By Fabian J. Theis

A complete advent to cutting edge tools within the box of biomedical sign research, overlaying either idea and perform.

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Extra resources for Biomedical Signal Analysis: Contemporary Methods and Applications

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However, the major drawback of the classical Fourier transform is its inability to deal with nonstationary signals. Since it considers the whole time domain, it misses the local changes of high-frequency components in the signal. In summary, it is assumed that the signal properties (amplitudes, frequency, and phases) will not change with time and will stay the same for the whole length of the window. To overcome these disadvantages, the short-time Fourier transform was proposed by Gabor in 1946 [88].

Thus, this new transform works by sweeping a short-time window over the time signal, and thus determines the frequency content in each considered time interval. The transform modulates the signal with a window function g(t). In this context ω and τ are the modulation and translation parameters. The window g(t) has a fixed time duration and a fixed frequency resolution. Although the frequency and time domains are different, when used to represent functions, they are linked: A precise information about time can be achieved only at the cost of some uncertainty about frequency, and vice versa.

Frequency resolution of the WT can easily be detected. The foundation of the WT is based on the scaling property of the Fourier transform. 22) with a > 0 being a continuous variable. A contraction in the time domain produces an expansion in the frequency domain, and vice versa. 3 illustrates the corresponding resolution cells in the time-frequency domain. The figure makes visual the underlying property of wavelets: they are localized in both time and frequency. The functions ejωt are 40 Chapter 2 perfectly localized at ω, they extend over all time; wavelets, on the other hand, that are not at a single frequency are limited to finite time.

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