Biosignals may be classified in many ways. The following is a brief discussion of some of the most
important classifications.
•
Classification according to source.
Biosignals may be classified according to their source or physical
nature. This classification was described in the preceding section. This classification may be used
when the basic physical characteristics of the underlying process is of interest, e.g., when a model
for the signal is desired.
•
Classification according to biomedical application.
The biomedical signal is acquired and processed
with some diagnostic, monitoring, or other goal in mind. Classification may be constructed
according to the field of application, e.g., cardiology or neurology. Such classification may be of
interest when the goal is, for example, the study of physiologic systems.
•
Classification according to signal characteristics.
From point of view of signal analysis, this is the
most relevant classification method. When the main goal is processing, it is not relevant what is
the source of the signal or to which biomedical system it belongs; what matters are the signal
characteristics.
We recognize two broad classes of signals:
continuous signals
and
discrete signals.
Continuous signals
are described by a continuous function
s
(
t
) which provides information about the signal at any given
time. Discrete signals are described by a
sequence s
(
m
) which provides information at a given discrete
point on the time axis. Most of the biomedical signals are continuous. Since current technology provides
powerful tools for discrete signal processing, we most often transform a continuous signal into a discrete
one by a process known as
sampling.
A given signal
s
(
t
) is sampled into the sequence
s
(
m
) by