-https://stackoverflow.com/questions/10572939/connecting-two-points-in-a-3d-scatter-plot-in-python-and-matplotlib

和

-https://jakevdp.github.io/PythonDataScienceHandbook/04.12-three-Dimension-plotting.html

不要忘記在jupyter筆記本中使用“％matplotlib筆記本”以使其可旋轉。 ]]>

You can downsample a signal with scipy.signal.decimate or scipy.signal.resample or by reshaping the array as shown here.

But I don’t think 2048 samples is that much. Instead of downsampling it would be better to have a look at the 15 components and see which one of them are not important for predicting the target value and remove those components.

]]>here is the blog post that i am used for this task

https://blog.orikami.nl/diagnosing-myocardial-infarction-using-long-short-term-memory-networks-lstms-cedf5770a257

however i used the 2048 samples as my features and 15 channels as the time stamp for the data.

i want to know how to downsample the data when there is 15 channel involved in the data? ]]>

Thanks ]]>

thank you for your very interesting post. I was wondering what was the ‘gt’ parameter within the

calculate_crossings() funtion :

zero_crossing_indices = np.nonzero(np.diff(np.array(list_values) & gt; 0))[0]

Futhermore is it possible that it would be: ‘,’ instead of the: ‘;’ in the line above ]]>

There does seem to be some literature on using SVD or PCA for reducing triaxial signals to 1D. (See DOI: 10.1049/el.2018.6117 and DOI: 10.1190/1.1443068 ), but you could also try to calculate the resultant vector ( r = sqrt(x^2 + y^2 + z^2) ).

Whether you want the x-component of the frequency spectrum (frequency value) or the y-component (amplitude value) really depends on the specific problem at hand. Do the different classes of signals have a peak at the same frequency, but of different amplitude, or do they have peaks with the same amplitude but at different frequencies?

You can also try to use the PSD instead of the FFT, which has a more smooth frequency spectrum. ]]>