Direct Oral Anticoagulants (DOACs) Use in Patients with Renal Insufficiency and Obesity

The popularity of Direct Oral AntiCoagulants (DOACs) for approved indications has risen dramatically following their introduction to the UK’s National Health Service (NHS) due to their convenience of dosing surpassing warfarin. However, prescribing these medications to high-risk patients has been challenging since mainly due to uncertainties around limited clinical trial data. Patients with chronic kidney disease and obesity pose a risk in particular as DOAC dosing was significantly affected by the variables such as, body weight and renal function. Due to the increased prevalence of CKD and obesity among the NHS patient population, the cost savings of preferring DOACs over warfarin was no longerbeneficial due to higher costs of mortalities and consequential morbidities (e.g., strokes and bleeding events). There are very limited interventional studies to rationalise the sample sizes to generalise findings. Therefore, a retrospective real-world data-driven approach was used in this thesis in an attempt to optimise the DOACs dosing regimen for patients with renal impairment and obesity.The main data-driven techniques used in the thesis employed machine learning and multivariate logistic regression (The systematic review in Chapter 6 describes the potential of in-silico modelling). These were applied to a pre-processed dataset, carefully collected from Calderdale and Huddersfield NHS Foundation Trust Hospitals, and profiled accordingly. The methodology was executed in three phases: overall analysis of the full dataset, comprising different BMI categories (Chapter 3), the data analyses comprising patients with morbid obesity only (Chapter 4), and the analyses of the overall dataset comprising patients with different categories of renal impairment (Chapter 5).The factors that influenced the clinical outcomes (such as mortality, ischaemic stroke, clinically relevant non-major bleeding (CRNMB), thromboembolism, length of stay, and emergency visits) in renal impairment and obesity were then determined following data analysis. Some of these factors, which included the individual DOACs administered, exerted a protective effect, while others worsened the safety and, or efficacy indicators. Also, it was found that some of the machine learning models employed in the thesis predicted the target (i.e., DOAC dose regimen) more accurately than others. Chapter 7 provides a discussion of the findings and makes reference and comparison with the existing evidence in the literature. More importantly, the results from patients with renal impairment and obesity were compared. Overall, the aim of generating real-world evidence for optimising DOACs safety and effectiveness in obesity and renal impairment was achieved. Our findings would support clinicians’ decision-making by reducing the uncertainty in DOACs prescribing.There is a need to validate the thesis findings with well-designed prospective studies. There is also a need to explore pharmacometrics analyses and advanced data-driven techniques such as reinforcement learning to arrive at more precise DOAC dosing estimates for patients with renal impairment and obesity

Complex Assessment of Pilot Fatigue in Terms of Physiological Parameters

Únava pilotů je jedním z hlavních důvodů leteckých nehod, ke kterým došlo v důsledku pochybení lidského činitele. Z tohoto důvodu je v zájmu zachování nejvyšších standardů letové bezpečnosti ve všech fázích letu zásadní být schopen zabránit vzniku únavy nebo alespoň být schopen ji účinně detekovat a následně na tuto skutečnost upozornit posádku, aby byla schopna unaveného člena posádky odstavit. V současnosti existují studie zabývající se detekcí a sledováním únavy pilotů prostřednictvím fyziologických parametrů, jako je srdeční aktivita, pohyby očí, aktivita horních končetin apod. Ze všech dostupných fyziologických měření se pak analýza variability srdečního rytmu (HRV) jeví jako nejvhodnější metoda zkoumání únavy pilota. Ačkoli se k hodnocení únavy používá mnoho parametrů vycházejících z analýzy variability srdečního rytmu, v literatuře neexistuje shoda o tom, které z těchto parametrů variability srdeční frekvence jsou nejdůležitější pro použití při detekci únavy piloty. Na základě tohoto nedostatku informací v kontextu současného stavu poznání je cílem této práce zjistit nejvýznamnější parametry analýzy variability srdečního rytmu, které lze přímo použít při