598
R. B. Pachori and V. Gupta
dynamics. The median (MD) frequency measure is a universal index for muscle
force and fatigue. The peak frequency (PF) feature is used for identifying fatigue
state. The mean and total power features are also used for the identification of
fatigue. The first, second, and third spectral moments are alternative statistical
measures for fatigue identification. The frequency ratio is also used as a feature
to distinguish between rest and contraction states of muscles [39].
12.7.3 Time-Frequency Domain-Based Analysis
The time-frequency domain analysis is widely used for the analysis of nonstationary
signals because it provides time and frequency information together for a given signal. There are several time-frequency domain analysis methods such as short-time
Fourier transform (STFT), wavelet transform (WT), and Wigner-Ville distribution
(WVD) [40].
The time-frequency analysis is used for detecting spike-like epileptic patterns
in EEG signals because these patterns appear for a short time period or random
in most suspected epileptic EEG signals. Due to random occurrence of these
patterns, the frequency-domain analysis does not provide exact time information for
these patterns. The choice of epoch length is an important issue in time-frequency
domain analysis of EEG signal. The epoch lengths of 1–2 s duration are usually
recommended for time-frequency domain analysis of EEG signal. The epochs of
this time duration may provide stability in data features [30].
The time-frequency analysis of ECG signal identifies the typical pattern or wave.
The time-frequency analysis provides the separation of the mother’s and baby’s
ECG signals. It should be noted that the waveform of the fetal ECG signal is
analogous to adult ECG signal [30].
12.7.4 Other Methods
In real situations, most of the signals are nonlinear and nonstationary in nature. The
analysis of such type of signals is a tedious task. The predefined basis function
may fail to provide solutions. This problem can be overcome by an adaptive
or signal-dependent basis which is used for the representation of nonlinear and
nonstationary signals. A method named Hilbert-Huang transform (HHT) is an
adaptive and empirical method. HHT consists of two parts for signal analysis. One
of them is empirical mode decomposition (EMD) and the second one is Hilbert
spectral analysis (HSA). This method provides good results for time-frequencyenergy representations of many signals [41].
Another method for analysis of real signals is higher-order spectra (HOS). A
real signal most specifically a non-Gaussian signal can be decomposed into higherorder spectral functions in which each higher-order spectral function may contain
R. B. Pachori and V. Gupta
dynamics. The median (MD) frequency measure is a universal index for muscle
force and fatigue. The peak frequency (PF) feature is used for identifying fatigue
state. The mean and total power features are also used for the identification of
fatigue. The first, second, and third spectral moments are alternative statistical
measures for fatigue identification. The frequency ratio is also used as a feature
to distinguish between rest and contraction states of muscles [39].
12.7.3 Time-Frequency Domain-Based Analysis
The time-frequency domain analysis is widely used for the analysis of nonstationary
signals because it provides time and frequency information together for a given signal. There are several time-frequency domain analysis methods such as short-time
Fourier transform (STFT), wavelet transform (WT), and Wigner-Ville distribution
(WVD) [40].
The time-frequency analysis is used for detecting spike-like epileptic patterns
in EEG signals because these patterns appear for a short time period or random
in most suspected epileptic EEG signals. Due to random occurrence of these
patterns, the frequency-domain analysis does not provide exact time information for
these patterns. The choice of epoch length is an important issue in time-frequency
domain analysis of EEG signal. The epoch lengths of 1–2 s duration are usually
recommended for time-frequency domain analysis of EEG signal. The epochs of
this time duration may provide stability in data features [30].
The time-frequency analysis of ECG signal identifies the typical pattern or wave.
The time-frequency analysis provides the separation of the mother’s and baby’s
ECG signals. It should be noted that the waveform of the fetal ECG signal is
analogous to adult ECG signal [30].
12.7.4 Other Methods
In real situations, most of the signals are nonlinear and nonstationary in nature. The
analysis of such type of signals is a tedious task. The predefined basis function
may fail to provide solutions. This problem can be overcome by an adaptive
or signal-dependent basis which is used for the representation of nonlinear and
nonstationary signals. A method named Hilbert-Huang transform (HHT) is an
adaptive and empirical method. HHT consists of two parts for signal analysis. One
of them is empirical mode decomposition (EMD) and the second one is Hilbert
spectral analysis (HSA). This method provides good results for time-frequencyenergy representations of many signals [41].
Another method for analysis of real signals is higher-order spectra (HOS). A
real signal most specifically a non-Gaussian signal can be decomposed into higherorder spectral functions in which each higher-order spectral function may contain