Epileptic Seizure Detector (3)

I installed an accelerometer on the underside of the floorboard where my son sleeps to see if there is any chance of detecting him having an epileptic seizure by the vibrations induced in the floor.

I used the software for the seizure detector that I have been working with before (see earlier post).
The software logs data to an SD card in Comma-Separated-Values (CSV) format, recording the raw accelerometer reading, and the calculated spectrum once per second.  This left me with 26 MB of data to analyse after running it all night…..
I wrote a little script in Python that uses the matplotlib library to visualise it.   I create a 2 dimensional array where there is one column for each record in the file (ie once column per second).  The rows are the frequency bins from the fourier transform.  The values in the array are the amplitude of the spectral component from the fourier transform.
The idea is that I can look for periods where I have seen high levels of vibration at different frequencies to see if it could detect a seizure.  The results are shown below:
Here you can see the background noise of a few counts in the 1-7 Hz range.   The 13-15Hz signal is a mystery to me.  I wonder if it is the resonant frequency of our house?
Up to 170 sec is just me walking around the room – discouragingly little response – maybe something at about 10 Hz.  This is followed by me sitting still on the floorboard up to ~200 seconds (The 10 Hz signal disappears?)
The period at ~200 seconds is me stamping vigorously on the floorboard, to prove that the system is alive.
Unfortunately the period after 200 seconds is me lying on the floorboard shaking as vigorously as I could, and it is indistinguishable from the normal activity before 170 seconds.
So, I think attaching a simple IC accelerometer to a floorboard will not work – attaching it directly to the patient’s forearm looks very promising, but not the floorboard.
I am working on an audio breathing detector now as the next non-contact option….
The code to analyse the data and produce the above chart can be found on github.  It uses the excellent matplotlib scientific visualisation package.

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