how to untilt my graphed data?

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cocopuffs 2015-4-20

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评论： Star Strider 2015-4-22

Hello guys.

So I had some respiratory flow rate vs. time. I needed to find to graph volume of ventilation vs. time so I took the running integral of the flow rate data. However, taking this running integral results in volume data that is tilted. I need to untilt the data before analyzing it but I was unsure how to do it. The help would be greatly appreciated. I used cumtrapz to integrate the flow rate data.

Also, I attached an image of the graph as it may help.

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Star Strider 2015-4-20

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It looks as though you are integrating a constant as well as your data. I don’t have your data so I can only suggest possible approaches.

The easiest approach would be to subtract the mean of ‘flow_data’ from ‘flow_data’ before you do the integration.

A more complicated approach would be to use a highpass or bandpass filter on the integrated data to eliminate the descending baseline.

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Star Strider 2015-4-20

The volumes that you integrated are most likely correct. (I suggested subtracting the mean because that can be due to instrumentation error.) Respiratory flow is not constant, and therefore volume would not be constant. (The only situation in which flow and volume are relatively constant is in a patient on a ventilator.) Respiratory flow and volume change with metabolic requirements (driven primarily by carbon dioxide — acid — load and secondarily by oxygen demand). It might be valuable to separate the frequency components in your data, and filters would do that.

If you want to filter them, I would start with the fft of your volume data to determine the frequency components, then use one or more bandpass filters to separate the components in each frequency band. I have described the generic design of digital filters here. For a bandpass filter, you have to supply two frequencies for the bandpass and bandstop arguments, but otherwise the design is the same. (I use bandstop frequencies that are 0.8 the lower bandpass frequency and 1.25 the higher bandpass frequency, with a 1 dB passband ripple and a 10 dB stopband ripple.)

One definitely valid reason to filter them would be to separate the low-frequency components from the higher-frequency components because all are important. I would definitely keep all the data.