Autonomic-cardiorespiratory regulation: a physiology-based mathematical model

A Novel Strategy to Fit and Validate Physiological Models: A Case Study of a Cardiorespiratory Model for Simulation of Incremental Aerobic Exercise

Applying complex mathematical models of physiological systems is challenging due to the large number of parameters. Identifying these parameters through experimentation is difficult, and although procedures for fitting and validating models are reported, no integrated strategy exists. Additionally, the complexity of optimization is generally neglected when the number of experimental observations is restricted, obtaining multiple solutions or results without physiological justification. This work proposes a fitting and validation strategy for physiological models with many parameters under various populations, stimuli, and experimental conditions. A cardiorespiratory system model is used as a case study, and the strategy, model, computational implementation, and data analysis are described. Using optimized parameter values, model simulations are compared to those obtained using nominal values, with experimental data as a reference. Overall, a reduction in prediction error is achieved compared to that reported for model building. Furthermore, the behavior and accuracy of all the predictions in the steady state were improved. The results validate the fitted model and provide evidence of the proposed strategy's usefulness.

Cognitive Behavioral Therapy Plus a Serious Game as a Complementary Tool for a Patient With Parkinson Disease and Impulse Control Disorder: Case Report

Background

Impulse control disorders (ICDs) are commonly developed among patients who take dopamine agonist drugs as a treatment for Parkinson disease (PD). Gambling disorder and hypersexuality are more frequent in male patients with PD, with a prevalence over 4% in dopamine agonists users. Although impulsive-compulsive behaviors are related to antiparkinsonian medication, and even though ICD symptomatology, such as hypersexuality, often subsides when the dopaminergic dose is reduced, sometimes ICD persists in spite of drug adjustment. Consequently, a multidisciplinary approach should be considered to address these comorbidities and to explore new forms of complementary interventions, such as serious games or therapies adapted to PD.

Objective

The aim of this study is to present the case of a patient with ICD (ie, hypersexuality) triggered by dopaminergic medication for PD. A combined intervention was carried out using cognitive behavioral therapy (CBT) for ICD adapted to PD, plus an intervention using a serious game—e-Estesia—whose objective is to improve emotion regulation and impulsivity. The aim of the combination of these interventions was to reduce the harm of the disease.

Methods

After 20 CBT sessions, the patient received the e-Estesia intervention over 15 sessions. Repeated measures, before and after the combined intervention, were administered to assess emotion regulation, general psychopathology, and emotional distress and impulsivity.

Results

After the intervention with CBT techniques and e-Estesia, the patient presented fewer difficulties to regulate emotion, less emotional distress, and lower levels of impulsivity in comparison to before the treatment. Moreover, the frequency and severity of the relapses also decreased.

Conclusions

The combined intervention—CBT and a serious game—showed positive results in terms of treatment outcomes.

Separation of respiratory influences from the tachogram: a methodological evaluation

The variability of the heart rate (HRV) is widely studied as it contains information about the activity of the autonomic nervous system (ANS). However, HRV is influenced by breathing, independently of ANS activity. It is therefore important to include respiratory information in HRV analyses in order to correctly interpret the results. In this paper, we propose to record respiratory activity and use this information to separate the tachogram in two components: one which is related to breathing and one which contains all heart rate variations that are unrelated to respiration. Several algorithms to achieve this have been suggested in the literature, but no comparison between the methods has been performed yet. In this paper, we conduct two studies to evaluate the methods' performances to accurately decompose the tachogram in two components and to assess the robustness of the algorithms. The results show that orthogonal subspace projection and an ARMAX model yield the best performances over the two comparison studies. In addition, a real-life example of stress classification is presented to demonstrate that this approach to separate respiratory information in HRV studies can reveal changes in the heart rate variations that are otherwise masked by differing respiratory patterns.