Application of Hierarchical Temporal Memory to Anomaly Detection of Vital Signs for Ambient Assisted Living

This thesis presents the development of a framework for anomaly detection of vital signs for an Ambient Assisted Living (AAL) health monitoring scenario. It is driven by spatiotemporal reasoning of vital signs that Cortical Learning Algorithms (CLA) based on Hierarchal Temporal Memory (HTM) theory undertakes in an AAL health monitoring scenario to detect anomalous data points preceding cardiac arrest. This thesis begins with a literature review on the existing Ambient intelligence (AmI) paradigm, AAL technologies and anomaly detection algorithms used in a health monitoring scenario. The research revealed the significance of the temporal and spatial reasoning in the vital signs monitoring as the spatiotemporal patterns of vital signs provide a basis to detect irregularities in the health status of elderly people. The HTM theory is yet to be adequately deployed in an AAL health monitoring scenario. Hence HTM theory, network and core operations of the CLA are explored. Despite the fact that standard implementation of the HTM theory comprises of a single-level hierarchy, multiple vital signs, specifically the correlation between them is not sufficiently considered. This insufficiency is of particular significance considering that vital signs are correlated in time and space, which are used in the health monitoring applications for diagnosis and prognosis tasks. This research proposes a novel framework consisting of multi-level HTM networks. The lower level consists of four models allocated to the four vital signs, Systolic Blood Pressure (SBP), Diastolic Blood Pressure (DBP), Heart Rate (HR) and peripheral capillary oxygen saturation (SpO2) in order to learn the spatiotemporal patterns of each vital sign. Additionally, a higher level is introduced to learn spatiotemporal patterns of the anomalous data point detected from the four vital signs. The proposed hierarchical organisation improves the model’s performance by using the semantically improved representation of the sensed data because patterns learned at each level of the hierarchy are reused when combined in novel ways at higher levels. To investigate and evaluate the performance of the proposed framework, several data selection techniques are studied, and accordingly, a total record of 247 elderly patients is extracted from the MIMIC-III clinical database. The performance of the proposed framework is evaluated and compared against several state-of-the-art anomaly detection algorithms using both online and traditional metrics. The proposed framework achieved 83% NAB score which outperforms the HTM and k-NN algorithms by 15%, the HBOS and INFLO SVD by 16% and the k-NN PCA by 21% while the SVM scored 34%. The results prove that multiple HTM networks can achieve better performance when dealing with multi-dimensional data, i.e. data collected from more than one source/sensor

GIS in Healthcare

The landscape of healthcare is dynamic, gradually becoming more complicated with factors beyond simple supply and demand. Similar to the diversity of social, political and economic contexts, the practical utilization of healthcare resources also varies around the world. However, the spatial components of these contexts, along with aspects of supply and demand, can reveal a common theme among these factors. This book presents advancements in GIS applications that reveal the complexity of and solutions for a dynamic healthcare landscape

Novel Cardiac Mapping Approaches and Multimodal Techniques to Unravel Multidomain Dynamics of Complex Arrhythmias Towards a Framework for Translational Mechanistic-Based Therapeutic Strategies