monitorování únavy pilota. Pro účely zisku dat byly provedeny 24hodinové experimenty, při nichž byla sbírána data o srdeční aktivitě 16 subjektů na Ústavu letecké dopravy, Fakulty dopravní, Českého vysokého učení technického v Praze. Údaje o srdeční aktivitě subjektu byly zaznamenány ve formě elektrokardiogramu (EKG), zatímco plnily letové úkoly. První část této práce přináší teoretické základy únavy v prostředí kokpitu a vysvětluje několik metod, které se používají pro analýzu variability srdeční frekvence zaznamenaných signálů EKG. Následující části obsahují metody statistické analýzy používané k zjištění parametrů s nejvyšší importancí. Výsledky naznačují, že parametr pVLF analýzy ve frekvenční a časově-frekvenční doméně a parametr nHF analýzy HRV ve frekvenční doméně jsou parametry s nejvyšší importancí v případě indikace únavy člena letové posádky. Klíčová slova: Únava pilota, fyziologické parametry, srdeční aktivita, variabilita srdečního rytmuPilot fatigue is one of the main reasons of aircraft accidents that were caused due to the human error factors in flight crew. Therefore, in order to maintain the highest standards of flight safety throughout all flight phases, it is crucially important to be able to prevent occurrence of fatigue or at least to be able to efficiently detect it, afterwards alert the crew to eliminate the fatigued member from flying. At present, there are many studies focusing on detection and monitoring of pilot fatigue by tracking pilot’s physiological parameters such as: cardiac activity, eye movements, upper-limb activities etc. Among all those physiological measurements available, heart rate variability analysis seems to be the most accurate method to examine pilot fatigue. Although many indices of heart rate variability analysis are used to evaluate fatigue, there is no consensus in the literature on which of those heart rate variability indices are the most important ones to utilize on determination of pilot fatigue. Based on this lack of information on the current state of the art, the purpose of this thesis is to ascertain the most significant parameters of heart rate variability analysis that can be directly used in determining pilot fatigue. For obtaining data, a 24-hours of cardiac activity measurements were conducted on 16 subjects on a flight simulator located at the Department of Air Transport, Faculty of Transportation Sciences, Czech Technical University in Prague. The subject’s cardiac activity data were recorded in form of electrocardiogram (ECG) while they performed flying tasks. The first part of this thesis delivers a theoretical background on fatigue in the cockpit environment and explains several methods that are used for heart rate variability analysis of the recorded ECG signals. The following parts provide the statistical analysis methods used to find out the most important parameters. The results indicate that pVLF index of the frequency domain and time-frequency domain analysis and nHF parameter of frequency-domain analysis of HRV corresponds to the most important indices which indicate fatigued condition of a flight crew member. Keywords: Pilot fatigue, physiological parameters, cardiac activity, heart rate variabilit

Performance Evaluation of Smart Decision Support Systems on Healthcare

Medical activity requires responsibility not only from clinical knowledge and skill but also on the management of an enormous amount of information related to patient care. It is through proper treatment of information that experts can consistently build a healthy wellness policy. The primary objective for the development of decision support systems (DSSs) is to provide information to specialists when and where they are needed. These systems provide information, models, and data manipulation tools to help experts make better decisions in a variety of situations. Most of the challenges that smart DSSs face come from the great difficulty of dealing with large volumes of information, which is continuously generated by the most diverse types of devices and equipment, requiring high computational resources. This situation makes this type of system susceptible to not recovering information quickly for the decision making. As a result of this adversity, the information quality and the provision of an infrastructure capable of promoting the integration and articulation among different health information systems (HIS) become promising research topics in the field of electronic health (e-health) and that, for this same reason, are addressed in this research. The work described in this thesis is motivated by the need to propose novel approaches to deal with problems inherent to the acquisition, cleaning, integration, and aggregation of data obtained from different sources in e-health environments, as well as their analysis. To ensure the success of data integration and analysis in e-health environments, it is essential that machine-learning (ML) algorithms ensure system reliability. However, in this type of environment, it is not possible to guarantee a reliable scenario. This scenario makes intelligent SAD susceptible to predictive failures, which severely compromise overall system performance. On the other hand, systems can have their performance compromised due to the overload of information they can support. To solve some of these problems, this thesis presents several proposals and studies on the impact of ML algorithms in the monitoring and management of hypertensive disorders related to pregnancy of risk. The primary goals of the proposals presented in this thesis are to improve the overall performance of health information systems. In particular, ML-based methods are exploited to improve the prediction accuracy and optimize the use of monitoring device resources. It was demonstrated that the use of this type of strategy and methodology contributes to a significant increase in the performance of smart DSSs, not only concerning precision but also in the computational cost reduction used in the classification process. The observed results seek to contribute to the advance of state of the art in methods and strategies based on AI that aim to surpass some challenges that emerge from the integration and performance of the smart DSSs. With the use of algorithms based on AI, it is possible to quickly and automatically analyze a larger volume of complex data and focus on more accurate results, providing high-value predictions for a better decision making in real time and without human intervention.A atividade médica requer responsabilidade não apenas com base no conhecimento e na habilidade clínica, mas também na gestão de uma enorme quantidade de informações relacionadas ao atendimento ao paciente. É através do tratamento adequado das informações que os especialistas podem consistentemente construir uma política saudável de bem-estar. O principal objetivo para o desenvolvimento de sistemas de apoio à decisão (SAD) é fornecer informações aos especialistas onde e quando são necessárias. Esses sistemas fornecem informações, modelos e ferramentas de manipulação de dados para ajudar os especialistas a tomar melhores decisões em diversas situações. A maioria dos desafios que os SAD inteligentes enfrentam advêm da grande dificuldade de lidar com grandes volumes de dados, que é gerada constantemente pelos mais diversos tipos de dispositivos e equipamentos, exigindo elevados recursos computacionais. Essa situação torna este tipo de sistemas suscetível a não recuperar a informação rapidamente para a tomada de decisão. Como resultado dessa adversidade, a qualidade da informação e a provisão de uma infraestrutura capaz de promover a integração e a articulação entre diferentes sistemas de informação em saúde (SIS) tornam-se promissores tópicos de pesquisa no campo da saúde eletrônica (e-saúde) e que, por essa mesma razão, são abordadas nesta investigação. O trabalho descrito nesta tese é motivado pela necessidade de propor novas abordagens para lidar com os problemas inerentes à aquisição, limpeza, integração e agregação de dados obtidos de diferentes fontes em ambientes de e-saúde, bem como sua análise. Para garantir o sucesso da integração e análise de dados em ambientes e-saúde é importante que os algoritmos baseados em aprendizagem de máquina (AM) garantam a confiabilidade do sistema. No entanto, neste tipo de ambiente, não é possível garantir um cenário totalmente confiável. Esse cenário torna os SAD inteligentes suscetíveis à presença de falhas de predição que comprometem seriamente o desempenho geral do sistema. Por outro lado, os sistemas podem ter seu desempenho comprometido devido à sobrecarga de informações que podem suportar. Para tentar resolver alguns destes problemas, esta tese apresenta várias propostas e estudos sobre o impacto de algoritmos de AM na monitoria e gestão de transtornos hipertensivos relacionados com a gravidez (gestação) de risco. O objetivo das propostas apresentadas nesta tese é