Science method

Electrocardiography - Science method

Recording of the moment-to-moment electromotive forces of the HEART as projected onto various sites on the body's surface, delineated as a scalar function of time. The recording is monitored by a tracing on slow moving chart paper or by observing it on a cardioscope, which is a CATHODE RAY TUBE DISPLAY.
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Need ECG image dataset for stroke prediction. Any public access database?
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Hello Sourav,
thank you for your response. I might then suggest to maybe first check this article: Deep Neural Networks Can Predict New-Onset Atrial Fibrillation From the 12-Lead ECG and Help Identify Those at Risk of Atrial Fibrillation–Related Stroke; as more than 25% of all strokes are deemed a result of AF, and ≈20% of strokes caused by AF occur in individuals not previously diagnosed with AF. That might be interesting article to read presenting comparison of patients at high risk for new-onset atrial fibrillation and no history of AF predicting future AF and stroke using deep learning model.
other useful article might be: Electrocardiographic abnormalities in acute cerebrovascular events in patients with/without cardiovascular disease.
this is review of 361 patients with results of the most common ECG abnormalities associated with stroke being T-wave abnormalities, prolonged QTc interval and arrhythmias.
You might need to read more and then separate patients with/without cardiovascular disease
Hope this would help
Filip
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Hello everyone!
I have an ECG signal sampled at 500 Hz and, from that, I would like to compute the RR interval and then Welch's PSD. All this aims to understand the sympathetic activation of the person.
I wanted to enhance the R peaks using the 'sym4' wavelet. However, I have some difficulties in understanding how to choose the proper level of decomposition.
Can someone help me with this topic? Is there a "standard" way to assess the proper level of decomposition?
Thank you in advance for your time!
Luca
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There is an equation that can be used to determine the maximum level of decomposition.
Max Level = fix(length_of_the_signal/(length_of_the_filter-1))
Or you can simply use the inbuilt matlab function "wmaxlev(sizeX,wname)" to determine the level.
If you want to understand more about the working of the function you can always check the source code by typing the command "open('wmaxlev')"
Hope that works for you!
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I need a brief MATLAB code
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plot(xx)
xx is your one-dimensional time series
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Whether Powerline interference persist even after use of band filter of 0.5-45 Hz or its useless to apply notch filtering?
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I would make sure your 45 Hz filter is acting upon the 50Hz component. If the filter is of low order, the attenuation at 50Hz may be neglectable. In this case, moving the filter to 35Hz may help, as suggested or you may need a notch filter with a high Q.
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hi my thesis is about detection of myocardial infarction from ECG signals and i want to know is there any database for it?
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hi,
i hope you are fine. is there any research or literature review available on the comparative analysis of different ensemble method used for misdiagnosis of cancer patient against xgboost method?
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Various file formats coming along when recording ECG's with different stationary or mobile devices. txt-files are more uncommon.
Therefore I'm looking for a high-quality ECG viewer, which also can import txt-files with varying sample rates, detects abnormal beats like sVES, VES , technical artifacts, etc. and extract R-R-intervals.
Is there any software for free available?
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I agree with Thomas Gronwald. I will also recommend Kubios software. The free version (known as Kubios Standard) provides basic analysis functionality which may suffice for most researchers. A comparison of basic and free versions is available here: https://www.kubios.com/hrv-standard/
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I have readings of ECG taken from rats and I would like to analyze them, but the quality of the ECG is as attached. I knew about a software called labchart8, but I do not have a license for it. Can any one advise me how to interpret those ECGs in my case?
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Comparison of ECG in rats and humans is highlighted in the attached document :
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Dear all,
I am a new bee to the area of ECG analysis and I am doing research on the mentioned dataset. I want to know how can I find the beats which are the onsets of arrhythmia?
Any help would be appreciated.
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This dataset include preliminary set of beat annotation ( all beat marked as normal ) with additional annotation that includes ventricular fibrillation/flutter. We can take help of http:/physionet.org/physiobank/database/cudb/
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Hi,
I was working on a thesis project on a portable ECG device, I checked these
So it was basically biopotential->Amplifier->logging->displaying using Teensy(Programmable chip), Adafruit(Bluetooth Module), SPI LCD Screen, and resistors and capacitors.
I developed and assembled all except Teensy, adafruit, SPI LCD screen, as I don't have circuit on how to connect output to these 3 devices.
Can anyone share some other resources it would be very much valuable.
