type getmask.m trainDs = transform(trainDs, @getmask); testDs = transform(testDs, @getmask);
function outputCell = getmask(inputCell) %GETMASK Convert region labels to a mask of labels of size equal to the %size of the input ECG signal. % % inputCell is a two-element cell array containing an ECG signal vector % and a table of region labels. % % outputCell is a two-element cell array containing the ECG signal vector % and a categorical label vector mask of the same length as the signal. % Copyright 2020 The MathWorks, Inc. sig = inputCell{1}; roiTable = inputCell{2}; L = length(sig); M = signalMask(roiTable); % Get categorical mask and give priority to QRS regions when there is overlap mask = catmask(M,L,'OverlapAction','prioritizeByList','PriorityList',[2 1 3]); % Set missing values to "n/a" mask(ismissing(mask)) = "n/a"; outputCell = {sig,mask}; end
This example shows how to segment human electrocardiogram (ECG) signals using recurrent deep learning networks and time-frequency analysis.
openExample('deeplearning_shared/WaveformSegmentationUsingDeepLearningExample')
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