  In the analysis of biological time series, the state space comprises a
framework for the study of systems with presumably deterministic properties.
However, a physiological experiment typically captures an observable, or, in
other words, a series of scalar measurements that characterize the temporal
response of the physiological system under study; the dynamic variables that
make up the state of the system at any time are not available. Therefore, only
from the acquired observations should state vectors reconstructed to emulate
the different states of the underlying system. It is what is known as the
reconstruction of the state space, called phase space in real-world signals,
for now only satisfactorily resolved using the method of delays. Each state
vector consists of m components, extracted from successive observations delayed
a time t. The morphology of the geometric structure described by the state
vectors, as well as their properties, depends on the chosen parameters t and m.
The real dynamics of the system under study is subject to the correct
determination of the parameters t and m. Only in this way can be deduced
characteristics with true physical meaning, revealing aspects that reliably
identify the dynamic complexity of the physiological system. The biological
signal presented in this work, as a case study, is the PhotoPlethysmoGraphic
(PPG) signal. We find that m is five for all the subjects analyzed and that t
depends on the time interval in which it evaluates. The H\'enon map and the
Lorenz flow are used to facilitate a more intuitive understanding of applied
techniques.
