Refactoring and randomness test

The first usable versions of our features detection code ( were full of hardwired constants and heuristics.
The code has now been modularized, it spreads several methods with clean condition of method exit.

We were proud that our design was able to look at each beat and heart sound, which is a far greater achievement than what ML code does usually. Something really interesting was how we used compression to detect heart sounds features automatically in each beat.
Now we introduce something similar in spirit: Until now sometimes our code was unable to find the correct heart rate if the sound file was heavily polluted by noise. Now we use a simple statistical test akin to standard deviation, to test the randomness of beats distribution. If it is distributed at random, then it means our threshold is too low: We detect noise in addition to the signal.
This helped us to improve the guessing of the heart rate.

In an unrelated area, we also started to work on multi-HMM, which means detecting several, concurrent features. An idea that we toy with, would be to use our compression trick, at beat level, whereas now it is used at heart sound level. This is tricky and interesting in the context of a multi-HMM. Indeed it makes multi-HMM​ more similar to unsupervised ML algorithms.


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