Nonlinear Systems in Healthcare towards Intelligent Disease Prediction

Healthcare is one of the key fields that works quite strongly with advanced analytical techniques for prediction of diseases and risks. Data being the most important asset in recent times, a huge amount of health data is being collected, thanks to the recent advancements of IoT, smart healthcare, etc. But the focal objective lies in making sense of that data and to obtain knowledge, using intelligent analytics. Nonlinear systems find use specifically in this field, working closely with health data. Using advanced methods of machine learning and computational intelligence, nonlinear analysis performs a key role in analyzing the enormous amount of data, aimed at finding important patterns and predicting diseases. Especially in the field of smart healthcare, this chapter explores some aspects of nonlinear systems in predictive analytics, providing a holistic view of the field as well as some examples to illustrate such intelligent systems toward disease prediction

Fractal Analysis of Cardiovascular Signals Empowering the Bioengineering Knowledge

The cardiovascular system is composed of a complex network of vessels, where highly uniform hierarchical branching structures are regulated by the anatomy and local flow requirements. Arteries bifurcate many times before they become capillaries where the scaling factor of vessel length, diameter and angle between two children branches is established at each level of recurrence. This behaviour can be easily described using a fractal scaling principle. Moreover, it was observed that the basic pattern of blood distribution is also fractal, imposed both by the anatomy of the vascular tree and the local regulation of vascular tone. In this chapter, arterial physiology was analysed, where waveform complexity of arterial pressure time series was related to arterial stiffness changes, pulse pressure variations and the presence wave reflection. Fractal dimension was used as a nonlinear measure, giving place to a ‘holistic approach of fractal dimension variations throughout the arterial network’, both in health and disease

Analysis of ischaemic crisis using the informational causal entropy-complexity plane

In the present work, an ischaemic process, mainly focused on the reperfusion stage, is studied using the informational causal entropy-complexity plane. Ischaemic wall behavior under this condition was analyzed through wall thickness and ventricular pressure variations, acquired during an obstructive flow maneuver performed on left coronary arteries of surgically instrumented animals. Basically, the induction of ischaemia depends on the temporary occlusion of left circumflex coronary artery (which supplies blood to the posterior left ventricular wall) that lasts for a few seconds. Normal perfusion of the wall was then reestablished while the anterior ventricular wall remained adequately perfused during the entire maneuver. The obtained results showed that system dynamics could be effectively described by entropy-complexity loops, in both abnormally and well perfused walls. These results could contribute to making an objective indicator of the recovery heart tissues after an ischaemic process, in a way to quantify the restoration of myocardial behavior after the supply of oxygen to the ventricular wall was suppressed for a brief period.Fil: Legnani, Walter. Universidad Tecnológica Nacional. Facultad Regional Buenos Aires; Argentina. Universidad Nacional de Lanús; ArgentinaFil: Traversaro Varela, Francisco. Instituto Tecnológico de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Redelico, Francisco Oscar. Hospital Italiano; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Quilmes; ArgentinaFil: Cymberknop, Leandro Javier. Instituto Tecnologico de Buenos Aires. Departamento de Bioingenieria; Argentina. Universidad Tecnológica Nacional. Facultad Regional Buenos Aires; ArgentinaFil: Armentano, Ricardo Luis. Universidad Tecnológica Nacional. Facultad Regional Buenos Aires; Argentina. Instituto Tecnologico de Buenos Aires. Departamento de Bioingenieria; ArgentinaFil: Rosso, Osvaldo Aníbal. Universidad de los Andes; Chile. Universidade Federal de Alagoas; Brasil. Hospital Italiano; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

Aplicación de Conceptos del Cálculo Diferencial al Estudio de la Curva de Presión Arterial. Una Experiencia Interdisciplinar

