. Setting the
bandwidth ... ...
Sampling Frequency (Fs)
For irregularlysampled data, the sampling frequency does not have straightforward meaning.
Here take a representative value as Fs_{med }=1/(median(RR_interval))
Nyquist frequency (Fn)
Let's call our effective Nyquist frequency Fn_{med}=Fs_{med} / 2
Intuitively 0.5*Fs .. but this will not 'cover' all frequencies as some components will be >Fs_{med
}.
In this demo, we conservatively limit the upper bandwidth of interest to 0.45*Fn_{med}
Plotting the Lomb Scargle PSD
Plots are over the bandwidth [0 ... 0.45*Fn_{med}] Hz
Why use an upper frequency cutoff for HRV?
Bandwith limiting (antialiasing filter for resampling)
Why detrend the HRV?
Good point. Maybe to deal with nonstationarity? See the many references in the Literature.
.
Using the web Gui demo (with example data provided: all freq in Hz) ... ...
Gaussian noise:
This is from MatLab randn() and has been lowpass filtered at 1Hz and
sampled back onto a
random irregular time axis: Fs_{med} is 4Hz
Set bandwidth [0.001 ... 1.0]: all the signal appears in the PSD
Set bandwidth [0.001 ... 0.5]: lowpass effect
Set bandwidth [0.01 ... 0.5]: bandpass effect
Normal:
Set bandwidth [0.001 ... 0.3]: low frequencies dominate (PSD uninteresting)
Set bandwidth [0.09 ... 0.3]: typical LF HF distribution
after detrending
Premature baby:
Note typical high heart rate
Set bandwidth [0.001 ... 0.8]: low frequencies dominate (PSD uninteresting)
Set bandwidth [0.03 ... 0.8]: a typical LF HF distribution
after detrending: resp rate high
Synthetic sines (3peak, 3peak plus Brownian 'drift', textbook 2peak
LF and HF)
Frequencies of interest: <0.01 ... 0.04 ... 0.15 ... 40 Hz
Can be used to illustrate detrending and bandwidth selection
