Electrocardiography - Science method

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

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!

plot(xx)

xx is your one-dimensional time series

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.

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?

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/

Comparison of ECG in rats and humans is highlighted in the attached document :

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/

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.

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

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.

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.

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

📷

Instead of a thousand words )

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

Besides the ACDC 2017 dataset we can use LVSC ( Left ventricular segment challenge) 2009 Dataset . it will be usefull Dataset

Kindly check..

Check..

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.

Hello Kaled Nather Taha

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.

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

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.

Dear,

You can get data from following link:

1. Go to https://archive.physionet.org/physiobank/

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....

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!

Thank you Aparna Sathya Murthy Fehmi Özel and Jose Risomar Sousa for your suggestions. Moving average method on AD8232 data works fine.

Nityananda Das

Bruno Rodrigues de Oliveira

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

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.

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.

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.

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.

Dear Researcher

I wish the best for you with Regards

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...

Hi Pedro Morais

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

Occasionally authors share their data together with publications. This is still an uncommon practice but you may get lucky.

Hi, maybe this work would be helpful;

Hi, maybe this work would be helpful;

See the attached paper

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).

Pl. refer following articles:

cam_tutorial_ECG200.ipynb - Colaboratory (google.com)

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.

Fehmi Özel : Sir! Please find the code I have done in aurdino for fetching signals

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.

يجب توفر هذه البيانات للتشخيص

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.

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.

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');

Dear Mustafa Mahmood Eid

Do you concur with my diagnosis seconded by others?

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.

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).

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.

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

Thank you Dr. Benjamin Sanchez for sharing this information.

Best regards from Mexico,

Oscar J. Suarez

I know about the non-commercial versions of such programs, which you can see heer:

Dear Shahab Pasha ,

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

Go after Antoniou's book

ECG IMAGES DATASET OF CARDIAC AND COVID-19 PATIENTS

Dataset Link

http://dx.doi.org/10.17632/gwbz3fsgp8.1

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.

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.

In this link, you can find several datasets :

Best regards

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.

Hi,

Try using ECG-ViEW II.

PTB -XL data set is good for research purpose .

This could interest you:

Kaggle:

https://www.kaggle.com/plameneduardo/sarscov2-ctscan-dataset Github: https://github.com/Plamen-Eduardo/xDNN-SARS-CoV-2-CT-Scan Paper: https://www.medrxiv.org/content/10.1101/2020.04.24.20078584v3 Original Video: ELLIS Against COVID19 https://www.youtube.com/watch?v=7RtDgGkxavc

Mikhail Kataev you can buy Disposable ECG Electrodes online...

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:

1.

2.

3.

4.

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'.

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.

is there a image of tracing

I'm unable to see

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.

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.

Have a look at https://elitedatascience.com/machine-learning-algorithms

you can see this link

Have a look at https://otbioelettronica.it/en/

The team are very helpful and they hold an annual usergroup meeting to discuss advances and applications.

Very interesting topic! I think you may use above mentioned receommendations.

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