Is the PPG Signal Chaotic?

Abstract

PhotoPlethysmoGraphic (PPG) signal is an easily accessible biological signal that gives valuable diagnostic information. The novelty is the study procedure of the dynamic of the PPG signals, in our case of young and healthy individuals, with Deep Neural Network, which allows finding the dynamic behavior 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 that determines the dynamics of the PPG signal 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. INDEX TERMS Biological signal, DNN architectures, PPG signal dynamic, timescales.

Dynamical Analysis of Biological Signals with the 0–1 Test: A Case Study of the PhotoPlethysmoGraphic (PPG) Signal

“…The parameter K m trend allows evaluation of how K m changes as more data are added in the computation of K m ; so somehow, it enables the measurement of new operational modes (very low-frequency components of the PPG signal), as more minutes of the PPG signal are added to the K m analysis. Based on the results of a small timescale analysis of the PPG signal in healthy young people, we can draw that most of the PPG signals have a quasi-periodic behavior [46] (see Figure 3a,b to a complete description). We do not omit to express that several publications provide for a clear linkage between biological signals and quasi-periodic behavior but not with an accurate definition of what is meant by quasi-periodicity property, such as mentioned Ganeshapillai and Guttag [48].…”

“…As we will show later, the mutual coupling between the different components is intricate and operates at different timescales to regulate blood volume based on physiological needs [41][42][43][44]. At small timescales, the regular cardiac rhythm quasi-periodically modulated by the breathing rhythm prevails [37,45,46], as reflected in the two prominent peaks in the PSD of a typical PPG signal, relating to the respiratory, lower frequency peak, and heart, higher frequency peak, rates (see Figure 2f-j).…”

From Frequency Content to Signal Dynamics Using DNNs

“…Understanding signal dynamics makes it possible to conjecture the mechanisms that trigger its generation and, consequently, develop mathematical functional models that increasingly support more reliable reproductions and predictions. Among the sources of data that have been the subject of dynamic studies, there are medical imaging and volumes [1], voice signals [2], radio signals [3], and, more specifically, biological signals, such as EEG [4,5], ECG [6], the EMG [7], and PPG signals [8][9][10] which are the focus of the present work.…”

“…In previous work, we looked at the behavior of the PPG signal dynamics from a stochastic perspective by modifying the 0-1 test [10]. We also carried on with the study of its chaoticity level using methods based on DNNs [9].…”

“…This study aimed to evaluate the dynamic composition of the PPG signals. [9]. Once the DNNs retain the dynamical singularities of the reference signals, the system provides a combined (mixed) dynamical characterization of the input signals on large and small timescales.…”