Heart beat detection and segmentation

This is a quick description of our early detection of heart failure algorithm for feature identification and segmentation.

1) A sound file consists basically of a float array and sampling rate.
2) One normalizes this sound in amplitude (but we can do without) and in sampling rate (2000 times per seconds)
3) Contrary to what is done in Physionet 2016, there is no filtering or elimination of “spikes”.
4) The cardiac rhythm is detected through events that are roughly the S1 events (FindBeats.java). This is not trivial as there are noises, spikes, “plops”, respiration, abnormal heart sounds, human speaking and in one case even dog barks! Basically there are two ways to achieve the beat detection, one is to find the frequency of the envelope of the signal, the other is to make a FFT. But both approaches are subjectives as they imply to decide beforehand what is an acceptable heart rate. What does not help is that heart rate can roughly go from 30Hz to 250Hz, so as we can detect three or four main frequencies between 30Hz to 250Hz in the FFT of the sound file, which one is the correct one? One cannot decide in advance. What we do is having a two steps and indirect approach that fails quickly if we make a wrong guess. So it could converge quickly toward a result:
* In the first step we try to estimate the heart beat duration independently of the heart rate (because there are noise sources that make the heart rate counting unreliable).
* We use this first estimation to make a better guess of the heart rate in the second step. If the guess gives obviously wrong results we change the threshold for detecting heart rate.

4-1) One seeks to find the optimal detection level (threshold) enabling to detect heart beat rate in this sound file.
4-1-1) We create a window on the sound file, that is very low-pass filtered (currently 200Hz)
4-1-2) We register times of downward passage through the current treshold level
4-1-3) If a convincing heart rate (not necessarily exact) is obtained, the level of detection is kept, otherwise it is lowered and the procedure is repeated.
4-2) With this threshold, we will try to obtain a cardiac rhythm with roughly the same procedure as above but with a window filtered at 1000Hz and in a wide interval around the heart rate obtained in the first step.
4-2-1) We create a sound window which is low-pass filtered (currently 1000Hz)
4-2-2) A downward passage through the threshold is detected, ad hoc margins are imposed between the start and the end of the event.
4-2-3) If a convincing cardiac rhythm is obtained, the heart rate is kept, otherwise the level of detection is lowered and we start again

So we have here an approach which is very progressive, yet delivers results in a short time. It is also quite insensitive to sound events that could derail the heart beat counting, as the first step provide some good indications of the area where is the real heart beat, one spike makes the closer S1 perhaps not detected, but we gain a good idea of the heart duration.
5) In addition to S1, many other events are detected. A priori we assume that these are the other events S2, S3, S4. Even numbers going higher than four, it is useful for unusual heart sound classification.
6) These two Sx events detections are brought closer together
6-1) The list of S1 events is made more reliable, which makes it possible to deduce the events S2, S3, S4 (Segmentation.java).
6-2) A signature of the heart beat is computed to acknowledging there are more in one heart beat than its time of arrival and duration. We tested several schemes, and decided to use a Huffman compression of the heart beat. We had also the idea to use this to yet another kind of feature detection without training but it is not implemented at the moment because of lack of resources.
7) From there one can either train a HMM, or classify. We go out of cardiac specific, it’s just an HMM
8) One interprets the classification made by the HMM, with a score of similarity and comments on the events Sx

Another difference with Physionet 2016 is that they add a second approach, which is the variability of cardiac rhythm, they calculate a lot of indicators, but the HMM does it more accurately, with a probability of Transition from internal to internal state that is explained, instead of being a scalar computed over the whole file.

In a future version, I would like to work on the frequency peaks that can be identified with an FFT.
The general idea would be to look for what would be “apparently” harmonics but which in fact would indicate echoes on different paths.

Heart sound files analysis

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:

https://en.wikipedia.org/wiki/Heart_rate_variability‚Äč

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.