Calculation of signal to noise ratio

Hello.

I was wondering if it was possible to calculate the SNR of a signal with LabView? I use a DAQ hardware and an accelometer to measure a force.

Is that being said, possible to calculate SNR by looking at the graph of spectrum or vague? When there is no g on the accelerometer, there is a signal with a little noise... and I can see that in the spectrum of frequecny all the way to the cut-off frequency. What is the background noise?

I'd appreciate any help!

Thank you!!

Abraham_E,

Thanks for the data.  Curiously, the data do not seem to care what language you were using when you got it.

The first data column appears as timing information.  The dt in who is 0.0005, which corresponds to a frequency of 2 kHz.  You said 400 Hz in one of your messages.  The discussion at the point 2 below corresponds to a frequency of 2 kHz sampling.

1. you are right that SINAD is not very useful. SINAD assumes that all the power of the signal is the dominant frequency component that is not quite true for ECG.

2. the size of the FFT of the signal shows the dominant signals at 50 and 100 Hz which is likely to be able to line with frequency. Al here also erase lines 200 and 250 Hz. You certainly do not want to calculate SNR under the assumption that the desired signal is 50 Hz.  The SINAD VI calculates the fundamental frequency than 49,97 Hz.

3. If you don't know the 50 Hz and harmonic components, there is no obvious lines in the spectrum remaining. I interpret this means you have the significant variation of heart rate data.  This means that the heart rate is modulated in frequency. Frequency modulation broadens the spectrum.

4. because there is not predominant in the spectrum, it is unlikely that frequency domain techniques will be worth any in the determination of the SNR.

So, what can you do? Looking at the data graphic, I think a peak signal to noise power ratio could be significant. There are little noise, even at spikes, if it has a few problems too.  I have divided the data set in 1 second segments. For heart rate in these data, each segment contains one or two beats.  In each segment, I found the maximum and minimum values.  I set the value from Ridge to Ridge for the segment of the difference. I also calculated the RMS value for the entire segment. Then the SNR is the signal from Ridge to Ridge divided by the RMS value. It is probably more accurate to use the RMS value of the QRS and exclude parts QRS of the RMS of noise, but to do both is algorithmically and by the much more difficult calculation. It would be also better force segments contain exactly one beat of each. Again, it is much more complicated.  I then calculated the average of the SNR segment values and call it mean SNR.

As I pointed out, there are some problems with the definition (peak to peak/RMS).  If you just want to compare signals within your lab to see which improves things, this should be good.  If you want to publish data and compare to other published results, you have to find how they define and measure SNR.

In the attached VI I read the file once and recorded values in an array as a default value. Then I removed the file played screws and just worked with the data. To try this with other data simply insert read VI file in the appropriate place.

Lynn

Tags: NI Software

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