How to split time series array into subarrays according to sampling rate?

Hi, I have a data acquired from ECG sensor sampled at 128 sps I want to make subarrays each contains 256 sample?

Answer 1

[Krithika A]

sps = 128;
n = 768;   % 128*6 = 768, so a 6s long signal with 128 sps
x = randn(1,n);

y = reshape(x, [], sps*2);

[Phoenix98]

Hi, Thanks for your answer,I tested it and it works but ECG data is corrupt when it splitted

[Krithika A]

Can you give me specifics of the corruption? Like a plot of the corrupt data?

[Guillaume]

More specifically, reshape will never change any of the data in the matrix/vector it reshapes. So if the data is corrupt, it is because it was corrupt to start with.

So indeed, you need to be more specific about that corruption.

[Phoenix98]

Hi,

This is the plot of the first 256 samples before reshaping

and This is after reshaping

[Guillaume]

The problem is most likely with the way you plot your data (which you haven't shown). If you plot a matrix, you should get more than one plot.

[Phoenix98]

Hi, I have 12800 samples array, and I split them into 256 sample subarrays, so I have a matrix of 50 subarrays I plot only the first array in the matrix.

[Phoenix98]

Hi, I Test reshape(x,[],50) and it works well instead of reshape(x,[],256)

[Guillaume]

It should be indeed

reshape(x, [], 50)

to divide the data into 50 columns or even better:

reshape(x, 256, [])

to split the data into columns of 256 elements. In each case, each subarray ends up as a column.

[Krithika A]

Yes, Guillaume is 100% correct.

Glad you sorted it out.