Question: You are given two sets of time - series data collected from older healthy adults ( subj _ A & subj _ B ) .

You are given two sets of time-series data collected from older healthy adults (subj_A & subj_B).Each set contains four curated (i.e. pre-processed) time-series datasets representing continuous de-meaned contemporaneous (i.e. synchronized for each set) samples every 0.5 s.
(1) De-meaned systemic Mean Arterial Blood Pressure (extension .bp) measured at the index finger via photo-plethysmography (in mmHg), which is the average value of highly-sampled arterial blood pressure over each cardiac cycle (R-R interval of the respective electrocardiogram), properly pre-processed to generate evenly sampled time-series data.
(2) De-meaned Mean Cerebral Blood Flow Velocity (extension .fv)) measured at the right middle cerebral artery (MCA) via transcranial Doppler (in cm/s), which is the average value of highly-sampled cerebral blood flow velocity at the MCA over each cardiac cycle (R-R interval), properly pre-processed to generate evenly sampled time-series data.
(3) De-meaned Heart Rate (extension .hr) defined as the inverse of the R-R interval of each cardiac cycle (in beats/min), which is extracted from the highly sampled electrocardiogram (or equivalently from the arterial blood pressure signal) and properly pre-processed to generate evenly sampled time-series data.
(4) De-meaned end-tidal CO2(extension .co2) measured via capnography using a nasal cannula (in mmHg), which is extracted at each breath from the highly sampled capnogram (peak value at end of exhalation) and properly pre-processed to generate evenly sampled time-series data
The applied pre-processing of these time-series datasets includes (a) high-pass filtering above 0.005 Hz (which includes de-meaning) and (b) clipping of occasional outliers.
You are asked to:
(4) Compute the cross-correlation function estimates (using max lag of 100 samples) and the respective cross-spectra between (a) bp & fv,(b) bp & hr,(c) co2 & fv,(d) co2 & hr for each subject. Compare the results for the two subjects.
This is MATLAB

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