Browsing by Author "Rodriguez-Fernandez, Maria"

Now showing 1 - 17 of 17
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    A Cyber-Physical System for Real-Time Physiological Data Monitoring and Analysis
    (2024) Huanca, Fernando; Torres, Mario; Rodriguez-Fernandez, Maria; Nunez, Felipe
    In the pursuit of a personalized healthcare experience, data-driven decision-making has become increasingly relevant. In this context, there is a growing need for technological systems specifically tailored to efficiently manage vast amounts of healthcare data. To address this need, in this work, we contribute by presenting a cyber-physical solution for monitoring and analyzing physiological data obtained from wearable devices. The proposed system is designed following a service-oriented architecture, which promotes modularity and enables efficient data access and analysis. The system is capable of ingesting and consolidating wearable data produced by a variety of commercial devices, scaling to accommodate a large number of data producers, and accepting queries from a multitude of consumers through various mechanisms. Performance tests conducted in various scenarios using real data demonstrate the system's effectiveness in maintaining real-time data access.
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    A meta-learning approach to personalized blood glucose prediction in type 1 diabetes
    (2023) Langarica, Saul; Rodriguez-Fernandez, Maria; Nunez, Felipe; Doyle III, Francis J.
    Accurate blood glucose prediction is a critical element in modern artificial pancreas systems. Recently, many deep learning-based models have been proposed for glucose prediction, showing encouraging results in population modeling. However, due to the large amount of data required for training deep learning -based models, few studies have successfully addressed personalized modeling, which is critical to ensure safe policies in a closed-loop scheme given the high inter-patient variability. To address this concern, we propose a meta-learning-based technique for accurate personalized modeling that requires minimal data volume to personalize from its population version, needs few training iterations, and has a low risk of over-fitting. Results using the UVA/Padova simulator show that the proposed technique generalizes better and outperforms other approaches in standard and task-specific metrics, particularly for longer prediction horizons and higher degrees of distributional shifts.
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    A Probabilistic Approach to Blood Glucose Prediction in Type 1 Diabetes Under Meal Uncertainties
    (2023) Langarica, Saul; Rodriguez-Fernandez, Maria; Doyle III, Francis J.; Nunez, Felipe
    Currently, most reliable and commercialized artificial pancreas systems for type 1 diabetes are hybrid closed-loop systems, which require the user to announce every meal and its size. However, estimating the amount of carbohydrates in a meal and announcing each and every meal is an error-prone process that introduces important uncertainties to the problem, which when not considered, lead to sub-optimal outcomes of the controller. To address this problem, we propose a novel deep-learning-based model for probabilistic glucose prediction, called the Input and State Recurrent Kalman Network (ISRKN), which consists in the incorporation of an input and state Kalman filter in the latent space of a deep neural network so that the posterior distributions can be computed in closed form and the uncertainty can be propagated using the Kalman equations. In addition, the proposed architecture allows explicit estimation of the meal uncertainty distribution, whose parameters are encoded in the filter parameters. Results using the UVA/Padova simulator and data from a clinical trial show that the proposed model outperforms other probabilistic models using several probabilistic metrics across different degrees of distributional shifts.
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    Assessing Language Lateralization through Gray Matter Volume: Implications for Preoperative Planning in Brain Tumor Surgery
    (2024) Solomons, Daniel; Rodriguez-Fernandez, Maria; Mery-Munoz, Francisco; Arrano-Carrasco, Leonardo; Costabal, Francisco Sahli; Mendez-Orellana, Carolina
    Background/Objectives: Functional MRI (fMRI) is widely used to assess language lateralization, but its application in patients with brain tumors can be hindered by cognitive impairments, compensatory neuroplasticity, and artifacts due to patient movement or severe aphasia. Gray matter volume (GMV) analysis via voxel-based morphometry (VBM) in language-related brain regions may offer a stable complementary approach. This study investigates the relationship between GMV and fMRI-derived language lateralization in healthy individuals and patients with left-hemisphere brain tumors, aiming to enhance accuracy in complex cases. Methods: The MRI data from 22 healthy participants and 28 individuals with left-hemisphere brain tumors were analyzed. Structural T1-weighted and functional images were obtained during three language tasks. Language lateralization was assessed based on activation in predefined regions of interest (ROIs), categorized as typical (left) or atypical (right or bilateral). The GMV in these ROIs was measured using VBM. Linear regressions explored GMV-lateralization associations, and logistic regressions predicted the lateralization based on the GMV. Results: In the healthy participants, typical left-hemispheric language dominance correlated with higher GMV in the left pars opercularis of the inferior frontal gyrus. The brain tumor participants with atypical lateralization showed increased GMV in six right-hemisphere ROIs. The GMV in the language ROIs predicted the fMRI language lateralization, with AUCs from 80.1% to 94.2% in the healthy participants and 78.3% to 92.6% in the tumor patients. Conclusions: GMV analysis in language-related ROIs effectively complements fMRI for assessing language dominance, particularly when fMRI is challenging. It correlates with language lateralization in both healthy individuals and brain tumor patients, highlighting its potential in preoperative language mapping. Further research with larger samples is needed to refine its clinical utility.