Thank You
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Dear sir, I guess the Teensy is used to acquire the signal (use one of the analog inputs; for instance, pins for 14 to 23 Analog). The signal is of course converted (Analog to Digital) inside the Teensy. Then, you can display what you read using SPI (pins 10 to 13 of Teens) on SPI LCD Screen. For the Bluetooth device, I don't know what is it used for; maybe to save the data on an SD card and send the data (acquired ECG) to a remote station (computer I guess). You can use it. It is not difficult to assemble that and programs exist for each task: 1. Acquire analog signal using Teensy; 2. Display a value, figure using SPI of Teensy on an SPI LDC display; 3. Send data using a Bluetooth device.
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I have decomposed the ECG signal using the EMD function in MATLAB and it shown a figure window with 7 IMFs, but I need to separate the useful IMFs from the decomposed signal. Can anyone help me in this issue?
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helo ,
you can calculate the correlation coefficient between the original signal and the signal from each IMFs and study the ion or the comparaision in ordre to deduce the useful IMFs.
best regards
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I am trying to acquire ECG data. NI myDAQ has two analog input port. How should I place ECG electrodes to acquire ECG data
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Thanks Amandeep. The coment from Ashfaq is correct. The accuracy is low in the 2 lead conformation and only really useful for rhythm (think Apple Watch). The leads can be anywhere on the body but would be better placed spanning the heart.
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Hello,
I am in need of ECG and EMG data collected for the same person for to create ML models?
Could you provide pointers to that data collected for set of people.
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Dear Kumar ,
There are many ECG and EMG repository are available online openly. some of the link listed
Also you can directly contact and request to the corresponding author of journal related to your work.
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hi my thesis is about myocardial infarction detection. do you know how to import entire database for simulation of research papers?
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Hello!
I am also working on this database (PTB ECG database v 1.0.0)
when i download the database from here:
the file's extension is .dat and i can't open this file in Matlab because it gives me like this:
Error using load
Unknown text on line number 1 of ASCII file
s0484_re.dat
"�����\�".
Error in signal2image (line 8)
load('s0484_re.dat');
how can i open this file correctly?
Any help would be appreciated
📷
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Hi everyone, Have you every used a smartwatch to measure ECG in one of your studies? What is your experience? Any suggestions for the brand? I would like to use such measure mainly as a manipulation check not primary outcome. Thanks in advance.
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Hello Dr Martina D'Agostini This message is with regards to use of smartwatch for ECG recording. While I don't think there is a reliable and scientifically validated smartwatch for ECG measurement purpose, I will recommend use of photoplethysmography signal for the measurement of Interbeat intervals. In case you plan to analyse interbeat interval data for calculation of heart rate variability, I will recommend that you go for Polar devices. These devices are robust and have been validated in multiple scientific studies for the purpose of interbeat interval data analysis. You may find more information here: https://www.polar.com/us-en/products/fitness-trackers-and-watches
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2017 ACDC CMR dataset is widely used in segmentation, classification and other CV studies. And to my knowledge, there were many other open cardiovascular imaging dataset(including ECG, CT, MR) but I can't find their exact name and get access to their website. Can anyone help? thanks!
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Besides the ACDC 2017 dataset we can use LVSC ( Left ventricular segment challenge) 2009 Dataset . it will be usefull Dataset
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As compared to 12L ECG, How accurate (Sensitivity and Specificity) are single and six Lead portable or handheld ECG devices for LVH, STEMI, and NSTEMI detection?
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Fluke PS 410 is used to generate various types of ECG. How can we generate ECG signal using Fluke PS 410?
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ECG signal processing
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Yes, preprocessing steps changes the raw ECG signal upto some extent but this is mainly depends on the opted signal processing methods. Few methods do elimination of entire noisy components in the signal which causes huge data loss while few methods perform Denoising operation with the objective of minimal dataloss.
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72 old man suffering from acute MI but the cardiologist diagnosed him with ischemic heart disease although the ECG changes and serum troponin reach 0.5 ng/ml ... so not give any drug except aspirin 100 mg/daily?
after 48 hr the patient died?
what is the scientific opinion about that?
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My initial thought is: has consideration given to another cause of death?
When people have MI, they may often be quite unwell for lots of other reasons. They are made more unwell by MI then made even worse by another reason on top of that.
My experience is that these patients might be acutely exacerbated by other events - such as, for example, a pulmonary embolism.
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Dear all colleague,
We now working with SDDB ECG record https://physionet.org/content/sddb/1.0.0/. When we do a preliminary literature study and dataset assessment, we found that each record has Baseline Wander happen. But unfortunately, we cannot determine is this a true Baseline Wander or this is happen naturally from the heart, since we not found any kind of pre-processing for SDDB records, except there're signal segmentation for Sudden Death classification research.