melhorar o desempenho global de sistemas de informação em saúde. Em particular, os métodos baseados em AM são explorados para melhorar a precisão da predição e otimizar o uso dos recursos dos dispositivos de monitorização. Ficou demonstrado que o uso deste tipo de estratégia e metodologia contribui para um aumento significativo do desempenho dos SAD inteligentes, não só em termos de precisão, mas também na diminuição do custo computacional utilizado no processo de classificação. Os resultados observados buscam contribuir para o avanço do estado da arte em métodos e estratégias baseadas em inteligência artificial que visam ultrapassar alguns desafios que advêm da integração e desempenho dos SAD inteligentes. Como o uso de algoritmos baseados em inteligência artificial é possível analisar de forma rápida e automática um volume maior de dados complexos e focar em resultados mais precisos, fornecendo previsões de alto valor para uma melhor tomada de decisão em tempo real e sem intervenção humana

Automated Cardiac Rhythm Diagnosis for Electrophysiological Studies, an Enhanced Classifier Approach

INTRODUCTION Heart function can be impaired by rhythm disturbances (cardiac arrhythmia), illustrated by electrocardiogram (ECG) recordings. Computerised arrhythmia diagnosis is well established for ECG’s but less for intracardiac electrophysiological (EP) testing. Accurate diagnosis is pre-requisite for delivering appropriate treatment to patients however existing algorithms misdiagnose a proportion of arrhythmias. Studies suggested artificial intelligence (AI) classifiers are accurate using ECG and intracardiac electrogram features and reviews suggested new features might augment diagnosis. This study aimed to develop an accurate cardiac rhythm diagnostic algorithm for electrophysiological (EP) studies with potential application as a generic rhythm classifier. METHOD An ethically approved prospective clinical study collected clinical history, right atrial and right ventricular intracardiac electrograms, beat-to-beat cardiac stroke volume, body motion and body temperature data during EP studies. An iterative system development life-cycle was used, including knowledge management and classifier development sub-processes. Domain expert knowledge and clinical arrhythmia diagnosis were modelled, synthesised as AI classifiers and used to classify cardiac rhythms. RESULTS Data collected from 65 patients was pre-processed into instances for classifier inputs. Decision tree, naïve Bayes, neural network, support vector machine and inference engine classifiers developed using Matlab showed good performance and were combined as a production system in a mixture-of-experts multi-classifier system. 18 different rhythms were classified, with the naïve Bayes classifier used to classify 11 rhythms, decision tree 4 rhythms, neural network and support vector machine one each, unclassified instances by the inference engine classifier and final class allocation using decision rule. Production system showed overall correct clasification rate 0.960; error 0.040; mean sensitivity 0.855; mean specificity 0.977; mean κ 0.767; mean positive predictive value 0.792; mean negative predictive value 0.975; mean Pearson’s phi 0.787, with P 0.9 for sinus node dysfunction and atrio-ventricular nodal/ junctional tachycardias. Temperature, accelerometry and QT interval were assessed as features by a comparison of algorithm performances with each feature removed and found not to affect classification performance. An evaluation showed 10 beat analysis performed better than 5 beat analysis. CONCLUSIONS Modelling of the clinical diagnosis process produced an AI based mixture-of-experts multi-classifier system, which accurately diagnosed different 18 cardiac rhythms. The naïve Bayes classifier performed best and classified 11 rhythms. Features for clinical symptoms and predisposing factors, atrial electrogram morphology and changes in stroke volume were found to influence rhythm classification. High performances encourage further development and potential future improvements include: a larger sample dataset; inclusion of His and coronary sinus electrograms; data mining for unknown features with significant influence on diagnosis; binary classification. The aim to classify rhythm using artificial intelligence suitable for use during EP studies was satisfied and the research hypothesis that it outperformed current algorithms was accepted. The system was likely to be able to accept updates but needs conversion as a precursor to use in a live clinical environment

