets include some particular pathophysiological conditions such as CHF and some cardiac arrhythmias as you saw. However, the nightmare is an uncontrolled situation. Maybe you can reach sleep labs directly for sleep heartbeat data.

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.

Dear Fatemeh Meschi,

Tachycardia is the rule in hemorrhagic shock, as a compensatory mechanisms. Do initialy there was increase heart rate. But as more bleeding occured it compressed the abdominal viscera. There can be decrease heart rate during shock, when it occurs with intra abdominal bleeding. The mechanism is: pertonial blood triggers parasympathetic activity, as abdominal viscera contains vagal receptors predominately over sympathetic receptors.

Ref:BMJ 2004:328:45

http//doi.org/10.1136/bmj.328.7437.451

As mentioned above, PPG is the best way. Paste PPG sensor/device on any finger, lower or upper limb. Then calibrate it , if required.

Please visit the following links below: it relevant to your question:--->

Hi Shreya.

Just wondering if you managed to fix the issue. If not, did you try any alternatives?

Thanks.

Manik

Patients fitted with total artificial hearts have their hearts explanted and completely replaced by mechanical contraptions that perfuse the body. This was usually undertaken as a "bridge" to keep patients alive and in relative good condition while waiting for an allogenic heart transplant.

Medical practice has currently migrated away from "total" heart replacements and towards pumps that are fitted inside the heart and take over the task of pushing blood. Interestingly, these pumps work continuously and thus the patient has no pulse! Despite this, these pumps really improve patient status dramatically!

Pushing blood without a pulse does appear to have some unforeseen drawbacks; carotid baroreceptor rely on pulses to monitor arterial pressure and thus "read" lower pressures, unnecessarily stimulating the sympathetic nervous system. The lack of pulse could also lead to more gastrointestinal bleeding in these patients, through hitherto unforeseen mechanisms.

As for total cardiac explantation and fitment of artificial hearts, Lotfali Bolboli is indeed correct. Heart produces several hormones, like BNP. BNP levels rapidly drop to almost zero after explantation and BNP supplementation is necessary to protect patients from rapid renal failure (10.1016/j.jtcvs.2003.07.023).

All in all, there's nothing to indicate that there exist insurmountable obstacles to replacing the heart with something else that provides "stable circulation flow", but the challenges are great and have currently led medical science to gravitate towards devices that help patients' biological hearts!

I hope that helped.

The clinical significance of the selected parameters is related to the initial condition of the patient and the disease. Any sustained positive dynamics should be considered clinically significant.

Polar chest strap should do the job. It comes with its software or connects to a polar watch which can export data as a csv.

Another one is Whoop 3.0. Provides 100 samples per second continuous measurements and many have now been able to export data

May be:

In hereditary neuropathy, "uniform conduction block" was once thought to be a characteristic in contrast to "non-uniform conduction block". Uniform conduction block is characterized by conduction block without any temporal dispersion. Now this is only seen CMT 1A hereditary neuropathy. Non-uniform conduction block is seen in other hereditary neuropathies and acquired demyelinating neuropathy. In the recent years, uniform conduction block is also observed in "axonal form of GBS" (AMAN and AMSAN), Some called this "axonal conduction block". This is misnomer. This is discussed in my recent paper in Muscle Nerve in 2021 Feb issue.

I am no expert in penguins. However, heart rate is directly correlated to gas exchange. When they slow their heart-rate - their body uses less oxygen/per minute, meaning they can survive on their gas reserve (in the lungs) for much longer. This, in turn, lowers the amount of CO2 build-up (toxic) in the body.

It seems to me it may be a method for them to stay underwater for longer periods!

If I understand correctly, you are trying to confirm whether or not the factors you believe are important in determining a certain outcome (e.g. hypertension/heart rate --> cardiovascular disease) do actually impact said outcome (in your dataset).

Now, if you would like to do this for each variable in and of itself (E.g. Hypertension alone or elevated heart rates alone), then you could simply calculate a measure of association for each of these variables and see how significant the association is.

The question of which measure of association to use depends on the type of data you have. For instance, if you have a binary risk factor (E.g. Hypertensive/Non-hypertensive) and a binary outcome (E.g. Cardiovascular disease/No CVD), then you can simply calculate the odds ratio (or the risk ratio, with the latter having the advantage of being more easily interpretable).

Other types of combinations (E.g. Binary risk factors and continuous outcomes) may be assessed using different measures of association, all depending on the type of data you have.

Now, if you'd like to input several risk factors into a single model, then the choice of which factors are the most important (and thus deserve inclusion in the model) can be determined by a number of approaches . As Ángel Herráiz-Adillo mentioned above, one approach would be to assess each variable in and of itself, calculate a p-value for each, and then include those variables having a significant p-value, but as mentioned, this has been criticized as being a somewhat inappropriate method.

A more holistic approach (E.g. Does it make sense, in terms of pathophysiology (for instance), to include this particular factor as a predictor of this particular disease? That decision can be based on your understanding & reading of previous literature) has been advocated.