If this phenomenon are a baseline wander, what is the best practice baseline wander for this record? Here i put the picture of SDDB signal segment below from Physionet.
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The Holter electronics would have to be designed to set the high bandpass filter to >10-30 Hz. Baseline wander is nothing more than a low frequency signal and the monitor has no clue that it is not a physiologic signal. ECG recorders allow baseline wander to be displayed because some of the components of the ECG are low frequency, like the T wave. Therefore, most ECG recording devices use a fairly low high-band-pass filter, around 0.15 Hz.
It depends on what you are looking for as to whether you can get away with raising the high-band-pass filter. Intracardiac signals recorded during an electrophysiology study typically are filtered 30 Hz to 500 Hz, in which case there will be no baseline wander. The recorded signals, however, will have only high-frequency characteristics, so they will look very sharp and spikey. If that doesn't matter to you, that's your answer
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I am working on Isoproterenol induced myocardial infarction model
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Yes, Dear Alok Kumar Bharti. YES ECG is specifically reliable when there are typical changes in serial ECG. So doing ECH IN serial is of importance.
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Hi Everyone,
I am planning to do a Fetal ECG analysis from existing recorded data. How can I collect fetal ECG data sets (Raw data) from other research work for my analysis ?
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Dear,
You can get data from following link:
2. Left side, you can get PHYSIONET, select it, then phyiobank and finally phyiobank ATM. i.e. https://archive.physionet.org/cgi-bin/atm/ATM
There you can search for Fetal ECG.
Still, if not able to download, pl. let me know....
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I am working on IoT Based non-invasive driver health monitoring system for driver to avoid accident . I have developed system and it's working very well with . Now I want to embed sensors on steering wheel and I need to mount metal strip as ECG dry electrode for ECG reading. I need suggestion which metal should I used that have best conductivity with human skin.
Thanks
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Stainless steel, anodized aluminum and NASICON-ceramic material have all been found to have acceptable conductivity for dry cathodes.
That said, because of metals' inelastic surface, such electrodes are very vulnerable to motion artifacts, which may become a major problem in embedded sensors on steering wheels, as you intend to implement them.
You can get some ideas from previous similar implementations, such as the CardioWheel:
I would also look into porous material such as polysiloxane, which should, at least in theory, come with attenuated motion artifacts.
This paper may be of use to you:
Best of luck with your project!
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I am working on real time ECG monitoring system. I send ECG data from AD8232 to raspberry pi4 through Arduino serial communication and processing it using python. However, filtering the incoming ECG data points using a zero phase filter is not feasible as it does forward and backward filtering. Is there any linear filter/filtering method which can be implemented in python in real time and filter ECG signals as it comes?
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Thank you Aparna Sathya Murthy Fehmi Özel and Jose Risomar Sousa for your suggestions. Moving average method on AD8232 data works fine.
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Dear Colleagues,
Please suggest any open source software for ECG signal analysis.
Thanks in advance
N Das
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In the paper we propose a new method for removing PLI.
This method consists in identifying the ECG and noise frequency range for further zeroing wavelet detail coefficients in the subbands with no ECG coefficients in the frequency content. Afterward, the enhanced ECG signal is obtained by the inverse discrete wavelet transform (IDWT). Matlab implementation: https://github.com/brunobro/power-line-interference-removal-in-ECG
Would anyone have another similar idea, but who do not use classic approaches to solve this problem?
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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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Why the P and T waves are low-frequency signals whereas
the QRS complex is a high-frequency signal. Include diagrams of action potentials and an ECG waveform in your reasoning.
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P wave indicates depolariztion of atria and qrs represents depolariztion of the ventricles. The ventricular walls are much more thiker than atrial walls.
The amplitude of the waves in depends upun the chamber dimensions and the wall thickness. So the qrs waves are more prominent as it reflects ventricular activity. So propagation velocity is not sole determinant of frequency or amplitude of the waves in the ECG.
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my research is accuracy of ECG among doctors
dependent - ECG score (normally distributed)
independent - sociodemographic characteristic ( not normaly distributed)
which do i use for one to one correlation?
pearson/ spearman OR single linear regression?
what is the differences
i was told it was the same
but yet the results are different
i want to proceed with multiple linear regression
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All the fellows have contributed well. Whenever we are to analyze the data, we ask ourselves whether we want to check whether variables are related or one is said to have impact on the other. If the case is of measuring strength of relation then we can use cross-tabulation (nominal or ordinal data), Spearman (ordinal data) and Pearson f(Continuous data).
