12 Biomedical Engineering Fundamentals
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different information about the signal [42]. These methods also provide significant
contribution in biomedical signal analysis.
12.8 Modeling of Biomedical Signals
Biomedical signals can be modeled by mathematical functions. The procedure is
started with finding a model which follows the laws of physics. The equations
are solved for typical functions. The response of these equations is compared with
developed physical model with the same typical functions. If these two responses are
approximately equal, then we can use the developed model for analysis; otherwise,
we have to improve our developed model [43].
12.8.1 Models for ECG Signal Representation
The ECG signals have pseudo-periodicity feature and features related to the
constituent signals (P, QRS, and T). The modeling of ECG signals can be possible
with parametric and nonparametric models. Most of them are parametric models
such as impulse response of a pole-zero model and damped sinusoid model.
The nonparametric models fail to exploit the nature of ECG signals. Hence, the
parametric models overcome these problems.
The autoregressive (AR)/autoregressive moving-average (ARMA) model which
is a parametric model is also used for the modeling of ECG signals. The amplitudemodulated (AM) sinusoidal signal model which is a special case of AR/ARMA
model is also used due to its burst-like feature [44]. The model based on hidden
Markov is also proposed to model every specific abnormal beat classification [45].
The dynamical model based on three coupled ordinary differential equations was
used for generating synthetic ECG signals [46]. The Hilbert transform-based model
is a recent approach for ECG signal modeling [47].
12.8.2 Models for EEG Signal Representation
The EEG signals have certain deviation or patterns as compared to the normal
EEG signals during neurological disorders. These patterns occur for one or few
seconds in the EEG signals. These patterns can be identified by modeling of EEG
signals to detect various neurological diseases. The parametric modeling of EEG
signal is the most common approach among them. The parametric model which is
mostly used for EEG signal modeling is a rational transfer function with selected
parameters. If the parameters lie in the denominator, then it is known as an allpole or AR model, whereas if all the parameters lie in the numerator, then it is
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different information about the signal [42]. These methods also provide significant
contribution in biomedical signal analysis.
12.8 Modeling of Biomedical Signals
Biomedical signals can be modeled by mathematical functions. The procedure is
started with finding a model which follows the laws of physics. The equations
are solved for typical functions. The response of these equations is compared with
developed physical model with the same typical functions. If these two responses are
approximately equal, then we can use the developed model for analysis; otherwise,
we have to improve our developed model [43].
12.8.1 Models for ECG Signal Representation
The ECG signals have pseudo-periodicity feature and features related to the
constituent signals (P, QRS, and T). The modeling of ECG signals can be possible
with parametric and nonparametric models. Most of them are parametric models
such as impulse response of a pole-zero model and damped sinusoid model.
The nonparametric models fail to exploit the nature of ECG signals. Hence, the
parametric models overcome these problems.
The autoregressive (AR)/autoregressive moving-average (ARMA) model which
is a parametric model is also used for the modeling of ECG signals. The amplitudemodulated (AM) sinusoidal signal model which is a special case of AR/ARMA
model is also used due to its burst-like feature [44]. The model based on hidden
Markov is also proposed to model every specific abnormal beat classification [45].
The dynamical model based on three coupled ordinary differential equations was
used for generating synthetic ECG signals [46]. The Hilbert transform-based model
is a recent approach for ECG signal modeling [47].
12.8.2 Models for EEG Signal Representation
The EEG signals have certain deviation or patterns as compared to the normal
EEG signals during neurological disorders. These patterns occur for one or few
seconds in the EEG signals. These patterns can be identified by modeling of EEG
signals to detect various neurological diseases. The parametric modeling of EEG
signal is the most common approach among them. The parametric model which is
mostly used for EEG signal modeling is a rational transfer function with selected
parameters. If the parameters lie in the denominator, then it is known as an allpole or AR model, whereas if all the parameters lie in the numerator, then it is