Models and Analysis of Vocal Emissions for Biomedical Applications

The International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications (MAVEBA) came into being in 1999 from the particularly felt need of sharing know-how, objectives and results between areas that until then seemed quite distinct such as bioengineering, medicine and singing. MAVEBA deals with all aspects concerning the study of the human voice with applications ranging from the neonate to the adult and elderly. Over the years the initial issues have grown and spread also in other aspects of research such as occupational voice disorders, neurology, rehabilitation, image and video analysis. MAVEBA takes place every two years always in Firenze, Italy. This edition celebrates twenty years of uninterrupted and succesfully research in the field of voice analysis

Characterization of the Substrate Modification in Patients Undergoing Catheter Ablation of Atrial Fibrillation

Tesis por compendio[ES] La fibrilación auricular (FA) es la arritmia cardíaca más común. A pesar de la gran popularidad de la ablación con catéter (AC) como tratamiento principal, todavía hay margen de mejora. Aunque las venas pulmonares (VPs) son los principales focos de FA, muchos sitios pueden contribuir a su propagación, formando el sustrato de la FA (SFA). El mapeo preciso del SFA y el registro de la modificación del SFA, como marcador positivo después de AC, son fundamentales. Los electrocardiogramas (ECG) y los electrogramas (EGM) se reclutan para este propósito. Los EGM se utilizan para detectar candidatos de AC como áreas que provocan o perpetúan la FA. Por lo tanto, el análisis de EGM es una parte indispensable de AC. Con la capacidad de observar las aurículas globalmente, la principal aplicación de los ECG es evaluar la modificación del SFA analizando las ondas f o P. A pesar del extenso análisis de cualquiera de los tipos de registro, existen algunas brechas. La AC no-VP aumenta el tiempo en quirófano, provocando mayores riesgos y costos. En cuanto al análisis de la modificación del SFA, se utilizan varios umbrales para definir una onda P prolongada. El principal objetivo de la presente Tesis es contribuir al esfuerzo de análisis de SFA y de modificación de SFA. Para ello, la presente Tesis se desarrolló bajo dos hipótesis principales. Que la calidad de la información extraída durante el SFA y el análisis de modificación del SFA se puede mejorar mediante la introducción de pasos innovadores. Además, la combinación de análisis de ECG y EGM puede aumentar la resolución del mapeo y revelar nueva información sobre los mecanismos de FA. Para cumplir con el objetivo principal, el análisis se divide en 4 partes, conformando los 4 capítulos del Compendio de articulos. En primer lugar, se reclutó la dimensión de correlación de grano grueso (DCGG). DCGG localizó de manera confiable EGM complejos y la clasificación por tipos de FA arrojó una precisión del 84 %. Luego, se adoptó un análisis alternativo de la onda P, estudiando por separado su primera y su segunda parte, correspondientes a la aurícula derecha (AD) e izquierda (AI). Los resultados indicaron LA como la principal fuente de modificación del SFA y subrayaron la importancia de estudiar partes integrales de ECG. Los hallazgos de este estudio también sugieren la implementación de partes integrales de ondas P como un posible alivio de las discrepancias en los umbrales de ondas P para definir el tejido fibrótico. Posteriormente, se estudió el efecto diferente del aislamiento de la VP izquierda (AVPI) y derecha (AVPD) sobre la modificación del SFA. AVPI fue la parte crítica, siendo la fuente exclusiva de acortamiento de onda P. El análisis de los registros durante la AC también permitió una observación más cercana de las fluctuaciones de la variabilidad de la frecuencia cardíaca (VFC) a lo largo del procedimiento de CA, lo que reveló información sobre el efecto de la energía de radiofrecuencia (RF) en el tejido auricular. La última parte se centró en el seno coronario (SC), una estructura fundamental en el mapeo de FA para aumentar la resolución de la información. Se definieron los canales más y menos robustos durante el ritmo sinusal (RS) y se investigó la utilidad de SC en la evaluación de la modificación del SFA. Aunque CS no proporcionó una imagen global de la alteración del SFA, pudo registrar con mayor sensibilidad las fluctuaciones en la respuesta auricular durante la AC. Los hallazgos presentados en esta Tesis Doctoral ofrecen una perspectiva alternativa sobre la modificación del SFA y contribuyen al esfuerzo general sobre el mapeo de FA y la evaluación del sustrato posterior a la CAAC, abriendo futuras líneas de investigación hacia una resolución más alta y un mapeo más eficiente de los mecanismos desencadenantes de la FA.