Hello Bipin
To create the segments from your 1-D ECG signal, you don’t need to use the MATLAB function “bsxfun”. The function is used when binary functions need to applied to two arrays in an element-wise fashion. In your use-case, only the correct indices need to be calculated. Please follow the code snippet below to create the segments with the required size and overlap:
% Generate a random signal of size 1-by-N
N = 10000; % Length of the signal
signal = randn(1, N);
% Define segment size and overlap
segmentSize = 2000;
overlap = 0.90;
stepSize = round(segmentSize * (1 - overlap));
% Create overlapped segments
numSegments = ceil((N - segmentSize) / stepSize) + 1;
segments = zeros(segmentSize, numSegments);
for i = 1:numSegments
startIdx = (i-1) * stepSize + 1;
endIdx = startIdx + segmentSize - 1;
if endIdx > N
endIdx = N;
startIdx = endIdx - segmentSize + 1;
end
segments(:, i) = signal(startIdx:endIdx);
end
The shape of your output will be dependent on various factors like network architecture, task, output layer. For example, in tasks like signal source separation (as in the provided MathWorks example), it is common to design the network such that the input and output segment sizes are the same. In a classification task, the output shape would differ and be a single output. You need to ascertain the nature of the output based on the deep learning network used.
Assuming that the output is received as overlapped segments with the same format as the input segments, the non-overlapped signal can be reconstructed using the following code snippet:
% Placeholder for deep learning model output (assuming same size as input)
outputSegments = segments; % This should be the output from your deep learning model
% Reconstruct the signal from the overlapped output segments
reconstructedSignal = zeros(1, N);
weighting = zeros(1, N);
for i = 1:numSegments
startIdx = (i-1) * stepSize + 1;
endIdx = startIdx + segmentSize - 1;
if endIdx > N
endIdx = N;
startIdx = endIdx - segmentSize + 1;
end
reconstructedSignal(startIdx:endIdx) = reconstructedSignal(startIdx:endIdx) + outputSegments(:, i)';
weighting(startIdx:endIdx) = weighting(startIdx:endIdx) + 1;
end
reconstructedSignal = reconstructedSignal ./ weighting;
% Plot original and reconstructed signals for comparison
figure;
subplot(2, 1, 1);
plot(signal);
title('Original Signal');
subplot(2, 1, 2);
plot(reconstructedSignal);
title('Reconstructed Signal');
In the above code snippet, the following operations have been performed to reconstruct the non-overlapped signal:
- Summing Overlapping Segments: For each segment, add its values to the corresponding indices in the reconstructed signal.
- Weighting: Keep track of how many segments contribute to each index in the reconstructed signal.
- Averaging: Divide the summed values by the number of contributing segments to get the final reconstructed signal.
The above steps should be helpful in constructing overlapped segments and reconstructing non-overlapped signals from the output.
I hope you find the above explanation and suggestions useful!
