Interpolate the time stamped patient data

问题

Question: I am dealing with a patient data, where parameters are recorded at different sampling frequency, so having a different time stamp.

I want to create a matrix where data is interpolated by "Last known value" till the new original value changes in the time. So at the end I have uniform matrix where each parameter have values at every time stamp.

Data is in following format:

Time              Hear Rate(Variable)         
18:00:00 PM       74 
18:02:00 PM       75
18:04:00 PM       85     
18:06:00 PM       71        
18:08:00 PM       79        
18:10:00 PM       72  

Time              Blood Press. (Variable)         
18:01:00 PM       100
18:05:00 PM       120
18:09:00 PM       121

Target :

Time              Hear Rate(Variable)    Blood Press.    
18:00:00 PM       74                      NaN
18:01:00 PM       74                      100
18:02:00 PM       75                      100 
18:03:00 PM       75                      100
18:04:00 PM       85                      100 
18:05:00 PM       85                      120
18:06:00 PM       71                      120
18:07:00 PM       71                      120 
18:08:00 PM       79                      120
18:09:00 PM       79                      121
18:10:00 PM       72                      121

The Interpolated data in the missing place should be the previous value of the known event and should remain same until next change occurs.

I am currently referring the following thread from MATLAB User Forum

http://www.mathworks.com/matlabcentral/answers/101237-how-can-i-do-1-d-interpolation-with-interp1-to-find-the-nearest-value-to-the-left-of-the-point-i-e

回答1:

You're essentially trying to do a zero-order hold as you've seen from the link you provided.

Let's call the first set of times t_hr (a cell array of strings) and the second set t_bp (also a cell array of strings). Then call the heart rates hr and the blood pressures bp.

Build a new cell array t by combining t_hr and t_bp:

t = [t_hr; t_bp];

Fill in hr and bp with some NaNs to make them the same length as t. In t, the first part of the vector corresponds to times we know about for hr, and the second half corresponds to bp. Use that knowledge accordingly:

hr = [hr; nan(length(t) - length(hr),1)];
bp = [nan(length(t) - length(bp),1); bp];

Now you've got three vectors: t (okay, that's actually a cell array); hr and bp, and the elements of hr and bp correspond to elements in t, including the NaNs:

t = 
'18:00:00 PM'
'18:02:00 PM'
'18:04:00 PM'
'18:06:00 PM'
'18:08:00 PM'
'18:10:00 PM'
'18:01:00 PM'
'18:05:00 PM'
'18:09:00 PM'

hr =
74
75
85
71
79
72
NaN
NaN
NaN

bp =
NaN
NaN
NaN
NaN
NaN
NaN
100
120
121

Now, we can sort t:

[t_sorted, idx] = sort(t);

idx contains the indices of t that got moved around to form t_sorted. In this case, idx == [1 7 2 3 8 4 5 9 6].'. We can use that to sort bp and hr:

 hr_sorted = hr(idx)
 hr_sorted =
 74
 NaN
 75
 85
 NaN
 71
 79
 NaN
 72

 bp_sorted = bp(idx)
 bp_sorted =
 NaN
 100
 NaN
 NaN
 120
 NaN
 NaN
 121
 NaN

Then, apply the zero-order hold:

for ii = 2:length(t)
    if isnan(hr_sorted(ii))
        hr_sorted(ii) = hr_sorted(ii-1);
    end
    if isnan(bp_sorted(ii))
        bp_sorted(ii) = bp_sorted(ii-1);
    end
end

and your final vectors become:

hr_sorted =
74
74
75
85
85
71
79
79
72

bp_sorted =
NaN
100
100
100
120
120
120
121
121

Note that your target answer has 11 different times in it, but you only supplied nine. (It's missing 18:03 and 18:07.) You can easily extend this answer by adding extra NaNs to hr and bp, and making t a cell array comprising of, say, t_hr, t_bp, and a cell array t_missing ={'18:03:00 PM', '18:07:00 PM'}.

来源：https://stackoverflow.com/questions/24083490/interpolate-the-time-stamped-patient-data

标签

matlab

time-series

interpolation