From Waves to Wisdom: Deciphering ECG Data

A biomedical engineering student from Florida International University, Elmar Orjuv has been researching the cardiovascular system and the constant changes the heart will make throughout complex and simple exercises.

We spoke about the research he had conducted regarding his heart and the functionality of his heart by his research from recording the heart rate and the heart rhythm through an ECG machine after doing various exercises such as sitting down, lying, and working out. He shares data that the ECG created through a line graph that explains the change of patterns after various exercises as well as any irregularities within Elmar’s Heart rate that can be an underlying heart condition.

This personalized data takes a deep dive into Elmar’s heart patterns, beats per minute, and any irregularities as he explains how the heart works and the importance of regularly getting checkups on the heart through an ECG machine.

Take a listen.

TRANSCRIPT OF INTERVIEW

Keith : 0:00 :

Hello, everybody. My name is Keith Jaramillo. And today I have a special guest, Elmar Orjuv , who is going to talk to us about data visualization and data within his major. And that ECG research that he has currently been doing within Florida International University. Hi Elmar, how are you?

ELMAR : 0:25 :

Hello. I’m doing I’m doing great. I’m very excited to talk to you about the data analysis in my major today.

Speaker 1 0:33

Perfect. So could you just sort of introduce yourself so everyone can know who you are and what you’re about.

ELMAR : 0:40:

So my name is Elmar. I am a senior in biomedical engineering with a minor in chemistry. I’m actually interested in data analysis. We’re working with data it programming and just overall using data in my field,

Keith: 0:55:

How does your major use data and data visualization.

ELMAR : 0:58 :

So in my major biomedical engineering, data analysis, extremely important as one of the most crucial directions in the field, biometrics and biometrics analysis involves data analysis. So for example, the report that we will be discussing today, the ECG report, which is electrocardiogram, it is basically a graph that records the electrical events that happened in your heart.

KEITH: 1:26:

What type of data visualization works best? When it comes to your research? And which one is most commonly used?

ELMAR: 1:36 :

I would definitely say it depends on what field is exactly it’s being used in. So for example, for this specific report the ECG, it definitely is better to use a scatterplot that shakes time as x axis and voltage as y axis to represent how the voltage change as time went on. However, we also definitely use bar graphs, we definitely use plots. We definitely use pie charts as well, it just all depends on what data exactly is being analyzed.

KEITH: 2:06:

Can you explain how you use that data and the data visualizations within this ECG project?

ELMAR: 2:13:

Definitely, I was a subject for this experiment, specifically, the electrodes were placed on my left wrist, right wrist and the left ankle. And my pulse was recorded in four different settings. So it first was supine position, which means lying down relaxed, the second is seated. And third was specifically recording deep breathing. And the fourth was right after exercise, which went for around five minutes. All of that was to represent this how to set specific segments in the cardio gram change in response to your activity levels.

KEITH: 2:48:

So how did you guys collect this data to get the most precise, like heart rate and see how your heart changes throughout these different activities?

ELMAR:2:59:

The data was collected using BiPAC. It’s specific software for engineering students, its success X, clinical engineering center labs, the soft specifically collected the numbers that the timeline that were used to make the scatter plots x and y values, obviously. And those values were time voltage. And then that data was used to make drops in MATLAB using using code and just specific commands. And then that those graphs were plotted, and we analyze the data. Using those plots.

KEITH: 3:34:

Throughout your research you did, like you said, You did various exercises, such as deep breathing working out or after working out, laying down and sitting are sitting up. So did you see a big difference between these three, when you were when you guys got your data and your data visualizations?

ELMAR: 3:53:

If you look at the graph, it’s important to introduce the term wavelength. So wavelength is basically how long it takes between each heartbeat, from peak to peak, changes in activity levels, that wavelength or period of the heartbeat decrease accordingly. So for example, it goes all the way to the activity of our nervous system. When you are arrested, and you are not exactly participate in the activities, the alomost nervous system is predominant, and your heartbeat is slow. Therefore the period that it takes for a heartbeat to occur is longer. However, when you’re performing activities like working out, or breathing, even even the even the breath the fact that and the sympathetic nervous system activates and become more more more active, it takes over, so off and it has a lot of effects on the on the body overall. However, in terms of ECG, it significant decreases according to the act. He’s a little obviously, time that it takes for a heartbeat. And overall, all those changes should be in very limited amounts, significant changes, extremely significant changes that are constant may detect abnormalities. And depending on a segment, or an under the interval that’s affected, those rapid changes may signal some abnormalities.

KEITH: 5:26:

Why did you guys choose specifically a line graph over, you know, a scatterplot? And what what was the decision behind making that what made the line graph perfect for this research?

ELMAR:5:41:

So to begin with, the decision wasn’t exactly ours as the ECG, it’s traditionally a line graph. It’s time versus voltage. And, however, the decision originally of the Zoom was due to the fact that ECG specifically wants to record how the voltage changes over time, as a function of time of time, we need to see the electrical activity of the heart, the best way to detect that is voltage. Obviously, we can use other graphs to get some values like mean, standard deviation, however, for the best visualization for a doctor, for example, cardiologist to detect any abnormalities or for researcher to dig in abnormalities, it’s best to use a line graph to see irregularities as compared to just the rest of the graph.

Keith :6:32 :

Did you find anything you’ve regular within your data?

ELMAR:6:35:

most of the values for in in limits of normal, they weren’t. They were, they were fine. And according to the average, however, there was one irregularity within my heart activity that was detected. It was the QRS complex.

KEITH: 6.40:

Could you explain that?

ELMAR: 6:45:

Basically, QRS complex is if you look at if you look at the ECG ECG graph, there, there are certain peaks, the QRS is the three points that detect the peak, Q is the lowest point before the peak, r is the highest point, which is the peak and the S is the lowest point right? After after the peak, QRS complex, and irregardless, it may signal some abnormalities in the heart. So for example, if your QRS changes significantly after exercise, it may indicate several issues potentially, like myocardium hypertrophy of the heart, potential damages that could come from infection. However, as I don’t have any history of those diseases, or syndromes or any other occurrences, it was assumed that I have an imbalance of electrolytes, which is also a potential cause of that, which basically means that electrical events in my heart are slightly abnormal, depending on the day due to the nutrients that eat. So for example, calcium, magnesium, just, overall, all those nutrients and electrolytes in my body, they may cause imbalance and that disrupts the flow of electrical impulses.

KEITH: 8:16:

Where did you see these irregular parts in your data and with what what exercise did you see it the most.

ELMAR: 8:24:

So the normal QRS, the resting QRS in of my heart was definitely in limits, it was, it was average, it was normal, it was very close to the average value I’ve ever read. After five minutes of exercise, specifically jumping jacks, the QRS, the heartbeat. That’s that specific part of the graph, it was changed more than expected. So we compare the two is ECGs, which is the resting and after exercise. And after exercise, my heartbeat was generally normal. As I mentioned before, however, the QRS complex was changed more than expected. That’s exactly where the record was noticed.

KEITH: 9:09:

So overall, what makes this data so important?

ELMAR: 9:12:

geez, something that I would personally recommend to be taken at least yearly, because I’ve no mouths in the heart, and in the ECG may help you predict and possibly prevent infections, and maybe even detect issues or syndromes in your heart that you may want to get treated.

KEITH: 9:33 :

Thank you so much Elmar for teaching us about ECG and what data visualization would work best especially when it comes to the heart, and everything that has to do with at least the cardiovascular system. And yes, I’m very surprised that using line graphs is already built in within ECG machines and And thank you.

ELMAR: 10:00:

Thank you. It was a pleasure.

Transcribed by https://otter.ai