Decoding photoplethysmograms to broaden scope of health monitoring technologies – The Mahathian Post

To advance the potential applications of photoplethysmography, an optical technique to measure heart rate in cardiovascular and mental health monitoring, analysis of complex nonlinear data from photoplethysmograms (PPGs) is required. Bypassing traditional analysis methods to resolve the complex and dynamic PPG signals, researchers at Tokyo University of Science used nonlinear analysis to determine the accuracy of dynamic features estimated using short PPG signals.

With the increasing burden of cardiovascular and mental disorders worldwide, the need for early and timely detection increases health monitoring is becoming more and more relevant. Wearable devices serve as a convenient, affordable, and non-invasive approach to systematic and prolonged health surveillance. “Photoplethysmography”, a simple optical technique based on photoelectric pulse wave signaling, has been used for decades to monitor parameters such as heart rate, oxygen rate and changes in blood volume in clinical settings and on the go portable devices. These measurements rely on basic signal processing and analysis such as noise filtering and motion reduction.

Information extracted from the dynamics of photoplethysmograms (PPGs), the biological signal records of photoplethysmography, can be used for physiological and mental health monitoring, but such advanced applications are hampered by high measurement noise and motion artifacts in PPGs, especially those that are obtained with wearable devices.

So how can the complex nonlinear dynamics of PPGs be analyzed to expand their clinical applications?

A team of researchers from Japan delved into the analysis of the complex properties of PPGs and evaluated the applicability of nonlinear analysis of short PPG signals in clinical measurements and the accuracy with which they can estimate the dynamic properties of PPGs. A group of researchers led by Dr. Nina Sviridova, an assistant professor from Tokyo University of Science, including Prof. Tohru Ikeguchi from Tokyo University of Science, Dr. Tiejun Zhao from Niigata Agro-Food University and Prof. Akimasa Nakano from Chiba University have published their findings in the journal’s special issue “Data Analytics for Mobile-Health”. sensors. The study was published in Volume 22, Issue 14 of the journal on July 9, 2022.

“Filtered signals can be used for traditional photoplethysmography applications, but they are not suitable for advanced analysis. As an alternative, only high-quality short segments of PPG signals can be used, but the applicability of nonlinear analysis to such short records has not been studied in detail,” explains Dr. Sviridova.

Advanced nonlinear analysis methods used to estimate PPG dynamics are often limited by the data length used. Previous studies indicate that recurrent quantification analysis (RQA), a nonlinear analytical approach, is not affected by signal length. In this study, researchers used RQA to extract the dynamic properties of PPGs such as determinism, divergence, predictability, and complexity from short signals. PPG recordings were obtained from thirty healthy subjects by measuring the transmission of near-infrared light from skin surfaces. These records were further subsampled to generate sparse time series data. Also, the chaotic “Rössler model” (a model used to describe continuous chaos in dynamic nonlinear systems) was used to calculate the relative error while accounting for noise.

The results suggested that dynamic properties such as determinism, predictability and entropy can be estimated with good accuracy (less than 1% error) using short-time series signals. Comparisons with the noisy Rossler system suggested that in the absence of noise, a shorter time series length is acceptable to measure these properties with accuracy. However, for some properties like divergence, short PPGs were not sufficient for an accurate estimation with an acceptable error (less than 1%).

These observations can help estimate the error associated with dynamic properties in cases where only short PPG signals are available and support future investigations using other photodetectors and studies in different experimental and real environments. An understanding of the complex properties of PPGs can further improve the clinical applications of wearable health monitoring technologies.

dr Highlighting the broader applications of her study, Sviridova says, “The results of this study will help improve the estimation of health parameters using wearable devices and ultimately accelerate the World Health Organization’s goal of early detection of cardiovascular and mental disorders.”

We certainly wish to see these findings translated into tangible health benefits for society.