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Reducing the recording signal length undoubtedly saves the precious memory space for BMS.
We found 7 min as the optimal signal length for classification, while maximizing specificity (wake detection).
While these constraints guarantee the existence of the inverse filters, their form is unconstrained and could be signal length dependent.
The present numerical illustrations prove that the computed error spectra are negligible with only a small fraction between 14 and 12 of the full signal length.
Stabilisation is considered as achieved if the successive differences of parameter estimations as a function of the signal length are within the prescribed accuracy threshold, such that the shape spectrum is indistinguishable from random background noise.
The theoretical predictions for all the spectral parameters (positions, widths, heights and phases of each resonance) as well as the shape of the computed spectrum are considered to be acceptable only if they stabilise while varying the fractions of the total signal length in the two variants of the FPT that converge inside and outside the unit circle.
A single realization has input signal length of 219 samples.
Signal length and noise standard deviation determine universal threshold.
The window length is 1/4 of the signal length.
The window length for (a) and (b) is 1/4 of signal length and that for (c) and (d) is 1/8 of signal length.
Here, the output signal length L o and input signal length L i are related by L o = (L i − 1).
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CEO of Professional Science Editing for Scientists @ prosciediting.com