600
R. B. Pachori and V. Gupta
known as all-zero or moving-average (MA) model. A model with parameters lie
in the numerator and a denominator is known as pole-zero or ARMA model [48].
The parametric modeling of ictal EEG signal using Prony’s method is also possible.
This method is based on the assumption that the original signal is a sum of damped
complex exponential sinusoids, and it has good frequency resolution compared to
AR model. This method suggests that the modeling of ictal EEG signal is based
on the poles of EEG signal [49]. A method based on second-order linear timevarying AR (TVAR) with appropriate chosen length obtained using FBSE is used
for parametric modeling of EEG signal [17].
12.8.3 Models for EMG Signal Representation
The main purpose for modeling EMG signal is to understand electrophysiological
information for the detection of neuromuscular disorders. The EMG signal modeling can also be possible by AR model [50]. A modified method autoregressive
integrated moving-average (ARIMA) model has been also proposed for EMG
signals in the literature [51].
12.8.4 Models of Other Biomedical Signals
The parametric modeling of PCG signals for the detection of murmurs is possible
with AR modeling. This model used dominant poles for pattern classification and
spectral tracking [52, 53].
The modeling of respiratory sound signals can be possible with mechanical as
well as electrical models. In these models, the vocal and respiratory tract of humans
can be represented by tubes and pipes and their electrical equivalent circuits [54].
12.9 Applications
The biomedical signals have also been used in certain areas of applications based on
signal processing techniques such as detection of heart-related diseases, neurological disorders, neuromuscular diseases, postural stability analysis, and other related
disease. The description of these applications is illustrated below.
R. B. Pachori and V. Gupta
known as all-zero or moving-average (MA) model. A model with parameters lie
in the numerator and a denominator is known as pole-zero or ARMA model [48].
The parametric modeling of ictal EEG signal using Prony’s method is also possible.
This method is based on the assumption that the original signal is a sum of damped
complex exponential sinusoids, and it has good frequency resolution compared to
AR model. This method suggests that the modeling of ictal EEG signal is based
on the poles of EEG signal [49]. A method based on second-order linear timevarying AR (TVAR) with appropriate chosen length obtained using FBSE is used
for parametric modeling of EEG signal [17].
12.8.3 Models for EMG Signal Representation
The main purpose for modeling EMG signal is to understand electrophysiological
information for the detection of neuromuscular disorders. The EMG signal modeling can also be possible by AR model [50]. A modified method autoregressive
integrated moving-average (ARIMA) model has been also proposed for EMG
signals in the literature [51].
12.8.4 Models of Other Biomedical Signals
The parametric modeling of PCG signals for the detection of murmurs is possible
with AR modeling. This model used dominant poles for pattern classification and
spectral tracking [52, 53].
The modeling of respiratory sound signals can be possible with mechanical as
well as electrical models. In these models, the vocal and respiratory tract of humans
can be represented by tubes and pipes and their electrical equivalent circuits [54].
12.9 Applications
The biomedical signals have also been used in certain areas of applications based on
signal processing techniques such as detection of heart-related diseases, neurological disorders, neuromuscular diseases, postural stability analysis, and other related
disease. The description of these applications is illustrated below.