I started with Fourier Analysis and now moving to a wavelet analysis. I will also probably be doing some system identification and time series analysis before I am done with the effort.
So far I will recap the effort as most of this was published in a private forum:
The data has come from a ***.005 file of a Philips Respironics Series 60 APAP. Initially, I was interested in characterizing the periodic breathing that I observe at various times throughout my sleep. Such a pattern is shown:
I started by using FFT which is very good for identifying dominant frequencies. This produced the following information:
My interpretation is that the largest peak is my breathing frequency (near 15 cycles/minute), the peaks to the right that appear to be harmonics and the peak I am interested in is the small one near 2 cycles/minute.
Since I was interested in low frequency information, I zoomed to that region:
What surprises me is that the low frequency peak is so small and indicates that periodic breathing is not as prevalent in the data I thought it was.
Below is a spectrogram of the data. The horizontal scale is time and the vertical scale is frequency. The lowest dark line horizontally across the spectrogram corresponds to my breathing frequency:
What is noticeable is how the breathing frequency is disrupted by the event clusters. However the definition of the spectrogram is not fine enough for me to draw any conclusions at this point.
I did do some analysis of a few cycles but I don't think that it gave more information than I already had.
So now I am moving into developing wavelet forms but am not yet at point where I want to publish that yet.