Plotting high frequency EDF data from timetable

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Using 2022a
Since 2020b the old FEX function edfread() was incorporated into matlab in the signal processing toolbox (native edfread). The function reads the data out in a timetable which I am having trouble manipulating/plotting:
For a record sampled at 500 Hz (e.g, EEG, MEG data):
a = edfread('test.edf')
a =
9500×128 timetable
Record Time C1 C2
0.632 sec {50×1 double} {50×1 double}
0.732 sec {50×1 double} {50×1 double}
0.832 sec {50×1 double} {50×1 double}
...
If this were sampled at 1024 Hz, the filed would contain 128x1 doubles corresponding to each Record Time increment (which would be 0.125 sec).
I'm having trouble finding an intuitive way to manipulate and plot these massive files.
I've tried:
  • I can write a code to "unpack" all of the 50x1 cells into a single vector, but this is very resource intensive.
  • Using 'DataRecordOutputType', 'vector' does not seem to change the output (I still get a timetable)
  • using table2cell(a) or table2array(a) works to get it out of timetable format, but leaves me with a header-less cell/array of cells and the "unpacking" problem still exists.

Accepted Answer

Star Strider
Star Strider on 18 Apr 2022
I do not have your data, however using the MATLAB sample data, extracting the entire record of each variable may not be difficult
tt = edfread('example.edf')
tt = 6×2 timetable
Record Time ECG ECG2 ___________ _______________ _______________ 0 sec {1280×1 double} {1280×1 double} 10 sec {1280×1 double} {1280×1 double} 20 sec {1280×1 double} {1280×1 double} 30 sec {1280×1 double} {1280×1 double} 40 sec {1280×1 double} {1280×1 double} 50 sec {1280×1 double} {1280×1 double}
RecTime = seconds(tt.('Record Time')); % Get Time Variable
ECGv = cat(1,tt.ECG{:}); % Concatenate The 'ECG' Variable
Ts = numel(tt.ECG{1})/mean(diff(RecTime)); % Sampling Intervals (Samples/Second)
Time = linspace(0, numel(ECGv)-1, numel(ECGv)).'/Ts; % Create Continuous Time Vector
figure % Plot Results
plot(Time, ECGv)
grid
xlabel('Time')
ylabel('Amplitude')
If I understand correctly what you want to do (and I may not), ‘unpacking’ the data is straightforward. This is somewhat facilitated by duration arrays having a regular sampling interval. It may be necessary to scale the ‘Time’ variable appropriately with your data.
.
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More Answers (1)

Campion Loong
Campion Loong on 18 May 2022
The stackedplot visualization for timetable is built for exactly this kind of use cases
edfData = edfread('example.edf');
% Unpack the ECG signals into a timetable of regularly sampled data
% (This assumes the 1280 data-points are sampled evenly between adjacent 'Record Time')
ECG = cat(1,edfData.ECG{:});
ECG2 = cat(1,edfData.ECG2{:});
tt = timetable(ECG, ECG2, 'TimeStep', seconds(10)/1280)
tt = 7680×2 timetable
Time ECG ECG2 _____________ ________ __________ 0 sec -0.11262 -0.006995 0.0078125 sec -0.0915 -0.006995 0.015625 sec -0.07742 -0.006995 0.023438 sec -0.04222 -5.005e-06 0.03125 sec -0.00702 -5.005e-06 0.039062 sec -0.00702 -0.006995 0.046875 sec 0.03522 -5.005e-06 0.054688 sec 0.07042 0.006985 0.0625 sec 0.0845 -5.005e-06 0.070312 sec 0.1197 -0.006995 0.078125 sec 0.13378 -0.006995 0.085938 sec 0.1197 -5.005e-06 0.09375 sec 0.07042 0.013975 0.10156 sec 0.0493 -0.013985 0.10938 sec -0.03518 0.006985 0.11719 sec -0.07038 0.020965
% Simply visualize both ECG series in one stackedplot
% (You can even scrub both series together with synchronized datatip to explore the signals further)
stackedplot(tt)

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