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    Circadian Rhythm of Blood Pressure of Dipper and Non-dipper Patients With Essential Hypertension: A Mathematical Modeling Approach
    (2021) Cortes-Rios, Javiera; Rodriguez-Fernandez, Maria
    Blood pressure in humans presents a circadian variation profile with a morning increase, a small postprandial valley, and a deeper descent during night-time rest. Under certain conditions, the nocturnal decline in blood pressure can be reduced or even reversed (non-dipper), which is related to a significantly worse prognosis than a normal fall pattern (dipper). Despite several advances in recent years, our understanding of blood pressure's temporal structure, its sources and mechanisms is far from complete. In this work, we developed an ordinary differential equation-based mathematical model capable of capturing the circadian rhythm of blood pressure in dipper and non-dipper patients with arterial hypertension. The model was calibrated by means of global optimization, using 24-h data of systolic and diastolic blood pressure, physical activity, heart rate, blood glucose and norepinephrine, obtained from the literature. After fitting the model, the mean of the normalized error for each data point was <0.2%, and confidence intervals indicate that all parameters were identifiable. Sensitivity analysis allowed identifying the most relevant parameters and therefore inferring the most important blood pressure regulatory mechanisms involved in the non-dipper status, namely, increase in sympathetic over parasympathetic nervous tone, lower influence of physical activity on heart rate and greater influence of physical activity and glucose on the systemic vascular resistance. In summary, this model allows explaining the circadian rhythm of blood pressure and deepening the understanding of the underlying mechanisms and interactions integrating the results of previous works.
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    Comparison of a tonometric with an oscillometric blood pressure monitoring device over 24 hours of ambulatory use
    (2021) Miranda Hurtado, Martin; Reyes Vasquez, Javiera; Rodriguez-Fernandez, Maria
    Purpose
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    Deep Learning-Based Glucose Prediction Models: A Guide for Practitioners and a Curated Dataset for Improved Diabetes Management
    (2024) Langarica, Saul; De La Vega, Diego; Cariman, Nawel; Miranda, Martin; Andrade, David C.; Nunez, Felipe; Rodriguez-Fernandez, Maria
    Accurate short- and mid-term blood glucose predictions are crucial for patients with diabetes struggling to maintain healthy glucose levels, as well as for individuals at risk of developing the disease. Consequently, numerous efforts from the scientific community have focused on developing predictive models for glucose levels. This study harnesses physiological data collected from wearable sensors to construct a series of data-driven models based on deep learning approaches. We systematically compare these models to offer insights for practitioners and researchers venturing into glucose prediction using deep learning techniques. Key questions addressed in this work encompass the comparison of various deep learning architectures for this task, determining the optimal set of input variables for accurate glucose prediction, comparing population-wide, fine-tuned, and personalized models, and assessing the impact of an individual's data volume on model performance. Additionally, as part of our outcomes, we introduce a meticulously curated dataset inclusive of data from both healthy individuals and those with diabetes, recorded in free-living conditions. This dataset aims to foster research in this domain and facilitate equitable comparisons among researchers.
