12 Biomedical Engineering Fundamentals
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12.7 Analysis of Biomedical Signals
Biomedical signals are primarily used for the diagnosis and monitoring of specific
pathological/physiological states. In some cases, the researchers have also used
these signals for decoding and eventual modeling of specific biological systems.
The recent advancement in technology allows the acquisition of multiple channels
of biomedical signals. This process leads to additional signal processing challenges
to identify meaningful interactions between these channels. The main aim of signal
processing is generally noise removal, accurate signal modeling, extraction of
components for analysis purpose, and feature extraction for deciding function or
dysfunction of the heart and brain. The signal processing is used in biological
applications due to several reasons. In most of the cases, the monitored biological
signal contains an additive combination of signal and noise. The presence of noise
can be due to instruments (sensors, amplifiers, filters, etc.) and electromagnetic
interference (EMI). Therefore, the different conditions suggest different assumptions for noise characteristics, which will eventually lead to an appropriate choice
of signal processing method [37].
12.7.1 Time-Domain Analysis
The time-domain analysis of biomedical signals is usually fast and easy to implement, because time-domain analysis does not need any transformation of biomedical
signals. In time-domain analysis, several features based on different characteristics
of signal are computed from biomedical signals. These time-domain features have
been generally used in different areas of medical as well as engineering research.
A major drawback of these features is due to the nonstationary nature of the
biomedical signal, which changes the statistical properties over time. Therefore, the
computed values of time-domain features may vary largely when the biomedical
signal is recorded in interference and noisy environments. However, the timedomain features have been widely used for biomedical signal due to their lower
computational complexity [38]. There are different time-domain characteristics
which vary from one biomedical signal to the other.
In the time-domain analysis of EEG signals, the artifact which usually exceeded
instantaneous amplitude as compared to normal instantaneous amplitude present
in EEG signal is determined by amplitude thresholds. The muscle artifacts can be
minimized with the use of slope or steepness threshold. The first-order derivative
of EEG signal gives us the slope. In addition to first-order derivative, the secondorder derivative is also used to measure the complexity present in EEG signal
[30]. Apart from this measure, there are some other complexity measures such as
fractal dimension and entropy which are also frequently used as features. The fractal
dimension of the EEG signal decreases as the age increases in humans. Hence, it can
be concluded with this fact that the fractal dimension is higher for a brain whose all
595
12.7 Analysis of Biomedical Signals
Biomedical signals are primarily used for the diagnosis and monitoring of specific
pathological/physiological states. In some cases, the researchers have also used
these signals for decoding and eventual modeling of specific biological systems.
The recent advancement in technology allows the acquisition of multiple channels
of biomedical signals. This process leads to additional signal processing challenges
to identify meaningful interactions between these channels. The main aim of signal
processing is generally noise removal, accurate signal modeling, extraction of
components for analysis purpose, and feature extraction for deciding function or
dysfunction of the heart and brain. The signal processing is used in biological
applications due to several reasons. In most of the cases, the monitored biological
signal contains an additive combination of signal and noise. The presence of noise
can be due to instruments (sensors, amplifiers, filters, etc.) and electromagnetic
interference (EMI). Therefore, the different conditions suggest different assumptions for noise characteristics, which will eventually lead to an appropriate choice
of signal processing method [37].
12.7.1 Time-Domain Analysis
The time-domain analysis of biomedical signals is usually fast and easy to implement, because time-domain analysis does not need any transformation of biomedical
signals. In time-domain analysis, several features based on different characteristics
of signal are computed from biomedical signals. These time-domain features have
been generally used in different areas of medical as well as engineering research.
A major drawback of these features is due to the nonstationary nature of the
biomedical signal, which changes the statistical properties over time. Therefore, the
computed values of time-domain features may vary largely when the biomedical
signal is recorded in interference and noisy environments. However, the timedomain features have been widely used for biomedical signal due to their lower
computational complexity [38]. There are different time-domain characteristics
which vary from one biomedical signal to the other.
In the time-domain analysis of EEG signals, the artifact which usually exceeded
instantaneous amplitude as compared to normal instantaneous amplitude present
in EEG signal is determined by amplitude thresholds. The muscle artifacts can be
minimized with the use of slope or steepness threshold. The first-order derivative
of EEG signal gives us the slope. In addition to first-order derivative, the secondorder derivative is also used to measure the complexity present in EEG signal
[30]. Apart from this measure, there are some other complexity measures such as
fractal dimension and entropy which are also frequently used as features. The fractal
dimension of the EEG signal decreases as the age increases in humans. Hence, it can
be concluded with this fact that the fractal dimension is higher for a brain whose all