ECG and Activity Monitoring: What Can We Learn?
Project Infobox 
| |
|---|---|
| Self researcher(s) | Maggie Delano |
| Related tools | |
| Related topics | Heart rate, Cardiovascular, Sleep |
Builds on project(s) |
|
| Has inspired | Projects (0) |
| Show and Tell Talk Infobox | |
|---|---|
| Featured image | 
|
| Date | |
| Event name | |
| Slides | Ecg-and-activity-monitoring-what-can-we-learn.pdf |
|
This content was automatically imported. See here how to improve it if any information is missing or out outdated. |
ECG and Activity Monitoring: What Can We Learn? is a Show & Tell talk by Maggie Delano that has been imported from the Quantified Self Show & Tell library.The talk is about Heart rate, Cardiovascular, and Sleep.
Description[edit | edit source]
A description of this project as introduced by Quantified Self follows:
Maggie Delano wanted to see what she would find out if she tracked her electrocardiograms continuously over time. Electrocardiogram is a measurement of ones' heart. A doctor would prescribe you a device that could measure your electrocardiogram when you have any sort of cardiovascular dysfunction. In this talk, she explains what an ECG and activity monitoring is and what she has learned from it.
Video and transcript[edit | edit source]
A transcript of this talk is below:
Maggie Delano - ECG and Activity Monitoring What Can We Learn
Good morning everyone, my name is Maggie Delano and I think the cardiovascular system is awesome. So I wanted to see what I would find if I tracked my electrocardiograms continuously over time. For people who don’t know what the electrocardiogram is, it’s a measurement of your heart. The electrical activity of your heart in particular, so if you ever have any sort of cardiovascular dysfunction, your doctor will probably prescribe you a device that could measure your electrocardiogram. So this is what my electrocardiograms looks like at rest. Each of these complexes corresponds with an individual cardiac cycle. And when we are looking at an electrocardiogram one of the most significant features you want to look at is the R-wave which is the peak in each of those cycles. And you can count R-waves to measure your heart rate. So you can measure it in two ways, you can measure okay, how many beats did I have in any given period of time, that’s my heart rate in beats per minute. Or you can look at the interval between subsequent beats and that measurement of your actual instantaneous heart rate. So when we have an electrocardiogram measurement, that can tell are a lot about cardiovascular health, but if you don’t have a context for that you don’t necessarily know what you were doing while you were measuring your cardiogram. So that’s where an activity tracker like an accelerometer comes into play. Accelerometers are basically in every Quantified Self device that you can buy today, and they can tell you things like your posture, both when you are sitting or when you are sleeping, and how active you are throughout the day. So we can combined the information we get about your cardiovascular health from electrocardiogram information, and the acceleration data we can start to tell a lot about health. So I’ve developed this wearable monitor for my master’s thesis, and one of the things that we wanted to do with that was measure how people’s cardiac health changes over time. And me as a relatively healthy individual and a quantified selfer, I wanted to see what could I learn if I started to where this device as I wandered around my daily activities. So today, I want to present two particular datasets that I got and thought they were representative of the sorts of things you can learn from wearing a device like this. So the two datasets are me, going about my daily activities, like one day after lab basically and then me wearing the monitor when I’m sleeping. So here we have my evening data. The top graph is my heart rate and the bottom graph is the three axis acceleration plot, and in the middle I kind of annotated exactly what I was doing during these different periods. One of the things that I really like about this is it. Sure, sure, how dynamic the cardiovascular system is. So little dips, peaks, and heart rate corresponding to me changing between different activities, like adjusting my bike lock or stopping to check things and you know, I’m cooking, but my heart rate is elevated as it was like when I got off my bike. So you can anecdotally see a lot about how your heart is responding to different sorts of activities. So I also just wanted to give you a little bit of a view of what things looked like you know kind of zoom in a little bit. So this is me when I’m actually resting, and so I’ve got a relatively reasonable heart rate, 57 bpm, and you can look at the acceleration data and see that I’m just kind of sitting in my chair relaxing. Then when I get ready to actually go out to get on my bike, here I am walking along. If you look at the top at the R-waves you can actually sort of start to see that breathing a little bit heavier than I was in the previous case. And in the acceleration data, there are distinct peaks that are associated with the steps that I’m taking as I’m walking. Now, here’s me cycling. My heart rate goes up a lot, my respiration is much more noticeable on my ECG trace and there are you know, actually some little bumps you can see in acceleration. So if you have ever have gotten falls steps on your Fitbit, this is the sort of thing that might cause that. So the first thing that I learned from wearing this around when I was doing my daily activities is that heart rate changes a lot. I’ve never been convinced that you can take like one measurement of day of your heart rate and that would really tell you anything about your health, and this basically just shows that your system is so dynamic and that is really true. One of the other things that I learned is that this is really interesting sort of anecdotally to see how my heart was changing over time. But in the shop window if I really get like more information I can see how healthy I am and track that over an extended period of time. So here is a similar graph, but me when I’m actually sleeping. So we can use the acceleration data to tell a lot about what posture I was in while I was sleeping. Sleep is a huge issue for me, so this is really interesting to track. So I’m tossing and turning a lot, particularly in the beginning of the record, you can see the big changes in posture correspond with big changes in heart rate. The big changes actually wake up during those interventions. One thing I found that was particularly interesting is that the one I called shift over there, there was a relatively small change in posture, but a really big spike in heart rate. So by measuring heart rate I have a bit of a better idea of like how my body is actually responding to me tossing and turning. So here, I am actually showing the R interval, which is the time between subsequent beats, and this looks a lot more noisy than the heart rate, the thing I showed before. But some of that variation is actually healthy, it shall heart rate variability. But one of the things that I learned when I was sleeping is that I actually have an arrhythmia. So there are premature contractions that occur, which are like these little blips here where they are actually like much faster than normal heartbeats. If we zoom in on this and look at the electrocardiogram information, you can actually see those premature beats. So premature beat occurs when a heat occurs faster than it supposed to and there is also a compensatory polls that occurs after that. So, on average, your heart rate is about the same. So if we are only measuring heart rate, you might not even notice that this arrhythmia happened at all, so one advantage of taking the electrocardiogram measurements rather than just looking at heart rate data. The other interesting thing that I learned about doing these things here is that I have a lot of premature contractions at the beginning of the record, and then towards the end of the record, I checked all of those different blips and there were basically no arrhythmias at all. And in the beginning I was tossing and turning a lot, and at the end I really wasn’t. So I’m now starting to think that maybe my arrhythmia has something to do with how well I’m unable to sleep. So the last lesson that I learned there was, I do have these premature contractions, and they tend to primarily happen during sleep, although I have seen them sometimes when I’m sitting around. The big takeaway I have from this is that I have a lot more questions that I haven’t necessarily answered anything yet. But I think it’s really exciting because even from just short snippets of data that I’ve been able to take, I have learned a lot about questions that I want to ask about my health coming up next. And there is a lot of interesting things that you can do with this data that I didn’t have the time to sort of talk about in this presentation, like heart rate variability which you can use to measure parasympathetic versus sympathetic response of your cardiovascular system. Also things like repertory rate, so you can measure it from ECG, and you can also see it from your acceleration data if you are actually breathing really slowly, like if I breathe in and out you can actually see it in the data which is really cool.
Also measuring things like cardiovascular dynamics, how do I change when I move from sitting to standing, and you can measure that over time to see how things go. So basically I think this is really exciting and there is a lot more to learn, and I am really glad to be here at this conference, and that’s all I have.
About the presenter[edit | edit source]
Maggie Delano gave this talk.