  This paper shows how the dynamics of the PhotoPlethysmoGraphic (PPG) signal,
an easily accessible biological signal from which valuable diagnostic
information can be extracted, of young and healthy individuals performs at
different timescales. On a small timescale, the dynamic behavior of the PPG
signal is predominantly quasi-periodic. On a large timescale, a more complex
dynamic diversity emerges, but never a chaotic behavior as earlier studies had
reported. The procedure by which the dynamics of the PPG signal is determined
consists of contrasting the dynamics of a PPG signal with well-known
dynamics---named reference signals in this study---, mostly present in physical
systems, such as periodic, quasi-periodic, aperiodic, chaotic or random
dynamics. For this purpose, this paper provides two methods of analysis based
on Deep Neural Network (DNN) architectures. The former uses a Convolutional
Neural Network (CNN) architecture model. Upon training with reference signals,
the CNN model identifies the dynamics present in the PPG signal at different
timescales, assigning, according to a classification process, an occurrence
probability to each of them. The latter uses a Recurrent Neural Network (RNN)
based on a Long Short-Term Memory (LSTM) architecture. With each of the
signals, whether reference signals or PPG signals, the RNN model infers an
evolution function (nonlinear regression model) based on training data, and
considers its predictive capability over a relatively short time horizon.
