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R. B. Pachori and V. Gupta
parts are active, but it is lower for an old brain whose parts are considerably less
active. The same behavior is also recorded for the other complexity measures from
EEG signals of older people as compared to younger people. This phenomenon
is also true for a person suffering from diseases like epilepsy and Alzheimer’s.
The complexity measures decrease due to the presence of such kind of diseases.
Moreover, the reduction of 30% fractal dimension is used as diagnosis criteria for
epilepsy from EEG signals [30].
The most common feature in time-domain analysis of ECG signals is the duration
of the heart cycle. The heart cycle duration is basically a time span from one R wave
to the next occurring R wave. The other generally used features for ECG signal are
the duration of QRS complex and the time interval between T and P waves. The QRS
complex is determined by the characteristic shape and relative stable time constant
in the pattern [30].
The time-domain analysis of EMG signals is possible with several features.
The mean absolute value (MAV), root mean square (RMS), zero crossing, v order,
log detector, waveform length (WL), Willison amplitude (WAMP), and slope sign
change (SSC) are commonly used for the time-domain analysis of EMG signals.
The MAV feature provides the information about energy and fatigue present in EMG
signals. On the other hand, the RMS feature represents the non-fatigue as well as
fatigue contraction. The frequency information present in EMG signals is provided
by time-domain features such as zero crossing, WAMP, and SSC. The v- order and
log detector features estimate the muscle contraction force and fatigue [39].
12.7.2 Frequency-Domain Analysis
The frequency-domain analysis of biomedical signals is possible with Fourier
transform which converts the time-domain representation of a signal into frequency
domain. The frequency-domain analysis is mainly used to characterize the frequency contents present in a signal. The major limitation of this technique is that the
Fourier transform works only for stationary signals because the time information is
lapsed in frequency-domain analysis.
In frequency-domain analysis of EEG signals, the main feature is the computation of power of the particular frequencies from the power spectra of the EEG
signal. The spectral analysis of EEG signal will quickly identify any irregular pattern
of higher harmonics in the frequency spectrum. The spectrum of EEG signal is
generally analyzed only over a consecutive short-time segment. This short-time
segment of EEG signal is known as “epoch.” The length of epochs decides the
frequency resolution of EEG signal in frequency spectrum. However, the selection
of longer time segments will result in lower time resolution which is a trade-off
between time and frequency resolution.
The frequency components of EEG signal such as alpha, beta, delta, and theta
waves are very informative and it can be easily extracted from the power spectra
R. B. Pachori and V. Gupta
parts are active, but it is lower for an old brain whose parts are considerably less
active. The same behavior is also recorded for the other complexity measures from
EEG signals of older people as compared to younger people. This phenomenon
is also true for a person suffering from diseases like epilepsy and Alzheimer’s.
The complexity measures decrease due to the presence of such kind of diseases.
Moreover, the reduction of 30% fractal dimension is used as diagnosis criteria for
epilepsy from EEG signals [30].
The most common feature in time-domain analysis of ECG signals is the duration
of the heart cycle. The heart cycle duration is basically a time span from one R wave
to the next occurring R wave. The other generally used features for ECG signal are
the duration of QRS complex and the time interval between T and P waves. The QRS
complex is determined by the characteristic shape and relative stable time constant
in the pattern [30].
The time-domain analysis of EMG signals is possible with several features.
The mean absolute value (MAV), root mean square (RMS), zero crossing, v order,
log detector, waveform length (WL), Willison amplitude (WAMP), and slope sign
change (SSC) are commonly used for the time-domain analysis of EMG signals.
The MAV feature provides the information about energy and fatigue present in EMG
signals. On the other hand, the RMS feature represents the non-fatigue as well as
fatigue contraction. The frequency information present in EMG signals is provided
by time-domain features such as zero crossing, WAMP, and SSC. The v- order and
log detector features estimate the muscle contraction force and fatigue [39].
12.7.2 Frequency-Domain Analysis
The frequency-domain analysis of biomedical signals is possible with Fourier
transform which converts the time-domain representation of a signal into frequency
domain. The frequency-domain analysis is mainly used to characterize the frequency contents present in a signal. The major limitation of this technique is that the
Fourier transform works only for stationary signals because the time information is
lapsed in frequency-domain analysis.
In frequency-domain analysis of EEG signals, the main feature is the computation of power of the particular frequencies from the power spectra of the EEG
signal. The spectral analysis of EEG signal will quickly identify any irregular pattern
of higher harmonics in the frequency spectrum. The spectrum of EEG signal is
generally analyzed only over a consecutive short-time segment. This short-time
segment of EEG signal is known as “epoch.” The length of epochs decides the
frequency resolution of EEG signal in frequency spectrum. However, the selection
of longer time segments will result in lower time resolution which is a trade-off
between time and frequency resolution.
The frequency components of EEG signal such as alpha, beta, delta, and theta
waves are very informative and it can be easily extracted from the power spectra