Not much to show, but some news:
Sounds files have problems that I did not anticipated. What I was expecting from the analysis of Physionet 2016 submissions was noise, spikes, weird amplitude and similar distortions of the signal.
What I found was different, there is little noise while you filter it a bit, there are few spikes.
However sometimes the signal is biased (more negative values than positive), the signal also appears to have little in common with textbooks, I can easily detect S1 and S2 events, but it is difficult to find S3 and S4.
When you hear the sounds, half of them looks weird, I am not a cardiologist, but I find it difficult to find what I could hear in a “textbook” heart sound.
This makes me think again about the Physionet 2016, successful submissions where mainly about heavily filtering, dealing with spikes with sophisticated algorithms and finding characteristics (features in ML slang) that encompass the whole file such as RR variability as in:
Clearly my approach is different, I focus on what identify a heart beat, which is entirely new. But I still plan to implement the RR variability analysis and tied it to my HMM classifier which will become quite hybrid in the process.