CODEWITHSUNDEEP
matlabsignalsnoise
Tom Alderson
Tom Alderson

Reputation: 31

Best way to extract neuronal spike times from a noisy signal / voltage meaurement

I'm a neuroscientist, and not a very good one. My colleague has kindly provided me with a noisy voltage measurements of the PY neuron of the Stomatogastric Ganglion of the lobster.

The activity of this neuron is characterised by a slow depolarised plateaux with fast spikes on top (a burst).

Both idealised and noisy versions are presented here for you to peruse at your leisure.

It's my job to extract the spike times from the noisy signal but this is so far beyond my experience level I have no idea where to begin. Fortunately, I am a total ninja at Matlab.

Could someone kindly provide me with the name of the procedure, filter or smoothing function which is best suited for this task. Or even the appropriate forum to ask such an asinine question.

Presumably, it needs to increase the signal to noise ratio? The problem here seems to be determining the difference between noise and a bona fide spike as the margin between the two is quite small.

UPDATE: 02/07/2013

I have tried the following filters in Matlab with mixed results. It's still very hard to say what is noise and what is a spike.

Lowpass Butterworth filter, median filter, gaussian, moving weighted window, moving average filter, smooth, sgolay filter.

Upvotes: 3

Views: 965

Answers (1)

marsei
marsei

Reputation: 7751

This may not be an adequate response for stackoverflow - but one way of increasing a signal to noise ratio in your case is to average parts of the signal.

  1. low pass your signal to remove noise (and spikes), and find the minima of the filtered signal (from your image, one minimum every 600 data points). Keep the indexes of each minimum,
  2. on the noisy signal, for each minimum index, select the consecutive 700 data points. If you have 50 minima, you should have a 50 by 700 matrix,
  3. average your matrix. You should have a 1 by 700 vector.

By averaging parts of the signal (minimum-locked potentials), you will take advantage of two properties: noise is zero-mean (well, it should be), and the signal of interest is repetitive. The first will therefore decrease as you pile up potentials, and the second will increase. With this process however, you will lose the spike times for each slow wave figure, but at least have them for blocks of 50 minima.

This technique is known in neuroscience as event-related potential (http://en.wikipedia.org/wiki/Event-related_potential). It may not fit perfectly your signal, or the result may not give nice spikes, but you may extract the spike times for some periods of interest (given the nature of your signal, I would say that you would need 5 or 10 potentials to see an emerging mean activity).

There are some toolboxes that do part of the job (but I would program it myself given the complexity of the task). These are eeglab or fieldtrip. They have a bunch of filter/decomposition options too, as well as some statistical features.

Upvotes: 1

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