[CA] La fibril·lació auricular (FA) és l'arítmia cardíaca més comú. Tot i la gran popularitat de l'ablació amb catèter (AC) com a tractament principal, encara hi ha marge de millora. Tot i que les venes pulmonars (VPs) són els principals focus de FA, molts llocs poden contribuir a la seva propagació, formant el substrat de la FA (SFA). El mapatge precís de l'SFA i el registre de la modificació de l'SFA, com a marcador positiu després d'AC, són fonamentals. Els electrocardiogrames (ECG) i els electrogrames (EGM) es recluten per a aquest propòsit. Els EGM es fan servir per detectar candidats d'AC com a àrees que provoquen o perpetuen la FA. Per tant, lanàlisi dEGM és una part indispensable dAC. Amb la capacitat d'observar les aurícules globalment, la principal aplicació dels ECG és avaluar la modificació de l'SFA analitzant les ones f o P. Tot i l'extensa anàlisi de qualsevol dels tipus de registre, hi ha algunes bretxes. L'AC no-VP augmenta el temps a quiròfan, provocant majors riscos i costos. Pel que fa a l'anàlisi de la modificació de l'SFA, s'utilitzen diversos llindars per definir una ona P perllongada. L'objectiu principal d'aquesta Tesi és contribuir a l'esforç d'anàlisi de SFA i de modificació de SFA. Per això, aquesta Tesi es va desenvolupar sota dues hipòtesis principals. Que la qualitat de la informació extreta durant el SFA i lanàlisi de modificació de lSFA es pot millorar mitjançant la introducció de passos innovadors. A més, la combinació d'anàlisi d'ECG i EGM pot augmentar la resolució del mapatge i revelar informació nova sobre els mecanismes de FA. Per complir amb l'objectiu principal, l'anàlisi es divideix en 4 parts i es conforma els 4 capítols del Compendi d'articles. En primer lloc, es va reclutar la dimensió de correlació de gra gruixut (DCGG). DCGG va localitzar de manera fiable EGM complexos i la classificació per tipus de FA va donar una precisió del 84%. Després, es va adoptar una anàlisi alternativa de l'ona P, estudiant per separat la primera i la segona part corresponents a l'aurícula dreta (AD) i esquerra (AI). Els resultats van indicar LA com la font principal de modificació de l'SFA i van subratllar la importància d'estudiar parts integrals d'ECG. Les troballes d'aquest estudi també suggereixen la implementació de parts integrals d'ones P com a possible alleugeriment de les discrepàncies als llindars d'ones P per definir el teixit fibròtic. Posteriorment, es va estudiar l'efecte diferent de l'aïllament de la VP esquerra (AVPI) i la dreta (AVPD) sobre la modificació de l'SFA. AVPI va ser la part crítica, sent la font exclusiva d'escurçament d'ona P. L'anàlisi dels registres durant l'AC també va permetre una observació més propera de les fluctuacions de la variabilitat de la freqüència cardíaca (VFC) al llarg del procediment de CA , cosa que va revelar informació sobre l'efecte de l'energia de radiofreqüència (RF) en el teixit auricular. L'última part es va centrar al si coronari (SC), una estructura fonamental al mapeig de FA per augmentar la resolució de la informació. Es van definir els canals més i menys robustos durant el ritme sinusal (RS) i es va investigar la utilitat de SC a l'avaluació de la modificació de l'SFA. Tot i que CS no va proporcionar una imatge global de l'alteració de l'SFA, va poder registrar amb més sensibilitat les fluctuacions a la resposta auricular durant l'AC. Les troballes presentades en aquesta Tesi Doctoral ofereixen una perspectiva alternativa sobre la modificació de l'SFA i contribueixen a l'esforç general sobre el mapeig de FA i l'avaluació del substrat posterior a la CAAC, obrint futures línies de recerca cap a una resolució més alta i un mapeig més eficient dels mecanismes desencadenants de la FA.