kbre93

Atrial Fibrillation (AF) is the most common serious heart arrthymia, affecting around 30 million people worldwide. People with AF have a 4 times higher risk of mortality and 5 times higher risk of stroke than the normal population.

I've implementated a widely-cited CNN architecture for ECG-based AF classifcation. Classification can be run directly off a 60 second recording from a Polar H10 Heart rate monitor.

Hi stenmorten try going into Settings > Privacy & Security > Bluetooth, and adding your terminal application or development environment to the list of applications that are allowed to use Bluetooth

This should be fixed now. Let me know if you still have issues

This should be fixed now. Let me know if you still have issues

The blue circle is your chest expansions and so when it follows the gold circle you’re controlling your breathing well. Good hrv is above 150 ms and is shown as a green band on the bottom graph

Thanks ar-jan I’ve merged those and I think the Bluetooth issues are related to security permissions

Yes basically, I remove the gravity component by high pass filtering, and then take just the component of acceleration which is along the axis of chest expansion (i.e. pointing out from the sensor) off the top of my head this is the z axis. But this only works when you are sitting quite still, movement introduces a lot of noise to the acceleration

Very nice, yeah the Polar H10 sends raw ECG, as well as the calculated heart rate, so could be what you're looking for.

I'm glad

It currently only works with a Polar H10, which has a heart rate monitoring (ECG) and an accelerometer (for measuring breathing rate through chest expansion)

Right, but I don't think they measure it directly (could be wrong), I think they measure it based on change in heart rate

Interesting, thanks for sharing, it looks like it has similar core sensors to the Polar H10, but I'm not sure how it compares overall

That article is referring to the internal memory that the Polar H10 has, which can be used to store some data. But you will eventually need to connect the device to retrieve that data. You can access this data programatically with a blue-tooth-low-energy client like my application is doing (using python)

This would be possible I with the Polar H10 I think. The H10 sends raw ECG and analysis on this waveform could be used to find anomalies like afib. I haven't done this myself though.

This is measured over a very short time scale though – so not a persons absolute max and min heart rate, just how it varies over a few seconds. And yes, higher HRV is generally healthier and points to a better balance between the fight-and-flight response, and rest-and-digest response.

No, this is just for biofeedback training, so just for a session once a day

Yep that's right, and the H10 is able to stream the ECG waveform. For this project I used the inter-beat-interval that is sent however, as it means I don't have to run peak detection on the ECG to get heart rate.

The H10 is a newer and better version of the H7. Because breathing rate is measured from chest expansion that is pretty easy to verify (there can be noise in the estimate of course due to movement artefacts). Heart rate estimation from the H10 is widely considered to be reliable, The H10 also offers to ability to stream the raw ecg to check this.

Yes it's common to estimate breathing rate based on the oscillations in the heart rate pattern. But estimating it from the chest expansion is a more direct method

Interesting, thanks for sharing. It's cool they do it as a platform, but I'm not sure what the benefit over using a Polar H10 would be, which are widely available, I guess it would be custom branding of the hardware

I think most bluetooth low energy devices can be connected to quite easily programmatically as long as you know the data specification. Polar use to publish their data spec on their website but took it down a few years ago so I had to search around to find it.

What's your os? Only been test on macOS so far

Yep it's via bluetooth

I've it's quite common for fitness trackers to predict the onset of sickness from heart rate variability. I've found they don't offer much real-time feedback, and don't have breathing detection though which was what I wanted to explore for this project, to train hrv

Yep it's taking the difference between peaks and troughs in heart rate, for plotting with the breathing rate it looks at the min and max over the breathing cycle.

Hey no worries and thanks. It was a break-through for this project to find the H10 gives accelerometer data as well as heart rate, so that it would be possible to estimate breathing rate, which I haven't seen other apps do

As you breath more slowly, heart rate oscillations (i.e. the variability) increase, which is a reflection of the parasympathetic nervous system activating (the "rest and digest" response). This can help to mediate the stress response, which from the sympathetic nervous system or the fight-or-flight response.

Thanks, I've personally found benefits from being to learn to control my breathing in stressful situations.

Right now it only works with the Polar H10, but it would be possible to adapt it if other chest strap monitors have an accelerometer in them.