During the last years, new forms of pedagogy have been implemented in the engineering area, given that the real challenges that the future engineer will face must be addressed in an interdisciplinary way. For this reason, from the first years of his career and from the basic sciences, it is convenient for the student to deal with difficulties coming from other scenarios and thus be able to establish bridges between the different sciences and enhance the contributions of each one of them. The main objective of this work is to describe the result of an interdisciplinary experience in which the student was involved in applying mathematical concepts to a biological discipline. The experience was carried out with students of an Advanced Calculus course, corresponding to the first year of the different Engineering careers of the Buenos Aires Faculty at the Universidad Tecnológica Nacional, where an activity related to the concept of “engineering applied to the modeling of the cardiovascular system” was presented to them. To address it, 58 students had to apply concepts studied in the subject and incorporate those linked to human physiology. They also participated in both general data collection and assistance in data acquisition, which were later analyzed in group work. A descriptive methodology based on an individual questionnaire was considered, in order to complete the evaluation of the experience. The obtained results evidenced a high interest on the part of the students in dealing with problematic situations, with an 89% approval in relation to the importance to establish relationships between the different areas of knowledge. The activity was designed within the framework of the research and development project: "Use of interdisciplinary problems in mathematics subjects in engineering careers" together with the Group of Research and Development in Bioengineering, belonging to the same institution, and by virtue of the expertise of its members in topics related to cardiovascular health. Indeed, the obtained results demonstrate that the implementation of research-based teaching strategies, promotes an increase in student attention, a greater participation and provides a direct application of the studied mathematical tools.En estos últimos años se han implementado nuevas formas de pedagogía en el área de ingeniería, habida cuenta que los desafíos reales que enfrentará el futuro ingeniero deberán ser abordados de manera interdisciplinaria. Es por ello que desde los primeros años de su carrera y ya desde las ciencias básicas, es conveniente que el estudiante pueda enfrentar problemas procedentes de otros escenarios y así poder establecer puentes entre las distintas ciencias y potenciar los aportes de cada una de ellas. El objetivo principal del presente trabajo es describir el resultado de una experiencia interdisciplinar en la que se involucra al estudiante en la aplicación de conceptos matemáticos a una disciplina biológica. La experiencia se llevó a cabo con estudiantes cursantes de la asignatura Análisis Matemático I, correspondiente al primer año de las distintas carreras de Ingeniería de la Facultad Buenos Aires en la Universidad Tecnológica Nacional, a quienes se les presentó una actividad relacionada con el concepto de “ingeniería aplicada al modelado del sistema cardiovascular”. Para abordarla, 58 estudiantes debieron aplicar conceptos estudiados en la asignatura e incorporar aquellos ligados a la fisiología humana. Asimismo, participaron tanto en la recolección general de datos como en la asistencia para la adquisición de los mismos, que luego se analizaron en trabajos grupales. Se consideró una metodología descriptiva a partir de un cuestionario individual, con el objeto de completar la evaluación de la experiencia. Los resultados obtenidos evidenciaron un elevado interés por parte de los estudiantes en el abordaje de situaciones problemáticas, con un 89% de aprobación en relación a la importancia de establecer relaciones entre las distintas áreas de conocimiento. La actividad fue diseñada en el marco del proyecto de investigación y desarrollo: "Empleo de problemas interdisciplinarios en asignaturas de matemática en carreras de ingeniería" conjuntamente con el Grupo de Investigación y Desarrollo en Bioingeniería, perteneciente a la misma institución y en virtud de la experticia de sus integrantes en tópicos relacionados con la salud cardiovascular. Efectivamente, los resultados obtenidos demuestran que la implementación de estrategias interdisciplinares de enseñanza basadas en situaciones problemáticas propicia un incremento en la atención por parte del estudiante, una mayor participación y proporciona una aplicación directa de las herramientas matemáticas estudiadas

Central Arterial Dynamic Evaluation from Peripheral Blood Pressure Waveforms Using CycleGAN: An In Silico Approach

Arterial stiffness is a major condition related to many cardiovascular diseases. Traditional approaches in the assessment of arterial stiffness supported by machine learning techniques are limited to the pulse wave velocity (PWV) estimation based on pressure signals from the peripheral arteries. Nevertheless, arterial stiffness can be assessed based on the pressure–strain relationship by analyzing its hysteresis loop. In this work, the capacity of deep learning models based on generative adversarial networks (GANs) to transfer pressure signals from the peripheral arterial region to pressure and area signals located in the central arterial region is explored. The studied signals are from a public and validated virtual database. Compared to other works in which the assessment of arterial stiffness was performed via PWV, in the present work the pressure–strain hysteresis loop is reconstructed and evaluated in terms of classical machine learning metrics and clinical parameters. Least-square GAN (LSGAN) and Wasserstein GAN with gradient penalty (WGAN-GP) adversarial losses are compared, yielding better results with LSGAN. LSGAN mean ± standard deviation of error for pressure and area pulse waveforms are 0.8 ± 0.4 mmHg and 0.1 ± 0.1 cm2, respectively. Regarding the pressure–strain elastic modulus, it is achieved a mean absolute percentage error of 6.5 ± 5.1%. GAN-based deep learning models can recover the pressure–strain loop of central arteries while observing pressure signals from peripheral arteries