[EN] Atrial fibrillation (AF) is the commonest cardiac arrhythmia. Despite the high popularity of catheter ablation (CA) as the main treatment, there is still room for improvement. Time spent in AF affects the AF confrontation and evolution, with 1,15% of paroxysmal AF patients progressing to persistent annually. Therefore, from diagnosis to follow-up, every aspect that contributes to the AF confrontation is of utmost importance. Although pulmonary veins (PVs) are the main AF foci, many sites may contribute to the AF propagation, by triggering or sustaining the AF, forming the AF substrate. Precise AF substrate mapping and recording of the AF substrate modification, as a positive marker after CA sessions, are critical. Electrocardiograms (ECGs) and electrograms (EGMs) are vastly recruited for this purpose. EGMs are used to detect candidate CA targets as areas that provoke or perpetuate AF. Hence, EGMs analysis is an indispensable part of the CA procedure. With the ability to observe the atria globally, ECGs' main application is to assess the AF substrate modification by analyzing f- or P-waves from recordings before and after CA. Despite the extensive analysis on either recording types, some gaps exist. Non-PV CA increases the time in operation room, provoking higher risks and costs. Furthermore, whether non-PV CA is beneficial is under dispute. As for the AF substrate modification analysis, various thresholds are used to define a prolonged P-wave, related with poor CA prognostics. The main objective of the present Thesis is to contribute to the effort of AF substrate and AF substrate modification analysis. For this purpose, the present Thesis was developed under two main hypotheses. That the information quality extracted during AF substrate and AF substrate modification analysis can be improved by introducing innovative steps. Also, that combining ECG and EGM analysis can augment the mapping resolution and reveal new information regarding AF mechanisms. To accomplish the main objective, the analysis is split in 4 parts, forming the 4 chapters of the Compendium of publications. Firstly, coarse-grained correlation dimension (CGCD) was recruited. CGCD reliably localized highly complex EGMs and classification by AF types yielded 84% accuracy. Then, an alternative P-wave analysis was suggested, studying separately the first and second P-wave parts, corresponding to the right (RA) and left (LA) atrium. The findings indicated LA as the main AF substrate modification source and underlined the importance of studying integral ECG parts. The findings of this study additionally suggest the implementation of integral P-wave parts as a possible alleviation for the discrepancies in P-wave thresholds to define fibrotic tissue. Afterwards, the different effect of left (LPVI) and right pulmonary vein isolation (RPVI) on the AF substrate modification was studied. LPVI was the critical part, being the exclusive source of P-wave shortening. Analysis of recordings during CA also allowed a closer observation of the heart rate variability (HRV) fluctuations throughout the CA procedure, revealing information on the effect of radiofrequency (RF) energy on the atrial tissue. The last part was focused on coronary sinus (CS), a fundamental structure in AF mapping to increase the information resolution. The most and least robust channels during sinus rhythm (SR) were defined and the utility of CS in AF substrate modification evaluation was investigated. Although CS did not provide a global picture of the AF substrate alteration, it was able to record with higher sensitivity the fluctuations in the atrial response during the application of RF energy. The findings presented in this Doctoral Thesis offer an alternative perspective on the AF substrate modification and contribute to the overall effort on AF mapping and post-CA substrate evaluation, opening future lines of research towards a higher resolution and more efficient mapping of the AF drivers.Vraka, A. (2022). Characterization of the Substrate Modification in Patients Undergoing Catheter Ablation of Atrial Fibrillation [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/191410Compendi

Hõljumtoiduliste põhjaloomade levik, toitumine ja elupaik madalas rannikumeres