[ES] Las arritmias cardíacas son un problema importante para los sistemas de salud en el mundo desarrollado debido a su alta incidencia y prevalencia a medida que la población envejece. La fibrilación auricular (FA) y la fibrilación ventricular (FV) se encuentran entre las arritmias más complejas observadas en la práctica clínica. Las consecuencias clínicas de tales alteraciones arrítmicas incluyen el desarrollo de eventos cardioembólicos complejos en la FA, y repercusiones dramáticas debido a procesos fibrilatorios sostenidos que amenazan la vida infringiendo daño neurológico tras paro cardíaco por FV, y que pueden provocar la muerte súbita cardíaca (MSC). Sin embargo, a pesar de los avances tecnológicos de las últimas décadas, sus mecanismos intrínsecos se comprenden de forma incompleta y, hasta la fecha, las estrategias terapéuticas carecen de una base mecanicista suficiente y poseen bajas tasas de éxito. Entre los mecanismos implicados en la inducción y perpetuación de arritmias cardíacas, como la FA, se cree que las dinámicas de las fuentes focales y reentrantes de alta frecuencia, en sus diferentes modalidades, son las fuentes primarias que mantienen la arritmia. Sin embargo, se sabe poco sobre los atractores, así como, de la dinámica espacio-temporal de tales fuentes fibrilatorias primarias, específicamente, las fuentes focales o rotacionales dominantes que mantienen la arritmia. Por ello, se ha desarrollado una plataforma computacional, para comprender los factores (activos, pasivos y estructurales) determinantes, y moduladores de dicha dinámica. Esto ha permitido establecer un marco para comprender la compleja dinámica de los rotores con énfasis en sus propiedades deterministas para desarrollar herramientas basadas en los mecanismos para ayuda diagnóstica y terapéutica. Comprender los procesos fibrilatorios es clave para desarrollar marcadores y herramientas fisiológica- y clínicamente relevantes para la ayuda de diagnóstico temprano. Específicamente, las propiedades espectrales y de tiempo-frecuencia de los procesos fibrilatorios han demostrado resaltar el comportamiento determinista principal de los mecanismos intrínsecos subyacentes a las arritmias y el impacto de tales eventos arrítmicos. Esto es especialmente relevante para determinar el pronóstico temprano de los supervivientes comatosos después de un paro cardíaco debido a fibrilación ventricular (FV). Las técnicas de mapeo electrofisiológico, el mapeo eléctrico y óptico cardíaco, han demostrado ser recursos muy valiosos para dar forma a nuevas hipótesis y desarrollar nuevos enfoques mecanicistas y estrategias terapéuticas mejoradas. Esta tecnología permite además el trabajo multidisciplinar entre clínicos y bioingenieros, para el desarrollo y validación de dispositivos y metodologías para identificar biomarcadores multi-dominio que permitan rastrear con precisión la dinámica de las arritmias identificando fuentes dominantes y atractores con alta precisión para ser dianas de estrategias terapeúticas innovadoras. Es por ello que uno de los objetivos fundamentales ha sido la implantación y validación de nuevos sistemas de mapeo en distintas configuraciones que sirvan de plataforma de desarrollo de nuevas estrategias terapeúticas. Aunque el mapeo panorámico es el método principal y más completo para rastrear simultáneamente biomarcadores electrofisiológicos, su adopción por la comunidad científica es limitada principalmente debido al coste elevado de la tecnología. Aprovechando los avances tecnológicos recientes, nos hemos enfocado en desarrollar, y validar, sistemas de mapeo óptico de alta resolución para registro panorámico cardíaco, utilizando modelos clínicamente relevantes para la investigación básica y la bioingeniería.[CA] Les arítmies cardíaques són un problema important per als sistemes de salut del món desenvolupat a causa de la seva alta incidència i prevalença a mesura que la població envelleix. La fibril·lació auricular (FA) i la fibril·lació ventricular (FV), es troben entre les arítmies més complexes observades a la pràctica clínica. Les conseqüències clíniques d'aquests trastorns arítmics inclouen el desenvolupament d'esdeveniments cardioembòlics complexos en FA i repercussions dramàtiques a causa de processos fibril·latoris sostinguts que posen en perill la vida amb danys neurològics posteriors a la FV, que condueixen a una aturada cardíaca i a la mort cardíaca sobtada (SCD). Tanmateix, malgrat els avanços tecnològics de les darreres dècades, els