But if the question is regarding one variable being the reason for the other then we need to use regression.
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Hi,
I am looking for a dataset as a supporter in my project. I'm looking for the heartbeat signal or ECG recording of a person having a nightmare. I've looked at dataset sites on the internet (especially PhysioNet) and I couldn't find them. If anyone has another idea or has data to share, I would be very happy.
Thank you very much in advance.
I wish you healthy days.
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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.
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If anybody have any database or paper related to this let me know.
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Dear Vivek Upadhyaya,
Thank you for wonderful question.
We are looking at the ECG of 370 patients who have been recruited for follow up to see mid term effects after having covid.
Our initial findings shoss increased trend of cases with sinus bradycardia and tachycardia, conduction defects in various forms and fragmented qrs patern in the inferior leads.
We will compare these with Ecg of the normal population to know the significance, once the follow up is completed.
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The analysis of electrocardiogram (ECG) signals allows the experts to diagnosis several cardiac disorders. However, the accuracy of such diagnostic depends on the signals quality. In this paper it is proposed a simple method for power-line interference (PLI) removal based on the wavelet decomposition, without the use of thresholding techniques.
A Matlab code for power-line interference removal is available in: https://github.com/brunobro/power-line-interference-removal-in-ECG.
I request the evaluation of fellow researchers.
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Dear Researcher
I wish the best for you with Regards
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Hey guys,
I'm working on an EEG and ECG synchronicity analysis via entropy analysis, i.e. (Cross) Multiscale Fuzzy Measure Entropy, in a post-physical exercise, recovery setting.
I already calculated Fuzzy Measure Entropy for EEG and ECG time signal of 1s interval length. EEG and ECG time signal was divided into the same interval length of 1s each in order to be able to calculate Cross Fuzzy Measure Entropy between EEG and ECG for synchronicity analysis. Entropy values of the 1s intervals were averaged to one minute values. Samplerate was 1024Hz and entropy dimension parameter M was set to 2. So, there a enough data points according to the present recommendations of 10^m data points considering Sample Entropy or Approximate Entropy and even less recommended data points for Fuzzy Entropy.
But I'm wondering, if 1s intervals of the ECG (corresponding to 1-2 heart beats) are long enough to evaluate the heart irregularity. Other studies often used much longer time intervals of ECG signal or didn't use the ECG signal at all, but RR-intervals. I couldn't find a recommendation of time length or heart beat amount in order to evaluate the heart irregularity correctly. Do you have any advice?
Furthermore, due to the fact that the brain and the heart "work" in different time scales, I'm working on a multiscale entropy approach to hopefully get a clearer view of the time scale effect between EEG and ECG irregularity analysis. I already calculated Multiscale Fuzzy Measure Entropy for EEG and ECG signal separately (scales 1-20, 1s intervals, 1024Hz sampling rate). Does anyone already made some experience in this context and could me give some advice? Does a Cross multiscale analysis between EEG and ECG makes sense in order to reduce the problem of different time scales in EEG and ECG signal?
Thank you very much in advance!
Best regards,
Alex
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Hi Alexander,
Awesome! thank you very much for sharing your hypothesis. Regarding your last comment, I think there is no criterion to establish a number of data points, but maybe you can extrapolate the concepts from heart rate variability to entropy from raw ECG data, e.g. if you want to evaluate the cardiac parasympathetic activity, then epochs of 10-20 seconds would be fine. A sample rate at 256 Hz would be also acceptable, in any way, the higher a sample rate, the higher risk to get a bias in your entropy calculation. With a higher sample rate, you are prone to involve random processes (or high-frequency noise) in your signals. You could also perform an empirical analysis with variations of your epoch lengths: 10 sec, 20 sec, 30 sec.. etc, and see where you observe more interesting results. Obviously, the number of cardiac cycles will not be homogenous across patients, but I think for entropy evaluation the length of the time series across patients should be the same. Probably this is still an open question that you have a chance to answer with your research. I hope it helps...
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Can I get ECG in digital format instead of raw signal or image format without company's help?
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Thank you for your reply.
I will read the poket guide you suggested. I guess I need get familiar with ECG data first before I know how to format the xml format data in my hand. Thank you for your guide.
Best wishes
Qingtao Hu
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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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We are in need of ECG data of covid19 patients in an effort to classify suspected patients who could possibly be infected by the coronavirus.
Other biodata like respiratory rate, body temperature, pulse or HRV should also prove helpful.
Any information on the possibility of future release of such data would be greatly appreciated.