Väitekirja elektrooniline versioon ei sisalda publikatsiooneHõljumtoiduliste põhjaloomade levik, toitumine ja elupaik madalas rannikumeres. Hõljumtoidulised põhjaloomad on tähtis funktsionaalne rühm rannikuökosüsteemides. Käesoleva doktoritöö eesmärk oli uurida, kuidas erinevad abiootilised ja biootilised tegurid ning nende interaktsioonid mõjutavad hõljumtoiduliste põhjaloomade levikut, toitumist ja kooseksisteerimist ning kuidas saab rakendada olemasolevaid andmeid nende kohta looduskaitseks. Uurimisalased liigid on söödav rannakarp, muutlik rändkarp ja harilik tõruvähk, mis on tähtsad elupaiga moodustajad Läänemeres. Rändkarbi toitumise hindamiseks korraldasime Pärnu lahes in situ ja ex situ katseseeriad looduslikest populatsioonidest kogutud isenditega. Tulemused näitasid, et isegi tugevalt eutrofeerunud veekogus sõltus rändkarpide toitumine peamiselt toidu kättesaadavusest. Samuti mõjutasid rändkarbi toitumist vee soolsus ja tuulest põhjustatud setete resuspensioon. Rannakarpide levikumustrite kirjeldamiseks kasutati olemasolevate arvukuse ja biomassi andmete põhjal Boosted Regressioon Trees masinõpe modelleerimist. Loodud mudelid näitasid, et põhjasubstraat, avatus lainetusele ja soolsus mängivad Eesti meres rannakarpide leviku kujundamises põhirolli. Bioloogilised interaktsioonid põhjataimestikuga mõjutasid rannakarpide biomassi nende elupaiga ulatuses. Põhjalikud teadmised liigi keskkonnaeelistustest ja levikumustrist panustavad nende liikide seire korraldamisse ja elupaikade säilitamisse. Rändkarbi ja tõruvähi kinnitumise ning keskkonnatingimuste vaheliste seoste uurimiseks teostati Pärnu lahes katse betoonist tehissubstraatidega. Tulemused näitasid, et loomade kinnitumist määrasid tehissubstraadi pinnaorientatsioon, bioloogilised interaktsioonid rändkarbi ja tõruvähi vahel ja erinevad keskkonnategurid. Rändkarpi mõjutas kõige rohkem temperatuur, tõruvähki aga avatus lainetusele. Tulemusi saab rakendada keskkonnaseisundi parandamise eesmärkidel, liikide leviku muutuste ennustamisel või võõrliikide seirekavade väljatöötamisel. Hõljumtoiduliste põhjaloomade elupaiga hindamiseks kasutati arvukuse ja biomassi olemasolevaid andmeid aastatest 1995-2014. 2015 aastal kogutud andmete põhjal hinnati hõljumtoiduliste põhjaloomade elupaiga seisundit heaks kümnes jaamas üheteistkümnest. Kogutud andmed aitavad kaasa looduskaitseliste tegevuste läbiviimisel ning soodustavad ökosüsteemi probleemide varajast avastamistDistribution, feeding and habitat of benthic suspension feeders in a shallow coastal sea. Benthic suspension feeders (BSF) are an important ecological guild in coastal ecosystems, functioning as a natural biological filter and providing habitat and food for other species. Human activities are causing rapid changes in coastal ecosystems with a wide range of ecological responses of species and communities. Detailed knowledge of the ecology of BSF species is highly important for human pressure mitigation. Study species were the non-indigenous Amphibalanus improvisus and Dreissena polymorpha; and the native Mytilus trossulus. The objectives of this study were to explore how various environmental gradients and biotic interactions affect BSF communities. Feeding rates of D. polymorpha were derived from field populations by measuring the content of algal pigments in specimens and their biodeposits in their natural habitat in the Pärnu Bay and separate mesocosms. Mussel feeding was mainly explained by chlorophyll a concentrations, salinity, and wind-induced disturbances. The study showed that mussel feeding was principally food regulated even in a highly eutrophic system. This supports their potential value as biofilters in the manipulation of eutrophic waterbodies. To explain the distribution and biomass of M. trossulus on a larger scale, we analysed existing data on the occurrence and biomass of M. trossulus for the period of 2005–2009 in the Estonian coastal sea. Distribution patterns of M. trossulus were manly explained by substrate availability, wave exposure, salinity, and biotic interactions with algae. These results provide a useful basis for predicting future bay mussel distribution in the Baltic Sea in the changing climate conditions. Colonization of A. improvisus and D. polymorpha was studied in in situ experiments in the Pärnu Bay using artificial substrate. The results showed that local colonization was structured by species-specific microhabitat use coupled with biotic interactions between the two species, and individual and interactive effects of environmental gradients. These results can be considered when designing artificial structures for monitoring or biomanipulation purposes in the respective sea area. In order to evaluate BSFs’ habitat quality we used available scientific data for the period of 1995–2014 to define the relevant assessment criteria in the Estonia coastal sea. We evaluated BSFs’ habitat to be in good status in 10 out of 11 stations based on the data collected in 2015. The use of existing field data provides a robust foundation for habitat quality assessment