1. BATCH ANALYSIS TOOL FOR E4 DATA
Peter de Looff Matthijs Noordzij
(Radboud Universiteit/De Borg) (Universiteit Twente)

2. E4 DATA – EMPATICA WEBSITE

3. ANOTHER EXAMPLE

4. WATCH OUT!

5. THESE FIGURES SCALE ENORMOUSLY
THESE FIGURES SCALE ENORMOUSLY! (You cannot avoid doing proper signal analysis and parameter extraction yourself somehow!)

6. E4 DATA ANALYSIS OF DOWNLOADED .CSV FILES:
From the Empatica website:
“For systematic Skin Conductance analysis from our EDA files we recommend LEDALAB (for MatLab). You may need to adjust skin conductance response (SCR) thresholds to account for lower magnitudes in typical wrist data. For heart rate variability (HRV) analysis we recommend Kubios (also for MatLab). Kubios takes in instantaneous heart rate data and allows you to correct errant beats and conduct HRV analysis respectively. Kubios allows you to overlay our IBI files on the BVP data to assist in error correction.”

7. E4 DATA ANALYSIS - CONTINUED
‘If you prefer there are a number of signal processing toolkits available in the free software domain. Python's SciPy libraries have outstanding signal processing and data visualization capabilities.’ New option (in python and web-based):

8. EDA EXPLORER
If you use our tool, please cite this article by Sara Taylor and Natasha Jacques. They are responsible for the artifact and peak detection scripts in the tool. In addition, you could also cite the authors of the RHRV package (later in this presentation)

9. DRAWBACKS OF THE WEB OPTION
A lot of clicking – error prone – time intensive
Max 6.5 hours?
If you want to change one little thing (e.g. treshhold for SCR’s) you have to do everything manually
No HR data analysis
You are left with large Excel files which require (tricky) further computations to get to a useful report.

10. NEXT STEPS Allow for batch processing of all data collected by the E4
Batch means that you can re-run your analysis basically with one command
Next to speed/ accuracy advantages this also contributes to reproducible research.
We use only open source software (no Matlab required):
Python and R
We rely on EDA-explorer (From MIT) and RHRV (an extensive HR analysis tool in R)
We have started out with one use case in mind.

11. USE CASE
One E4 file per day for a client. You could also use sessions instead of days.
You want a daily report on the data, where the data is interpreted relative to the “physiological personality” of that client.
This means you want to know (for example) whether the physiological values of a particular day could be called unusually high or low given what you already know about what this person typically shows as physiological values.

12. Batch RHRV package

13. RHRV Batch-processing HRV is a bit tricky. ECG is the gold-standard.
With the Empatica E4, we only have a ppg sensor.
The E4 does a good job at only detecting the ‘pure’ inter-beat-interval (IBI-files).
It doesn’t mean we can’t use it, it means that we have artefacts, and interpret the results accordingly.

14. RHRV
To get an indication of the amount of artefacts we made an Hrplots file in the directory.
With a standard ECG you get reasonably clean data.

15. RHRV
This is an example of a fair amount of artefacts, it represents the time between good detected beats. In an ‘ideal’ resting ECG situation with a HR of 60, all beats would be approximately 1 second apart. As you can see here there are a lot of wrong beats. Up to 400 seconds in the first minutes of the recording. On the next slide is an example of a better measure with the E4.
In this case there were more than 400 sec between detected beats. This means that the E4 was not able to get good beat for more than 400 sec

16. RHRV
Here you see that there are less artefacts in comparison with the previous slide.
To get a feel for the amount of artefacts we made an hrv.art.index variable in the _datasummary file.
The variable contains the amount of beats detected/ the session duration in seconds. So, be careful with interpretation of higher vs lower heart rates. The IBI is shorter there because the HR is higher. For the first plot the hrv.art.index is .23, for the second it is .84. So, more beats are detected in the second plot.
In this plot the maximum nr of seconds between two beats is 13 seconds

17. RHRV
RHRV parameters are based on the good beats that are provided by the Empatica algorithm. So, there is variation in the amount of beats used depending on the amount of artefacts.

18. IMPLEMENTATION – HOW DOES IT WORK?
You need to adjust 2 files: batch_edaexplorer_template_v3 and defines_template
All the scriptfiles you need must be in one folder.

19. WHAT DO YOU NEED TO DO THIS YOURSELF?
Anaconda 2
R / R studio
A number of tweaks additional libraries related to Python (see document) and R:
install.packages("RHRV")
install.packages("plyr")
install.packages("zoo")
After the intital set-up phase you only need to place the E4 zipfiles in the correct folder!
The initial set-up phase does require you to do a little Python and R (but Google/ stackoverflow are your friend  ).

20. WHAT DO YOU NEED TO DO THIS YOURSELF?
Live demo
Show the file structure. Mention what must go in what directory.
Explain the defines and batch files. All directories.
Explain the r script.
Mention the timezone.
pythonTroubleshooting Python/R collaboration.