However you choose to construct your model (there are a number of methods (e.g. forward or backward stepwise regression) that you can read about, and a number of pitfalls/caveats to avoid), you'll finally arrive at a model containing a number of variables, each of which has a certain measure of association. (Again, the measure of association depends on the type of data you have)

In that model, some variables may have a stronger association than others (For instance, the odds ratio for blood pressure might be 3.4, whereas for heart rate it might be 2.1, which may lead us to conclude that BP is a more important variable than heart rate). It's obviously not that simple as there are a number of pitfalls during model-building that can cause you to underestimate each variable's contribution (Collinearity being one example), so I would recommend that you take a bit of time to read a little bit about these pitfalls.

thank you! you have been very helpful with your answer I will explore the options you mentioned!

Aalyaa Anwar:

Dear Colegue, please I recomend to read my article on the American Hear Journal

Iosa D, DeQuattro V, Lee DD, Elkayam U, Palmero H.Am Heart J. 1989 Apr;117(4):882-7. doi: 10.1016/0002-8703(89)90627-3.PMID: 2494874

As well you may read my article on the WHO, pub 52, Chagas disease and Cardiovasclar Dysautonomia I am an expert on Cold Blood Test. With pleasure Dr Daniel Iosa, Editor International Journal of the International College of Angiology USA

Patients on hemodialysis have increased viscosity, that is multifactorial and can contribute to increased cardiovascular mortality in this group of patients with end stage renal disease (1).

Blood flow rate (BFR) through dialysis machine is one of the parameter of effective hemodialysis, and BFR <250 ml/min compared with >250 ml/min is associated with less effective dialysis, tissue ischemia, particularly the heart, and can lead to increased vascular events (2).

Dialysis is considered one of the strongest antihypertensive in patients who are undergoing hemodialysis. This why in hypertensive patients, antihypertensive drugs on the day of dialysis should not be given before dialysis session, because patients often develop hypotension during dialysis. Associated with hypotension, most of such patients develop tachycardia, but others have no effect, while a few may develop bradycardia(3). Hypotension is particularly more common in patients with ischemic heart disease and patients with diabetic complications (particularly autonomic neuropathy) with renal failure (4)

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Where is the "crossover"?

You should consider the correlation of measrements between phases of the same patient.

Have you thought about a resonable distributionmodelfor the heart rates? I would think that a Gamma model would possibly be more appropriate than a normal model.

You might think if it makes sense just to analyze the integral of the heart rates as a measure for the total blood low.

There are surely many more things to think about. I strongly suggest meeting with a local statistician (possible via web-conference if you cannot meet personally) and discuss your project.

I don't see a reason why you could drop the sphericity assumption. Greenhouse-Geisser is a typical solution, and some authors recommend to use it anyways, because you cannot rely on the results from Mauchly test (e.g. Maxwell & Delaney,

2004).

Other alternatives could be an MANOVA approach to repeated measures (Tabachnick & Fidell, 2007) or multilevel modeling. Both have pros and cons, but the latter is apparently the most flexible tool to incorporate different variance-covariance structures.

Mutwedu BWANA Valence

you are welcome

It could be the model or the background or the mouse you're using. Either way interesting.

Check your vaporizer to make sure it's calibrated. Different vaporizers and scavenging systems can yield slightly different results. I use 2.5% for 20-30sec. and then immediately reduce to 1.5% for a given procedure. 1% is acceptable as well but I would go no lower than that. 1-1.5% gives me a nice even plane of anesthesia. I generally see heart rates in the 450-550 range at 1.5% and 500-600 at 1%. I've never seen less than 400 with Iso at these settings. Make sure you're maintaining body temp as well and if your model is not in a pure background that is less than ideal.

Time permitting, it might be worth doing your exam twice. Once at a set Iso concentration and then again trying to match heart rates. Expect the heart rate to fluctuate - it comes with the territory for a myriad of reasons. Good luck.

you can find such data in physionet :

I agree with Natalia Kuzmenko

The heart rate and intensity of exercise are directly related: the more exercise, the higher the heart rate. When exercising as hard as possible, the heart rate will reach the maximum (HRmax)

Hi Alexandra, I think that my latest review paper could be useful. If you want, you can download a free copy of the article at this link: https://authors.elsevier.com/a/1asw3xsQaBzwg

Best regards,

Gloria

hi Sébastien,

With these miniature implantable temperature loggers, you can have the laboratory animal body temperature recorded to detect very small temperature changes, up to 25 milli Celsius, after a stressor is applied to the animal via vaccine, medication in the food or solution sprayed in the environment. The download of the data is done while the WeeDip is implanted in the animal by wireless communication with a antenna.

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

Please refer to the following article for this:

MATLAB code provided in MathWorks File Exchange:

Respiratory rate estimation from ECG and/or PPG:

https://in.mathworks.com/matlabcentral/fileexchange/55289-respiratory-rate-estimation

Follow a series of publications on the estimation of respiratory rate from ECG/PPG:

http://peterhcharlton.github.io/RRest/publications.html

Hope this will help to implement your idea.

Absolutely I will use such device

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

You can measure cortizon level in saliva.

