Caracterització multiescala de dades fisiològiques en la cardiologia translacional
(English) This thesis aims to explore cardiac cell physiology across various scales, also known as translational cardiology. The research employs novel computational techniques from Biomedical engineering and utilizes medical signal and image databases to investigate the root causes of cardiac contr...
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| Tipo de recurso: | tesis doctoral |
| Estado: | Versión publicada |
| Fecha de publicación: | 2023 |
| País: | España |
| Institución: | CBUC, CESCA |
| Repositorio: | TDR. Tesis Doctorales en Red |
| OAI Identifier: | oai:www.tdx.cat:10803/690430 |
| Acceso en línea: | http://hdl.handle.net/10803/690430 https://dx.doi.org/10.5821/dissertation-2117-405580 |
| Access Level: | acceso abierto |
| Palabra clave: | Àrees temàtiques de la UPC::Enginyeria biomèdica 616.1 62 |
| Sumario: | (English) This thesis aims to explore cardiac cell physiology across various scales, also known as translational cardiology. The research employs novel computational techniques from Biomedical engineering and utilizes medical signal and image databases to investigate the root causes of cardiac contractile system dysfunctions. The study proposes new biomarkers and measures that could aid in diagnosing, prognosticating, and treating cardiac diseases. Besides chapter 1, devoted to the introduction and description of the structure of the thesis, it consists of three main chapters, each of which deals with different stages of cardiac physiology: Chapter 2, focuses on the analysis of currents circulating in ion channels in human atrial cardiomyocytes (HAM), on an intracellular scale. The aim of this chapter is to develop a computational platform for transmembrane ionic currents. This platform allows us to process and extract the kinetic and morphological characteristics of each signal peak that have allowed us to automatically differentiate between healthy and pathological regimes. In conditions where the cardiac cell is stimulated with a time-varying voltage, beat-to-beat electrical alternation phenomena appear, which are indicative of arrhythmogenesis. Based on the characteristics extracted and validated with a synthetic model, a new biomarker has been designed, called the "alternation index", which allows a satisfactory quantification of the degree of alternation between electrical beats. Chapter 3, focuses into the mechanical analysis of single mouse ventricular cardiomyocytes (MVM) at a cellular level, using optical and calcium imaging recordings. The chapter aims to develop a computational video platform using the Digital Image Correlation (DIC) algorithm. This platform calculates displacement fields, strain fields, and sarcolemma length variations during contraction in an isolated cardiomyocyte. By utilizing this technique, potential disruptions in the latently contracting cardiomyocyte during long-term recordings are eliminated, allowing simultaneous monitoring of cardiac contraction and intracellular calcium in a non-invasive and label-free way. The methodology is validated using synthetically created data, and the study applies real experimental data, correlating contractile properties with the calcium signal, Ca2+. Chapter 4 focuses on the analysis of OF1 mouse atrial mechanical contraction signals on a tissue scale. The aim of this chapter is to develop a computational platform that utilizes artificial intelligence (AI) to automatically detect any mechanical contraction irregularities that could cause heart contractile system diseases, such as atrial fibrillation (AF). The study compares traditional AI algorithms based on machine learning (ML) to more advanced ones based on deep learning (DL). This chapter starts with the classification and then the detection of anomalies in mechanical shrinkage signals. First, contraction signals are automatically classified into those without anomalies and those with anomalies, creating a database of labelled contraction records and using supervised learning to determine which AI categorisation algorithms provide the highest accuracy. Secondly, artificial intelligence is used to train an anomaly detector, which determines the precise time at which the anomaly occurs. The various anomalous phenomena observed in the laboratory experiments are modelled in a synthetic database that is used to evaluate and train different anomaly detectors. Finally, the most accurate detector is validated with real experimental signals. |
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