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    Dosing time optimization of antihypertensive medications by including the circadian rhythm in pharmacokinetic-pharmacodynamic models
    (2022) Cortes-Rios, Javiera; Hermida, Ramon C.; Rodriguez-Fernandez, Maria
    Blood pressure (BP) follows a circadian variation, increasing during active hours, showing a small postprandial valley and a deeper decrease during sleep. Nighttime reduction of 10-20% relative to daytime BP is defined as a dipper pattern, and a reduction of less than 10%, as a non-dipper pattern. Despite this BP variability, hypertension's diagnostic criteria and therapeutic objectives are usually based on BP average values. Indeed, studies have shown that chrono-pharmacological optimization significantly reduces long-term cardiovascular risk if a BP dipper pattern is maintained. Changes in the effect of antihypertensive medications can be explained by circadian variations in their pharmacokinetics (PK) and pharmacodynamics (PD). Nevertheless, BP circadian variation has been scarcely included in PK-PD models of antihypertensive medications to date. In this work, we developed PK-PD models that include circadian rhythm to find the optimal dosing time (Ta) of first-line antihypertensive medications for dipper and non-dipper patterns. The parameters of the PK-PD models were estimated using global optimization, and models were selected according to the lowest corrected Akaike information criterion value. Simultaneously, sensitivity and identifiability analysis were performed to determine the relevance of the parameters and establish those that can be estimated. Subsequently, Ta parameters were optimized to maximize the effect on BP average, BP peaks, and sleep-time dip. As a result, all selected models included at least one circadian PK component, and circadian parameters had the highest sensitivity. Furthermore, Ta with which BP>130/80 mmHg and a dip of 10-20% are achieved were proposed when possible. We show that the optimal Ta depends on the therapeutic objective, the medication, and the BP profile. Therefore, our results suggest making chrono-pharmacological recommendations in a personalized way.
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    Emotion Recognition from ECG Signals Using Wavelet Scattering and Machine Learning
    (2021) Sepulveda, Axel; Castillo, Francisco; Palma, Carlos; Rodriguez-Fernandez, Maria
    Affect detection combined with a system that dynamically responds to a person's emotional state allows an improved user experience with computers, systems, and environments and has a wide range of applications, including entertainment and health care. Previous studies on this topic have used a variety of machine learning algorithms and inputs such as audial, visual, or physiological signals. Recently, a lot of interest has been focused on the last, as speech or video recording is impractical for some applications. Therefore, there is a need to create Human-Computer Interface Systems capable of recognizing emotional states from noninvasive and nonintrusive physiological signals. Typically, the recognition task is carried out from electroencephalogram (EEG) signals, obtaining good accuracy. However, EEGs are difficult to register without interfering with daily activities, and recent studies have shown that it is possible to use electrocardiogram (ECG) signals for this purpose. This work improves the performance of emotion recognition from ECG signals using wavelet transform for signal analysis. Features of the ECG signal are extracted from the AMIGOS database using a wavelet scattering algorithm that allows obtaining features of the signal at different time scales, which are then used as inputs for different classifiers to evaluate their performance. The results show that the proposed algorithm for extracting features and classifying the signals obtains an accuracy of 88.8% in the valence dimension, 90.2% in arousal, and 95.3% in a two-dimensional classification, which is better than the performance reported in previous studies. This algorithm is expected to be useful for classifying emotions using wearable devices.
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    Identification of perceived sentences using deep neural networks in EEG
    (2024) Valle, Carlos; Mendez-Orellana, Carolina; Herff, Christian; Rodriguez-Fernandez, Maria
    Objetive. Decoding speech from brain activity can enable communication for individuals with speech disorders. Deep neural networks (DNNs) have shown great potential for speech decoding applications. However, the limited availability of large datasets containing neural recordings from speech-impaired subjects poses a challenge. Leveraging data from healthy participants can mitigate this limitation and expedite the development of speech neuroprostheses while minimizing the need for patient-specific training data. Approach. In this study, we collected a substantial dataset consisting of recordings from 56 healthy participants using 64 EEG channels. Multiple neural networks were trained to classify perceived sentences in the Spanish language using subject-independent, mixed-subjects, and fine-tuning approaches. The dataset has been made publicly available to foster further research in this area. Main results. Our results demonstrate a remarkable level of accuracy in distinguishing sentence identity across 30 classes, showcasing the feasibility of training DNNs to decode sentence identity from perceived speech using EEG. Notably, the subject-independent approach rendered accuracy comparable to the mixed-subjects approach, although with higher variability among subjects. Additionally, our fine-tuning approach yielded even higher accuracy, indicating an improved capability to adapt to individual subject characteristics, which enhances performance. This suggests that DNNs have effectively learned to decode universal features of brain activity across individuals while also being adaptable to specific participant data. Furthermore, our analyses indicate that EEGNet and DeepConvNet exhibit comparable performance, outperforming ShallowConvNet for sentence identity decoding. Finally, our Grad-CAM visualization analysis identifies key areas influencing the network's predictions, offering valuable insights into the neural processes underlying language perception and comprehension. Significance. These findings advance our understanding of EEG-based speech perception decoding and hold promise for the development of speech neuroprostheses, particularly in scenarios where subjects cannot provide their own training data.