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Hi, maybe this work would be helpful;
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i need ECG dataset for those patients which they had positive covid-19 infection
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Hi, maybe this work would be helpful;
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Just searching for any good papers on the frequency content of EMG. Yes, I know it will be different if collected with a needle or on the skin. I am more interested in the second. I want to see how much it overlaps with the ECG...
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See the attached paper
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Hi, anyone working on a health monitoring system? I have some queries related to ECG.
1: Can we get ECG from fingertips?
2: Which sensor is best for reading ECG?
3: Which algorithm can we use to emit noise?
Thanks
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For ECG direct measurement you need two electrodes on the skin ou under the skin (implant).
If the electrodes are close to the heart the signal amplitude is higher.
A distance of at least 2cm is usually advised to get an acceptable signal to nose ratio (SNR).
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I am working on a research which is about classifying heart disease from 10s ECG data from 12 leads. The classification part is complete.
I want to implement grad cam algorithm to interpret important parts of the time series/ leads that made the classification.
Most resources work on image data.
Is there any good resource on using grad cam on time series data? Like a data of size (1024*12).
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Pl. refer following articles:
cam_tutorial_ECG200.ipynb - Colaboratory (google.com)
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If someone know any information from where raw ECG data can be collected or If anyone has already collected the raw data, can you send me or mail me.
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I recommend Polar H10 as it has quite nice properties for it price. Sampling rate 130 Hz, not avesomely high but sufficient for most evaluations done during daily activities. Data are streamed by Bluetooth and this give a possibility to communicate with a program in real time. ECG data can be as well stored and downloaded later using Polar dedicated software; they are exported in CSV format.
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I am doing changes in delay (working in aurdino), but not getting more than 60 samples in one second. what could be the reason?
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Fehmi Özel : Sir! Please find the code I have done in aurdino for fetching signals
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In all most every ECG trace paper we get a computer generated report. But it's performance in interpretations are many times far from accurate. But it measures the intervals very reliability. What is your experience /observations?
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Good question, computer is good in measuring PR and QRS intervals. QT measurement sometimes is misleading and needs to be recalculated especially in A.fib. rhythm or in the presence of U wave or baseline artifacts.
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researching in the area of emotion recognition
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يجب توفر هذه البيانات للتشخيص
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If I were to connect a snap electrode (ones used for ECG/EMG; 10mm diameter) to the constant-current stimulator for the purpose of stimulating the femoral nerve, is this still a valid procedure for interpolated twitch technique?
Previous studies have used 10mm diameter electrodes, and it seems that only ECG/EMG electrodes are this small.
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In our early work on the knee extensors, we compared the so-called nerve stimulation technique side-by-side with the muscle stimulation technique and got the same results in terms of torque production. We also had the participants compare the stimulation sensations. The muscle stimulation was considered more tolerable.
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Due to hand movements very noisy signal i get. I need to know when we normalise the data and which filter i should apply?
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Filtering the signal might help. The choice of the filter depends on the type of artifacts you see in your signal and the desired outcome. I'll assume that you want to perform R-peaks detection. You say that there were many hand movements, which I assume cause waves in the ECG isoelectric line, which should normally be flat. If so, apply high pass filter with 5 Hz cutoff. Conversely, if your signal is contaminated with high frequency noise but you can still somewhat see the PQRST complex, apply low pass filter with 35 Hz cutoff. If you are unsure you can also filter the signal with 5-35 Hz band pass filter and see whether the signal improves.
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Hi,
Can anyone please tell me what are the ranges of four different pole orders such as first order, second order, third order and fourth order respectively of butterworth filter for ECG signal?
Thank you in advance.
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Pampa, just run this MATLAB code (below). Enjoy :-)
%===
%Butterworth filter - by F.S. Schlindwein
N=4; % order - change here for 1, 2, 3, 4, ...
Np=512;
step=2/512;
frequencies=0:step:2-step;
%frequencies=0:2/512:2-2/512
Wn=0.2;
Fs=2;
%[zb,pb,kb] = butter(N,Wn,'low');
[Bb,Ab] = butter(N,Wn,'low');
%[sosb,gb] = zp2sos(zb,pb,kb);
%Hb = dfilt.df2sos(sosb,gb);
%grpdelay(Hb);
Hb = freqz(Bb,Ab,Np,'whole',Fs);
figure(1);
plot(frequencies(1:256), abs(Hb(1:256)));
grid on;
set(gca,'YLim',[0 1.1])
title('Gain of Butterworth');
print -deps -tiff -r600 Butter_gain;
%set(gca,'YLim',[0 5])
figure(2);
zplane(Bb,Ab);
title('Poles and Zeroes of Butterworth');
angleb=angle(Hb);
figure(3);
plot(frequencies(1:256), unwrap(angle(Hb(1:256))));
grid on;
title('Phase of Butterworth');
figure(4);
[Gd,W] = grpdelay(Bb,Ab,Np,Fs);
plot(frequencies(1:Np-8)/2,Gd(1:Np-8));
title('Group delay of Butterworth');
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A 27-year-old woman presents to her primary care provider for a routine physical examination. She denies any heart disease and has never had any cardiac symptoms. However, on physical examination, the clinician detects an irregular heart rhythm, and a 12-lead ECG is obtained.
