Detect beginning and end of inhalation in respiration signal
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This signal was collected from a respiratory effort belt transducer, sampled during speech production (fs = 1 kHz). I have many trials of data to process, so I hoped to do this semi-automatically and correct where needed.
I'd like to analyse the duration of speech-related inhalation events. At first, I thought to use findpeaks() to mark local minima and maxima. I planned to measure the time elapsed between paired troughs (i.e., beginning of pre-speech inhalation) and peaks (i.e., end of pre-speech inhalation/beginning of speech exhalation). Below is how I used findpeaks(), which in this case worked great.
But I'm now realizing that there are not always equal numbers of mini/maxima, and even then, pairing them appropriately is not always as straight-ahead as in the above example (e.g., two peaks but only one clear trough - which is the matching peak?). Moreover, sometimes this happens:
For above, I would choose the later, if less prominent, trough as my inhalation-initiation point.
Rather than simply measuring between minima/maxima, I think a better way to detect inhalations would be to look for their characteristic profile: smooth, steep, positive lines falling containing at least x samples. I have tried findchangepts() but the input arguments don't include parameters such as sign or slope.
Intuitively, I imagine a window moving in steps, testing the fit of an inhalation model, so long as the lengths of inhalations are free to vary (assuming a minimum threshold is reached).
Or, maybe all I need to look for are vectors of rising data points that meet a minimum number of elements.
I'm new to signal processing and I'm not sure where to look next - can anyone offer any keywords?
jonas on 20 May 2018
Edited: jonas on 20 May 2018
Based on the data provided in the comments, here's one way to use findchangepts to locate continuous steep slopes. As previously stated, I'm not an expert in signal processing so I'm sure there are more accurate methods, but this would be one option.
You can play with 'MinThreshold' to find a more suitable threshold. Reducing the value gives you more changepts.
clear all;close all
inhale = [103; 4284; 10599; 16892; 20776; 23713];
exhale = [708; 4916; 11244; 17623; 21019; 24259];
loopX = max([numel(inhale) numel(exhale)]);
for i = 1:loopX
title('changepoints, calculated with minthreshold=0.5')
%remove changepts with negative slope
thres=1e-4; %set minimum slope
title('changepoints with slope > threshold')
%remove changepts between start and end of inhalation process
title('removed intermediate points')
More Answers (1)
Image Analyst on 19 May 2018
Edited: Image Analyst on 19 May 2018
I'd try findpeaks(). It has tons of options (many of which are tricky) to help you find only the peaks you need. To find valleys, simply pass the negative of the signal into findpeaks.
Attach your signal data if you need more help - make it easy for people to help you, not hard.