Thanks you very much Hassan Sadeghi will have a look!

Dear Debanjan Borthakur,

it is important to consider the respiratory frequency for the proper interpretation of the results HRV parameters. Especially during slow breathing thing get different than expected during normal HRV assessments. High frequency (HF) oscillations of HRV are defined as the amount of oscillation in the frequency range 0.15 to 0.4 Hz, i.e. 9 to 24 breathing cylces per minute. During slow breathing the respiratory frequency may also be slower than 9 cycles per minute (e.g. 6 to 8 cycles per minute). Hence, the effect of slow breathing on heart rate modulations would then be quantified by the low frequency (HF) band of HRV (0.04 to 0.15 Hz, i.e. 2.4 to 9 breathing cycles per minute). In the case of slow breathing it does not make much sense to calculate LF/HF because it cannot be interpreted in the sense of a sympathovagal balance: LF is high caused by the vagal modulations at slow breathing whereas HF is lower because it does not capture respiratory influences anymore.

It is alway a good idea to know the respiratory frequency at least approximately because it helps to understand parameters like LF and HF and its proper physiological interpretation.

Whether it is helpful to remove the respiratory component - I don't know yet because I didn't try it myself yet. But I think it is the same reasoning as before: if you know the (approximate) respiratory frequency it is helpful for the proper interpretation of the results because you then know what information has been cancelled out during the process of HRV calculations.

Articles pointing at the impoartance of respiratory frequency for the proper interpretation of HRV parameters:

Articles also dealing with respirtory influences on HRV:

All the best,

Dirk

Thank you very much for your response!

I want to appreciate the three scholars for their contribution to my question.I want to say that your answer is in order.off-course internet of Things play a major role in advancement of electronics in our present age. Indeed ,there is an interconnection between IoT instrument based and our physical world. Thank you,Monir, puput Dani and Gomathy

Your intended use of the data is unethical, and would represent a severe violation of the patients' privacy. This question should be banned from this platform.

You can see also our work on the E4 validation:

Our results are slightly different from those of Matthijs Noordzij as we found no visual ressemblance between wrist and finger SC, and we found accurate HRV metrics compared to ECG only in motion- and stress-free conditions. Having an adaptation period (e.g., 1 min) before the recording intervals of interest seems to increase the measurement accuracy.

M ANOVA is the choice dear, please check https://www.technologynetworks.com/informatics/articles/one-way-vs-two-way-anova-definition-differences-assumptions-and-hypotheses-306553

Probably

Hi there,

As a person who teaches theoretical and clinical anatomy, I can recommend the following articles:

Articles alone, however, will not be sufficient. For concise, correct and optimal results in your research efforts, check out:

https://www.thieme.com/books-main/anatomy/product/3581-atlas-of-anatomy

https://www.thieme.com/books-main/physiology/product/3599-principles-of-medical-physiology-2-e

or alternatively:

https://www.thieme.com/books-main/physiology/product/1998-color-atlas-of-physiology

I hope that helps :)

Hi,

It is mighty difficult to measure levels of stress without recourse to bio-markers, matched by an array of self-reporting and administered questionnaires.

They are both pricey and time consuming, but thus far, the best shot at getting objective research results.

Cardio-vascular physiology can be influenced by other factors such as inborn genetic factors (e.g. frequent anomalies in the macro-anatomy of the vascular system) and acquired ones such as disease (e.g. thyroid gland-related, diabetes), smoking, diet, dynamic function of the gastro-intestinal system, etc.

If you still insist though, you can have a look at the following:

http://www.jorgegoncalves.com/docs/imwut19.pdf

Leading in this area of research are the Japanese scientists - you can check there for additional data.

I hope that helps :)

Thank you, Roberta. I will try it.

Valence, Arousal and a basic emotion label. Just with two dimensions you can not discriminate hunger from fear.

An optimal filter for short photoplethysmogram signals (https://www.nature.com/articles/sdata201876) with Matlab source code.

There is no difference between the number of PP and RR intervals to perform short-term HRV analysis. Both types of information represent essentially the same process - time intervals between heart contractions. As to the number of intervals needed for the analysis, it depends on what HRV parameters you want to calculate. For frequency-domain analysis, it would be more accurate to determine the length of recording in seconds rather than the number of intervals due to the nature of spectral analysis (assumed that FFT analysis will be used). It should be enough to record PP intervals for 5 minutes to estimate HF and LF. But it would be better to record intervals for 10 or more minutes to reliably estimate VLF.

Dear Shreya,

I think what do you want is a wireless sensor node that contains multiple sensors that can sense all the required parameters at a regular time, then acquiring these signal processing them and sending them to an access point which may be a laptop.

As an example you can see the work in the link: https://www.omicsonline.org/open-access/realtime-health-monitoring-system-on-wireless-sensor-network-2277-1891-1000126.php?aid=12892

The paper in the link may be also useful:

Best wishes

I don't think a single sensor can measure all these parameters

Hi dear Lori

I did not research on this topic but I searched for your question and I can introduce this article to you:

I hope you find it helpful