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    Identification of Statin’s Action in a Small Cohort of Patients with Major Depression
    (2021) Thakkar, Ishani; Massardo, Teresa; Pereira, Jaime; Quintana, Juan Carlos; Risco, Luis; Saez, Claudia G.; Corral, Sebastián; Villa, Carolina; Spuler, Jane; Olivares, Nixa; Valenzuela, Guillermo; Castro, Gabriel; Riedel, Byron; Vicentini, Daniel; Muñoz, Diego; Lastra, Raúl; Rodriguez-Fernandez, Maria
    Statins are widely used as an effective therapy for ischemic vascular disorders and employed for primary and secondary prevention in cardiac and cerebrovascular diseases. Their hemostatic mechanism has also been shown to induce changes in cerebral blood flow that may result in neurocognitive improvement in subjects with Major Depressive Disorder. Behavioral data, various blood tests, and resting-state brain perfusion data were obtained at the start of this study and three months post-therapy from a small cohort of participants diagnosed with Major Depressive Disorder. Subjects received either rosuvastatin (10 mg) or placebo with their standard selective serotonin reuptake inhibitors therapy. At the end of the study, patients using rosuvastatin reported more positive mood changes than placebo users. However, standard statistical tests revealed no significant differences in any non-behavioral variables before and after the study. In contrast, feature selection techniques allowed identifying a small set of variables that may be affected by statin use and contribute to mood improvement. Classification models built to assess the distinguishability between the two groups showed an accuracy higher than 85% using only five selected features: two peripheral platelet activation markers, perfusion abnormality in the left inferior temporal gyrus, Attention Switching Task Reaction latency, and serum phosphorus levels. Thus, using machine learning tools, we could identify factors that may be causing self-reported mood improvement in patients due to statin use, possibly suggesting a regulatory role of statins in the pathogenesis of clinical depression.
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    Increased respiratory modulation of cardiovascular control reflects improved blood pressure regulation in pregnancy
    (2023) Hurtado, Martin Miranda; Steinback, Craig D.; Davenport, Margie H.; Rodriguez-Fernandez, Maria
    Hypertensive pregnancy disorders put the maternal-fetal dyad at risk and are one of the leading causes of morbidity and mortality during pregnancy. Multiple efforts have been made to understand the physiological mechanisms behind changes in blood pressure. Still, to date, no study has focused on analyzing the dynamics of the interactions between the systems involved in blood pressure control. In this work, we aim to address this question by evaluating the phase coherence between different signals using wavelet phase coherence. Electrocardiogram, continuous blood pressure, electrocardiogram-derived respiration, and muscle sympathetic nerve activity signals were obtained from ten normotensive pregnant women, ten normotensive non-pregnant women, and ten pregnant women with preeclampsia during rest and cold pressor test. At rest, normotensive pregnant women showed higher phase coherence in the high-frequency band (0.15-0.4 Hz) between muscle sympathetic nerve activity and the RR interval, blood pressure, and respiration compared to non-pregnant normotensive women. Although normotensive pregnant women showed no phase coherence differences with respect to hypertensive pregnant women at rest, higher phase coherence between the same pairs of variables was found during the cold pressor test. These results suggest that, in addition to the increased sympathetic tone of normotensive pregnant women widely described in the existing literature, there is an increase in cardiac parasympathetic modulation and respiratory-driven modulation of muscle sympathetic nerve activity and blood pressure that could compensate sympathetic increase and make blood pressure control more efficient to maintain it in normal ranges. Moreover, blunted modulation could prevent its buffer effect and produce an increase in blood pressure levels, as observed in the hypertensive women in this study. This initial exploration of cardiorespiratory coupling in pregnancy opens the opportunity to follow up on more in-depth analyses and determine causal influences.