What does her ECG reveal?
1-Atrial flutter.
2-Atrial fibrillation.
3-Normal sinus rhythm with premature ventricular complexes.
4-Normal sinus rhythm with aberrantly conducted premature atrial complexes (PACs).
5-Normal sinus rhythm with PACs and Wolff-Parkinson-White (WPW).
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Dear Mustafa Mahmood Eid
Do you concur with my diagnosis seconded by others?
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I am trying to eliminate the ECG artifact in my sEMG data. Unfotunately, my efforts to find the ideal method in the literature were not successful.
I am using AcqKnowledge by Biopac for the data analysis.
Any help would be much appreciated. Thank you!
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Filtering poorly collected data is probably not the answer. It might help to put the EMG electrodes closely together, in line with the fiber direction of your muscle under study. They could then pick up the same amount of ECG signal. This would be canceled out in your differential amplifier for the EMG.
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Wavelet transform is being widely used as a method to denoise ecg signal. But it can be argued that it lacks self-adaptation. We need to find a suitable wavelet function but in clinical applications, there are complex noises so we cannot make a universal wavelet function. Is this a correct assumption and if it is then are there any better ways? There is some research going on using deep neural networks to achieve better denoising.
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The denoising of electrocardiogram (ECG) represents the entry point for the processing of this signal. The widely algorithms for ECG denoising are based on discrete wavelet transform (DWT). ... These performances are quantified by some ratios such as the output signal on noise (SNR) and the mean square error (MSE) ratio.
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Where I can find ECG dataset in JPG format containing normal and arrhythmia images? Famous datasets like MIT-BIH Arrhythmia Dataset and PTB Diagnostic ECG Database does not contain image data. I want to develop a mobile app which takes an ECG image as an input from Camera and then tells the diagnosis. Kindly help.
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ECG recordings in a data format (Ms vs mV) contain very rich, subtle information, which will be substantially degraded by converting them into JPG format.
When you really want to use such approach, you have a good reason for doing so, it would be a good idea to use Python program to convert ECG data to JPG format automatically. This program can be used in script to convert many files during a short time.
You can use any database, including MIT one, to generate those pictures.
I recommend you to read something about complexity measures that are used to classify & even predict various heart rhythms (some citations are in my paper on prediction of TdP arrhythmias).
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I am working on ECG arrhythmia classification by using SVM , implemented some kernels tricks
and using different kernels on MIT BIH dataset (features create 44187 row ,18 column matrix)
now it is difficult to plot support vector for such large data sets , now how can i plot it and please suggest any other plots or methods to show comparison between different kernels , i already have comparison chart of accuracy efficiency etc.
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It might interest you that there is a possibility to use complexity measures to assess the state of the observed complex system and make decision about arrhythmias.
An example how to do it can be bound in our paper on prediction of TdP arrhythmias from ECG recordings. Everything is explained in the paper in detail. The final version will contain rewritten entropy section and substantially improved methods, intro, etc.
Back to your question. Complexity measures when applied wisely enable us to substantially reduce the complexity of complex systems under the observation. This includes biosignals along with ECGs, EEGs, etc.
Hopefully this will enable you to orientate yourself in this exciting, yet quite complicated area of research.
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Hi
I am working on an IoT based health monitoring system as road safety for driver.
will read different vital signals.
Which electrodes we can use for batter reading on ECG? keep in mind we will place electrodes on the steering wheel.
Look forward
Thanks
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Dear Cathy, that was my first choice, but I have sugested also others leads because I dont know what Dr Supa will get during his research. Atte. Dr Daniel Iosa
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My lab has openings for graduate research scholars. I am looking for a highly motivated students for leveraging advanced bioelectromagnetics approaches to design and develop the new generation of smarter wearable sensors that provide medically accurate data. The ideal candidate will be a recent and motivated undergraduate in Electrical Engineering or Biomedical Engineering with strong academic records. The candidate will be expected to develop state-of-the-art wearable technologies to sense, perceive and control biological systems at the University of Utah. This work will contribute to the development of novel electromagnetic technologies to create innovative and impactful solutions. Visit srl.ece.utah.edu if interested.