Predictive Risk Analysis for Liver Transplant Patients — eHealth Model Under National Liver Transplant Program, Uruguay

Recent years have seen a phenomenal change in healthcare paradigms and data analytics clubbed with computational intelligence has been a key player in this field. One of the main objectives of incorporating computational intelligence in healthcare analytics is to obtain better insights about the patients and proffer more efficient treatment. This work is based on liver transplant patients under the National Liver Transplant Program of Uruguay, considering in detail the health parameters of the patients. Applying computational intelligence helped to separate the cohort into clusters, thereby facilitating the efficient risk-group analysis of the patients assessed under the liver transplantation program with respect to their corresponding health parameters, in a predictive pre-transplant perspective. Also, this marks the foundation of Clinical Decision Support Systems in liver transplantation, which act as an assistive tool for the medical personnel in getting a deeper insight to patient health data and thanks to the holistic visualization of the healthcare scenario, also help in choosing a more efficient and personalized treatment strategy.Agencia Nacional de Investigación e Innovación (ANII), UruguayUniversidad Tecnológica Nacional, Buenos Aires, ArgentinaUniversidad de la República, UruguayDirección Nacional de Sanidad de la Fuerzas Armadas, Montevideo, Urugua

Central Arterial Dynamic Evaluation from Peripheral Blood Pressure Waveforms Using CycleGAN: An In Silico Approach

Arterial stiffness is a major condition related to many cardiovascular diseases. Traditional approaches in the assessment of arterial stiffness supported by machine learning techniques are limited to the pulse wave velocity (PWV) estimation based on pressure signals from the peripheral arteries. Nevertheless, arterial stiffness can be assessed based on the pressure–strain relationship by analyzing its hysteresis loop. In this work, the capacity of deep learning models based on generative adversarial networks (GANs) to transfer pressure signals from the peripheral arterial region to pressure and area signals located in the central arterial region is explored. The studied signals are from a public and validated virtual database. Compared to other works in which the assessment of arterial stiffness was performed via PWV, in the present work the pressure–strain hysteresis loop is reconstructed and evaluated in terms of classical machine learning metrics and clinical parameters. Least-square GAN (LSGAN) and Wasserstein GAN with gradient penalty (WGAN-GP) adversarial losses are compared, yielding better results with LSGAN. LSGAN mean ± standard deviation of error for pressure and area pulse waveforms are 0.8 ± 0.4 mmHg and 0.1 ± 0.1 cm2, respectively. Regarding the pressure–strain elastic modulus, it is achieved a mean absolute percentage error of 6.5 ± 5.1%. GAN-based deep learning models can recover the pressure–strain loop of central arteries while observing pressure signals from peripheral arteries

Vascular reactivity in healthy subjects: simultaneous characterization of arterial pressure and diameter time profiles

Endothelial cells are involved in the local regulation of blood flow and vessel diameter, playing multiple roles, both in health and disease. Changes in endothelial functions are defined as endothelial dysfunction (ED). In vivo non-invasive evaluation of ED and vascular reactivity can be performed by means of different approaches, where Flow Mediated Dilation (FMD) and Peripheral Arterial Tonometry (PAT) measurements (in terms of a post occlusive reactive hyperemia maneuver, PORH) are considered the most relevant. The main objective of this study was to perform the evaluation of FMD and PAT simultaneously, in healthy men and women, in order to characterize the pressure and diameter time profiles during PORH. Different exponential functions were fitted to systolic pressure and diameter responses, suggesting a differentiated vascular behavior between genders.Fil: Arbeitman, Claudia Roxana. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Medicina Traslacional, Trasplante y Bioingeniería. Fundación Favaloro. Instituto de Medicina Traslacional, Trasplante y Bioingeniería; ArgentinaFil: Cymberknop, Leandro Javier. Universidad Favaloro; Argentina. Universidad Tecnológica Nacional. Facultad Regional Buenos Aires; ArgentinaFil: Farro, Ignacio. Universidad de la República; UruguayFil: Cardelino, J.. Universidad de la República; UruguayFil: Armentano, Ricardo Luis. Universidad de la República; Uruguay. Universidad Favaloro; Argentina. Universidad Tecnológica Nacional. Facultad Regional Buenos Aires; Argentin