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    Maximal pulmonary ventilation and lactate affect the anaerobic performance in young women exposed to hypobaric hypoxia
    (2023) Paez, Valeria; Rodriguez-Fernandez, Maria; Silva-Urra, Juan; Nunez-Espinosa, Cristian; Lang, Morin
    Background: Athletes, tourists, and mining workers from all over the world ascend daily to an altitude greater than 3.000 meters above sea level to perform different activities, all of which demand physical effort. A ventilation increase is the first mechanism once the chemoreceptors perceive hypoxia, and is key to maintaining blood oxygen levels during acute exposure to high altitudes and to buffering lactic acidosis during exercise. It has been observed that gender is a variable that can influence the ventilatory response. Still, the available literature is limited due to the few studies considering women as study subjects. The influence of gender on anaerobic performance and its effects under high altitudes (HA) environments have been poorly studied.
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    Quality of life, exercise capacity, cognition, and mental health of Chilean patients after COVID-19: an experience of a multidisciplinary rehabilitation program at a physical and rehabilitation medicine unit
    (2023) Paez, Valeria; Rodriguez-Fernandez, Maria; Morales, Diego; Torres, Camillo; Ardiles, Andres; Soza, Sergio; Bustos, Cynthia; Manriquez, Fernanda; Garcia, Cesar; Rocco, Rossana; Lang, Morin
    Background: Post-COVID disabilities, encompassing physical, cognitive, and psychological aspects, constitute the primary health sequelae for survivors. While the rehabilitation needs post COVID-19 are now well understood, each country possesses unique characteristics in terms of populations, healthcare systems, social dynamics, and economic profiles, necessitating context-specific recommendations. This study aims to address two main objectives: (1) analyze the impact of an 8-week multidisciplinary rehabilitation program on the quality of life, functional capacity, cognition, and mental health adaptations in adults recovering from COVID-19 in northern Chile, and (2) propose a personalized model for predicting program dropouts and responses.Methods: A total of 44 subjects were enrolled, forming two groups during the study: a treatment group (n = 32) and a dropout group (n = 12). The treatment group participated in the 8-week multidisciplinary rehabilitation program.Results: The results indicate that (1) After 8 weeks, the quality of life of the patients in the treatment group exhibited significant improvements reflected in all aspects of the Short Form-36 Health Survey (SF36, p < 0.005) and the total score (p < 0.001), with a concurrent decrease in dysfunctionality (p < 0.001). (2) Significant improvements were also observed in various physical performance tests, including the: 6-minute walk test, 1-min sit-to-stand, dynamometry, Tinetti balance, and Berg score (p < 0.001). Moreover, physical therapy led to a reduction in neuropathic symptoms and pain, psychological therapy reduced anxiety and depression, and language therapy enhanced memory and speech (all p < 0.05). (3) Demographic and clinical history characteristics did not predict responses to rehabilitation. (4) A regression model for predicting changes in SF-36 total score, based on physical function, physical role, general health, and mental health, was established based on the data from study (p < 0.01, adjusted R-2 = 0.893). (5) Classification models for predicting dropouts achieved 68% accuracy, with key predictors of treatment adherence including diabetes, hypertension, and dyslipidemia, Tinetti balance, physical role, and vitality of SF36, and performance on the 6-minute walk test and 1-minute sit-to-stand.Conclusions: This study demonstrates significant enhancements in quality of life, improved functional performance, and reductions in mental and cognitive burdens within an 8-week rehabilitation program. Additionally, it is possible to identify patients at risk of dropping out using cost-effective, outpatient, and clinically applicable tests.