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Thank you Dr. Benjamin Sanchez for sharing this information.
Best regards from Mexico,
Oscar J. Suarez
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I have digitized ECG data that need basic interpretation.
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I know about the non-commercial versions of such programs, which you can see heer:
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When using the ECG signals for detecting heart anomaly, is it necessary to have high resolution sampled signals or lower resolution sampling can be accurate enough?
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To produce any waveform you must sample it with a frequency fs greater equal the twice the highest frequency fm contained in it. fs>= fm.
Concerning ECG if you to reproduce its pulses you have to analyze the pulse in the frequency domain by following the practical approach followed by Jiri.
It is so that you choose a high sampling rate and then do the analysis to get the FFT spectrum and then reproduce your signal by IFT
Reduce fs slowly and repeat the same till you could not reconstruct the wave form this is the minimum sampling frequency.
Another very practical method is to enlarge the pulse and then estimate the number of regular points that can be used to draw it shape. The time between the adjacent samples will be the sampling time.
Best wishes
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I'm trying to theoretically design a active High-Pass Filter for ECG signal with cutoff frequency
Fc = 0.003 Hz...it's too close to 0 Hz and stop band frequency must be greater than 0 Hz . So how to exactly determine the orders of this HPF to satisfy this requirement ?
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Go after Antoniou's book
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I am researching QOVI-19 and need a database that contains biosignal data such as electrocardiograms.
Is there any dataset?
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ECG IMAGES DATASET OF CARDIAC AND COVID-19 PATIENTS
Dataset Link
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Hello everyone,
I know that impedance cardiography is used for calculating the stroke volume of the heart. Is there any way to estimate the value of stroke volume using just ECG signal without impedance cardiography? I need to estimate the stroke volume and the cardiac output from the ECG data.
Thank you so much
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There is absolutely no correlation between the parameters of and ECG waveform and the performance of the ventricular muscle eg contractility, stroke volume or any other ventricular parameter.
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Is it possible to use human portable electrocardiogram to record ECG in mice/rats to record their ECG? If we can, then what is their accuracy and their precision, and results? Whether that's validated?
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In my opinion heart rhythm is so different between small rodents and humans that human ECG recoding machines are not able to record rodents ECG correctly. We use a special monitoring system from HarvardApparatus to record ECG from rat/mouse at lab.
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I found some ECG along with PCG from Physionet 2016 challenge dataset with name "a". But, description of ECG is unknown hence i am unable to apply particular algorithm to detect important features. If there are any dataset of ECG along with PCG it will be of great help to my research. Thanks
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In this link, you can find several datasets :
Best regards
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We are looking for a database or a data set with ECG images. We have already found some that contain the ECG data, but we need the images to evaluate them with AI methods.
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Domina Petric is there also a ECG database with jpeg, png images (or a compareable image format)? As far as i can tell the provided source is more like an ECG-Guide, but not a image database. To program an AI algorithm a image database including a correct analysis of each image would be needed (best case if its a large collection).
When images are modified with colors or words in it, it is rather hindering than helpful when it comes to an algorithm reading it. Unlike for humans of course.
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I have an xml file(attached) and I'm trying to read lead II ECG data from full disclosure. however, I am trying to analyse the data and im getting unexpected RR values. I assume my extraction for Lead II is not correct. I'd appreciate you help with that
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Currently i am working on project which use ECG data to predict heart diseases.
I found this PTB-XL data set for my research and the data is in .dat and .hea format.
I am looking a code to get a visualize view of these ECG.
I found some youtube videos that they try to convert these (.dat and .hea) file types from PhysioBank ATM.
Since the dataset is recently release still its not available in PhysioBank ATM.
If anyone can provide me a code or have any thoughts please add a comment on this post. Your help would be greatly appreciate and it really help my project.
Thanks.
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PTB -XL data set is good for research purpose .
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I am looking for public datasets of COVID-19 patients with biosignal information (ECG, for instance) and/or imaging data (X-ray, CT scans). Found some CT scan datasets but with a low number of cases (~50).
Is there any dataset I might be missing?
Thanks!
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It is need for experimental long measurements of the ECG
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Mikhail Kataev you can buy Disposable ECG Electrodes online...
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Those who are working in the area of Data Acquisition, Signal Processing and LabVIEW.
