Plotting high frequency EDF data from timetable

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.

Answer 1

[Star Strider]

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.

.

[Andrew Michalak]

Thank you! This is what I was looking for. It still poses an additional step but seems to work very quickly!

[Star Strider]

As always, my pleasure!

Answer 2

[Campion Loong]

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)