How to identify timestamps (indices) of multiple threshold crossings in continuous data

Question

From an audio stream vector in Matlab I am trying to identify the time of onset and finish of audible events that occur multiple times within the time series data.

I am very much a novice with Matlab, but I have written code which identifies the peak and location of the event, however, I need to get the start of the event relative to a user defined threshold which occurs several tens of milliseconds before the peak.

Here is the code I am using at the moment:

function [emg] = calcPeaks(EMG, thresh)
%Rectify and downsample data 

emg = resample(abs(hilbert(EMG)),1000,10000); 
%Low Pass Filter 
[b,a]=butter(8,0.01,'low');
emg=filtfilt(b,a,emg);

%Plot the processed vector 
plot (emg); hold on;

%Find maximum for each Peak and Location
[pks,locs] = findpeaks(emg(1:end-2000),'minpeakheight',thresh);

plot(locs, emg(locs), 'ko'); hold on;

%Find Crossings above threshold
[FindCross] = find(emg(1:end-2000) > thresh);
[Gaps] = find(diff(FindCross)> thresh);
plot(FindCross, emg(FindCross), 'ro');
plot(Gaps, emg(Gaps), 'bo');

I tried to post an image of the datat but I don't have enough reputation :(

Answer 1

This should be getting you close to what you want (although same thresh for both is probably not what you intend):

[FindCross] = find(emg(1:end-2000) > thresh); %thresh is your minimum volume
[Gaps] = find(diff(FindCross)> thresh2); % thresh2 is related to the timing of events

However, note that this only finds gaps between areas which are above your noise level threshold, so won't locate the first event (presuming at start of data you are below the threshold).

A simple way to do this sort of thing is to threshold and then use diff to look for rising and falling edges in the thresholded data.

emg2 = emg > thresh; %emg2 = 1 and 0 for event / non event
demg = diff(emg2); % contains 0, -1, 1
rise = find(demg>0)+1; %+1 because of how diff works
fall = find(demg<0);

rise should then contain the positions where emg goes from below threshold to above threshold. If the data is sufficiently noisy, this could contain false positives, so you may want to then filter those results with additional criteria - e.g. check that after the rise the data stays above threshold for some minimum period.

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The problem with doing it by the method you're using to find gaps is the following. Presume your data looks like this, where 0 is below threshold and 1 above threshold: 000111000111000. That is, our first event starts at index 4 and finishes at index 6, and the second starts at index 10 and ends at index 12.

emgT = find(emg > thresh);

This finds all the places where our data = 1, so emgT = [4,5,6,10,11,12]

emgD = diff(emgT); 

This takes the difference between emgT(n+1), and emgT(n) - since there's no n+1 for the final datapoint, the output is one smaller than emgT. Our output is [1 1 4 1 1] - that is, it will find the gap between the two events, but not the gap between the start of the file and the first event, or the gap between the last event and the end of the file.