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Dear Shabana Urooj
Methodology used in ECG data of these studies may guide you which method to use depending on your budget, availability, and requirements:
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We collect the ECG signal by ourselves and have already detected the R peaks by our method, but we don't have any reference signal to verify the R peaks we found are accurate.
It seems like the statistical method such as sensitivity, positive predictivity, the error rate are the improper way to use.
Can someone please suggest some statistical method to evaluate the performance of peak detection without a reference signal.
Like, calculate the average RR interval?
Thank you all for your opinions. I highly appreciate
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Get an expert to generate the 'gold standard'. Better still, 3 experts and implemment a vote of off one out and averaging the agreeing 2. Yes, time consumining and expensive. Certain things simply cannot be done 'on the cheap'.
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Using ECG can we predict the mood of the person like sad, or happy, or angry, or anxious. If Yes, then on which movement of the heart (atrial contraction or atrial relaxation or ventricular contraction or ventricular relaxation can tell us that)? From the papers I have read, It is easy to detect stress levels of the heart which leads to ischemia. Can anyone please justify on this.
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Any stress or change of mood (anxiety, anger, fear, and elation) leads to sinus bradycardia on ECG due to release of the stress hormones (catecholamines and steroids).
In people with heart disease, such events may lead to heart attack or arrhythmias that will be manifested on ECG.
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You have invented a new type of artificial aortic valve, the performance of which you are now assessing in the first human heart. You have catheterized the aorta and placed a pressure transducer there. The ECG and aortic pressure are recorded simultaneously from your subject and are shown below (Note: The large voltage spike of the ECG corresponds to the beginning of ventricular contraction, while the more diffuse subsequent peak corresponds to repolarization and relaxation of the ventricular muscle)
a) You are concerned about the results of your tests. What is it about the pressure data that concerns you? (b) What do you think the general problem with your valve is, and how might you improve it?
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is there a image of tracing
I'm unable to see
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Some time go we have started this discussion by publishing a conference poster which attained much attention; I'm curious about the opinion of scientific community on this subject: what could be a possible language to use in this area?
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Dear @All, generally you are right that all the mechanisms which require active conductance (i.e. sustained on expense of the energy of the medium and not of the energy of the source) are order(s) of magnitude slower than the passive conductance. Of course conductance velocity depends on neural control, which in case of the heart appears under the name of a dromotropic arm of cardiac regulation, so this value is constrained. What we originally measured was passive conductance velocity.
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I want to send ECG sensor data from raspberry pi using python 3 to Thingspeak channel.
Thingspeak encounters the following problems:
* Thingspeak channel gets updated once every 15 seconds, which is too low for ECG signal that is sampled at 1KHz frequency.
* For high data update speed, thingspeak ask for payment.
How to send the data on a high update rate to Thingspeak?
Please recommend any free platforms like Thingspeak, so that I can send my data?
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Hi,
You can use google sheets for this purpose as Thingspeak will ask for a payment to reduce delay.
Use JSON (JavaScript Object Notation) to upload data from the sensor to google sheets. You can expect a delay of 1 sec.
You can find multiple tutorials in youtube regarding uploading sensor data in google sheets using JSON.
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I am working on ECG classification by SVM, need to know what is most recent trends and advancement in this classification technique
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I am doing my bachelor thesis on epilepsy so I am asking where can I find ECG dataset for epileptic seizures other than this dataset https://physionet.org/content/szdb/1.0.0/ as it is very small
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Hello,
I'm interested in purchasing a biosignal acq. system for measuring EMG (surface, with electrode arrangement in array as an option, no intracellular) and ECG (also surface, but possibly more than 9 electrodes). Maybe EEG can be considered too if it comes at no extra cost.
The recording conditions are not entirely specific, but at the most extreme, it would probably involve regular single limb movements (e.g. single arm or leg extension/flexion at a regular velocity) and also trembling (due to forced effort).
Also, I don't plan on constructing a full-scale biolab, so a minimal system would suffice. Ideally, a single acq. frontend + PC software is what I imagine as minimal. At most, I can accept one backend hardware.
I have looked around the net, but my search didn't get me further than Biopac and AD Instruments. I was wondering if there are other manufacturers that offer something fitting the descriptions above.
Thanks,
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The team are very helpful and they hold an annual usergroup meeting to discuss advances and applications.
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I have ECG values I want to write a computer code that could found out about Left ventricular hypertrophy possibility of a person using Estes criteria. Can someone guide me on how can I do this?
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Very interesting topic! I think you may use above mentioned receommendations.
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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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