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    Self-regulation learning as active inference: dynamic causal modeling of an fMRI neurofeedback task
    (2023) Vargas, Gabriela; Araya, David; Sepulveda, Pradyumna; Rodriguez-Fernandez, Maria; Friston, Karl J.; Sitaram, Ranganatha; El-Deredy, Wael
    Introduction Learning to self-regulate brain activity by neurofeedback has been shown to lead to changes in the brain and behavior, with beneficial clinical and non-clinical outcomes. Neurofeedback uses a brain-computer interface to guide participants to change some feature of their brain activity. However, the neural mechanism of self-regulation learning remains unclear, with only 50% of the participants succeeding in achieving it. To bridge this knowledge gap, our study delves into the neural mechanisms of self-regulation learning via neurofeedback and investigates the brain processes associated with successful brain self-regulation.Methods We study the neural underpinnings of self-regulation learning by employing dynamical causal modeling (DCM) in conjunction with real-time functional MRI data. The study involved a cohort of 18 participants undergoing neurofeedback training targeting the supplementary motor area. A critical focus was the comparison between top-down hierarchical connectivity models proposed by Active Inference and alternative bottom-up connectivity models like reinforcement learning.Results Our analysis revealed a crucial distinction in brain connectivity patterns between successful and non-successful learners. Particularly, successful learners evinced a significantly stronger top-down effective connectivity towards the target area implicated in self-regulation. This heightened top-down network engagement closely resembles the patterns observed in goal-oriented and cognitive control studies, shedding light on the intricate cognitive processes intertwined with self-regulation learning.Discussion The findings from our investigation underscore the significance of cognitive mechanisms in the process of self-regulation learning through neurofeedback. The observed stronger top-down effective connectivity in successful learners indicates the involvement of hierarchical cognitive control, which aligns with the tenets of Active Inference. This study contributes to a deeper understanding of the neural dynamics behind successful self-regulation learning and provides insights into the potential cognitive architecture underpinning this process.
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    Understanding the dosing-time-dependent antihypertensive effect of valsartan and aspirin through mathematical modeling
    (2023) Cortes-Rios, Javiera; Rodriguez-Fernandez, Maria
    Chronopharmacology of arterial hypertension impacts the long-term cardiovascular risk of hypertensive subjects. Therefore, clinical and computational studies have proposed optimizing antihypertensive medications' dosing time (Ta). However, the causes and mechanisms underlying the Ta-dependency antihypertensive effect have not been elucidated. Here we propose using a Ta- dependent effect model to understand and predict the antihypertensive effect of valsartan and aspirin throughout the day in subjects with grade I or II essential hypertension. The model based on physiological regulation mechanisms includes a periodic function for each parameter that changes significantly after treatment. Circadian variations of parameters depending on the dosing time allowed the determination of regulation mechanisms dependent on the circadian rhythm that were most relevant for the action of each drug. In the case of valsartan, it is the regulation of vasodilation and systemic vascular resistance. In the case of aspirin, the antithrombotic effect generates changes in the sensitivity of systemic vascular resistance and heart rate to changes in physical activity. Dosing time-dependent models predict a more significant effect on systemic vascular resistance and blood pressure when administering valsartan or aspirin at bedtime. However, circadian dependence on the regulation mechanisms showed different sensitivity of their circadian parameters and shapes of functions, presenting different phase shifts and amplitude. Therefore, different mechanisms of action and pharmacokinetic properties of each drug can generate different profiles of Ta-dependence of antihypertensive effect and optimal dosing times.
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    Unraveling autonomic cardiovascular control complexity during orthostatic stress: Insights from a mathematical model
    (2024) Hurtado, Martin Miranda; Kaempfer, Rafael; Geddes, Justen R.; Olufsen, Mette S.; Rodriguez-Fernandez, Maria
    Understanding cardiovascular control mediated by the autonomic system remains challenging due to its inherent complexity. Consequently, syndromes such as orthostatic intolerance continue to evoke debates regarding the underlying pathophysiological mechanisms. This study develops a comprehensive mathematical model simulating the control of the sympathetic branch of the cardiovascular system in individuals with normal and abnormal responses to the head-up-tilt test. We recruited four young women: one control, one with vasovagal syncope, one with orthostatic hypertension, and one with orthostatic hypotension, exposing them to an orthostatic head-up tilt test (HUTT) employing non-invasive methods to measure electrocardiography and continuous blood pressure. Our work encompasses a compartmental model formulated using a system of ordinary differential equations. Using heart rate as input, we predict blood pressure, flow, and volume in compartments representing the veins, arteries, heart, and the sympathetic branch of the baroreflex control system. The latter is modulated by high- and low-pressure baroreceptor afferents activated by changes in blood pressure induced by the HUTT. Sensitivity analysis, parameter subset selection, and optimization are employed to estimate patient-specific parameters associated with autonomic performance. The model has seven sensitive and identifiable parameters with significant physiological relevance that can serve as biomarkers for patient classification. Results show that the model can reproduce a spectrum of blood pressure responses successfully, fitting the trajectory displayed by the experimental data. The controller exhibits behavior that emulates the operation of the sympathetic system. These encouraging findings underscore the potential of computational methods in evaluating pathologies associated with autonomic nervous system control, warranting further exploration and novel approaches.