A Bayesian Filtering Approach for Tracking Sympathetic Arousal and Cortisol-Related Energy From Marked Point Process and Continuous-Valued Observations

Abstract

Multiple state variables governed by internal processes within the human body remain unobserved. On a number of occasions, these states are linked to point process bioelectric and biochemical observations coupled together with continuous-valued variables. These observations provide a means to estimate the latent states of interest. We develop a state-space method to estimate unobserved sympathetic arousal and energy production states from skin conductance and cortisol data respectively, comprising of a marked point process and a continuous-valued observation. The method involves Bayesian filtering applied within an expectation-maximization (EM) framework for state estimation and model parameter recovery. Results are evaluated on both simulated and experimental data. On experimental skin conductance data, high arousal levels are generally detected during cognitive stress periods and lower values are detected during relaxation. Results are also in conformity with general physiology for cortisol data. On separate experimental data, skin conductance-based estimates are validated/cross-checked with functional Near Infrared Spectroscopy features. Estimation is also performed on heart rate and skin conductance data to illustrate the method's wider applicability. We also compare the method with earlier approaches. We show how it outperforms a previous method for cortisol-based energy estimation, and its superiority to earlier methods for estimating sympathetic arousal. The EM approach is thus able to estimate latent physiological states within the body from point process bioelectric and biochemical phenomena. The method could be applied in wearable monitoring and automated closed-loop therapy delivery for patients diagnosed with certain types of neuropsychiatric or hormone disorders.INDEX TERMS Biomedical signal processing, state-space methods, estimation, emotion recognition, Kalman filters I. INTRODUCTIONS EVERAL internal state variables maintained by physiological processes within the human body remain largely unobserved. Nevertheless, they are often tied to more easilymeasured changes in biolelectric and biochemical phenomena that can be used for their estimation. For instance, energy

Data_Sheet_1.pdf

“…This conforms with the findings in Zheng et al (2020). Perhaps the participants had to concentrate more within higher arousal levels (Wickramasuriya et al, 2023). Overall, these findings may be an indication of a new avenue for decoder design research, leading to innovative fNIRS feature extraction to decode the hidden arousal and performance states.…”

“…Additionally, inspired by the study in Wickramasuriya et al (2023), we investigate HbT signal energy with respect to the presented music types. Particularly, we consider the collected signal from the PFC channels (i.e., channels 23-44) and smooth the signal using a 10 s sample-by-sample running average filter.…”

A multimodal dataset for investigating working memory in presence of music: a pilot study

“…This conforms with the findings in Zheng et al ( 2020 ). Perhaps the participants had to concentrate more within higher arousal levels (Wickramasuriya et al, 2023 ). Overall, these findings may be an indication of a new avenue for decoder design research, leading to innovative fNIRS feature extraction to decode the hidden arousal and performance states.…”

“…Additionally, inspired by the study in Wickramasuriya et al ( 2023 ), we investigate HbT signal energy with respect to the presented music types. Particularly, we consider the collected signal from the PFC channels (i.e., channels 23–44) and smooth the signal using a 10 s sample-by-sample running average filter.…”