Heart rate is the data you get from a regular heart rate monitor like a chest-worn belt or a wrist-worn band based on optical sensors. The heart rate computed by these devices is an averaged value of the R-R intervals (explained in the main page) calculated over a period of time.
HeartyPatch, on the other hand, keeps track of each individual heartbeat to measure the precise time between beats to arrive at what is known as heart-rate variability or HRV, for short.
Here is what an HRV tachogram plot looks like:
What’s seen here is a plot of R-R intervals against time, taken from a HeartyPatch. It can be seen that there is a pattern in regular heartbeat, but that pattern doesn’t always remain the same. There can be several reasons behind the plot variability.
A Poincare plot is a plot to quantify self-similarity in periodic events, in this case R-R intervals. In simpler words, it shows the difference between the current R-R intervals and the previous one.
Interpreting this plot is simple: there are a bunch of points that are quite close to each other, meaning that there is a known pattern in the variability. Too many outliers outside of the concentration of points could mean a certain condition or an abnormality. For example, below is a plot for a specific type of Arrhythmia, simulated by an Arrhythmia simulator connected to the HeartyPatch.
By detecting these patterns in the similarity of the R-R intervals, it is possible to quantify certain parameters that can, in turn, be used to detect an event. These are just the basic methods for time-domain analysis, but there are several other frequency-domain results that can be obtained. HeartyPatch is an ongoing project and new methods will continue to be added.
Heart rate variability, has been known to indirectly reflect functioning of the autonomous nervous system, among other things. There have been several detailed studies about HRV and its interpretation for use in a wide variety of applications. Some of the most prominent ones are:
Traditional devices rely on software algorithms to detect R peaks in the ECG signal, this limits the accuracy of the intervals between R peaks due to processing speed, latency and so on. The primary differentiating component in HeartyPatch is the MAX30003 chip from Maxim. However, the MAX30003 AFE used in the HeartyPatch has a built-in R-R detection algorithm using the popular Pan-Tompkins method. We have measured this to be very precise, on the order of a few milliseconds. This feature makes it ideal for heart rate variability analysis. This also off-loads the R-R detection algorithm from the main microcontroller, allowing it to be used for user-defined programs. This allows HeartyPatch to do most of the processing on the device itself, without sending it to a cloud for analysis. HeartyPatch provides a continuous plot of the Poincare plot as well as distribution of R-R intervals.
HeartyPatch comes pre-loaded with firmware that can:
Re-programming the onboard ESP32 is required only if you wish to use custom firmware for custom applications.
Again, HeartyPatch is an ongoing project and additional software/functionality is in development.
A $50 open source smartphone based on the Raspberry Pi Zero
An Earth-friendly way to easily upgrade and fix your own computer
An open-source, multi-parameter, full fledged human body vital sign monitoring HAT for Raspberry Pi as well as standalone use.