Analysis of the Chaotic Component of Photoplethysmography and Its Association with Hemodynamic Parameters

Abstract

Wearable technologies face challenges due to signal instability, hindering their usage. Thus, it is crucial to comprehend the connection between dynamic patterns in photoplethysmography (PPG) signals and cardiovascular health. In our study, we collected 401 multimodal recordings from two public databases, evaluating hemodynamic conditions like blood pressure (BP), cardiac output (CO), vascular compliance (C), and peripheral resistance (R). Using irregular-resampling auto-spectral analysis (IRASA), we quantified chaotic components in PPG signals and employed different methods to measure the fractal dimension (FD) and entropy. Our findings revealed that in surgery patients, the power of chaotic components increased with vascular stiffness. As the intensity of CO fluctuations increased, there was a notable strengthening in the correlation between most complexity measures of PPG and these parameters. Interestingly, some conventional morphological features displayed a significant decrease in correlation, indicating a shift from a static to dynamic scenario. Healthy subjects exhibited a higher percentage of chaotic components, and the correlation between complexity measures and hemodynamics in this group tended to be more pronounced. Causal analysis showed that hemodynamic fluctuations are main influencers for FD changes, with observed feedback in most cases. In conclusion, understanding chaotic patterns in PPG signals is vital for assessing cardiovascular health, especially in individuals with unstable hemodynamics or during ambulatory testing. These insights can help overcome the challenges faced by wearable technologies and enhance their usage in real-world scenarios.

Robust arterial compliance estimation with Katz’s fractal dimension of photoplethysmography

“…While these methods demonstrate good performance in specific datasets, their internal mechanisms remain unknown, which may limit their ability to generalize to populations with different hemodynamic statuses. Nevertheless, these algorithms and relevant studies identified the temporal fluctuation or complexity of PPG as a promising candidate for AC assessment ( Sviridova et al, 2018 ; Xing et al, 2023a ; Xing et al, 2023b ).…”

“…In recent years, the concept of fractal dimension (FD), a widely used mathematical tool in biomedical signal processing, has shown promise in AC estimation ( Sviridova and Sakai, 2015 ; Sviridova et al, 2018 ; Xing et al, 2023a ). FD captures the geometric complexity of signals and encodes transient changes of physiological status into the temporal patterns.…”

“…For example, Esteller et al found that Katz’s FD (KFD) yielded consistent results in discriminating between states of brain function and was less susceptible to noise effects ( Esteller et al, 2001 ). In our previous comprehensive search, we found that Higuchi FD is closely related to mean blood pressure (BP), while KFD is significantly associated with compliance ( Xing et al, 2023a ; Xing et al, 2023b ). It is important to note that the compliance estimation method employed in those studies was imprecise and did not undergo validation against a gold standard.…”