seus mecanismes intrínsecs s'entenen de forma incompleta i, fins a la data, les estratègies terapèutiques no tenen una base mecanicista suficient i tenen baixes taxes d'èxit. La majoria dels avenços en el desenvolupament de biomarcadors òptims i noves estratègies terapèutiques en aquest camp provenen de tècniques valuoses en la investigació de mecanismes d'arítmia. Entre els mecanismes implicats en la inducció i perpetuació de les arítmies cardíaques, es creu que les fonts primàries subjacents a l'arítmia són les fonts focals reingressants d'alta freqüència dinàmica i AF, en les seves diferents modalitats. Tot i això, se sap poc sobre els atractors i la dinàmica espaciotemporal d'aquestes fonts primàries fibril·ladores, específicament les fonts rotacionals o focals dominants que mantenen l'arítmia. Per tant, s'ha desenvolupat una plataforma computacional per entendre determinants actius, passius, estructurals i moduladors d'aquestes dinàmiques. Això va permetre establir un marc per entendre la complexa dinàmica multidomini dels rotors amb ènfasi en les seves propietats deterministes per desenvolupar enfocaments mecanicistes per a l'ajuda i la teràpia diagnòstiques. La comprensió dels processos fibril·latoris és clau per desenvolupar puntuacions i eines rellevants fisiològicament i clínicament per ajudar al diagnòstic precoç. Concretament, les propietats espectrals i de temps-freqüència dels processos fibril·latoris han demostrat destacar un comportament determinista important dels mecanismes intrínsecs subjacents a les arítmies i l'impacte d'aquests esdeveniments arítmics. Mitjançant coneixements previs, processament de senyals, tècniques d'aprenentatge automàtic i anàlisi de dades, es va desenvolupar una puntuació de risc mecanicista a la aturada cardíaca per FV. Les tècniques de cartografia òptica cardíaca i electrofisiològica han demostrat ser recursos inestimables per donar forma a noves hipòtesis i desenvolupar nous enfocaments mecanicistes i estratègies terapèutiques. Aquesta tecnologia ha permès durant molts anys provar noves estratègies terapèutiques farmacològiques o ablatives i desenvolupar mètodes multidominis per fer un seguiment precís de la dinàmica d'arrímies que identifica fonts i atractors dominants. Tot i que el mapatge panoràmic és el mètode principal per al seguiment simultani de paràmetres electrofisiològics, la seva adopció per part de la comunitat multidisciplinària d'investigació cardiovascular està limitada principalment pel cost de la tecnologia. Aprofitant els avenços tecnològics recents, ens centrem en el desenvolupament i la validació de sistemes de mapes òptics de baix cost per a imatges panoràmiques mitjançant models clínicament rellevants per a la investigació bàsica i la bioenginyeria.[EN] Cardiac arrhythmias are a major problem for health systems in the developed world due to their high incidence and prevalence as the population ages. Atrial fibrillation (AF) and ventricular fibrillation (VF), are amongst the most complex arrhythmias seen in the clinical practice. Clinical consequences of such arrhythmic disturbances include developing complex cardio-embolic events in AF, and dramatic repercussions due to sustained life-threatening fibrillatory processes with subsequent neurological damage under VF, leading to cardiac arrest and sudden cardiac death (SCD). However, despite the technological advances in the last decades, their intrinsic mechanisms are incompletely understood, and, to date, therapeutic strategies lack of sufficient mechanistic basis and have low success rates. Most of the progress for developing optimal biomarkers and novel therapeutic strategies in this field has come from valuable techniques in the research of arrhythmia mechanisms. Amongst the mechanisms involved in the induction and perpetuation of cardiac arrhythmias such AF, dynamic high-frequency re-entrant and focal sources, in its different modalities, are thought to be the primary sources underlying the arrhythmia. However, little is known about the attractors and spatiotemporal dynamics of such fibrillatory primary sources, specifically dominant rotational or focal sources maintaining the arrhythmia. Therefore, a computational platform for understanding active, passive and structural determinants, and modulators of such dynamics was developed. This allowed stablishing a framework for understanding the complex multidomain dynamics of rotors with enphasis in their deterministic properties to develop mechanistic approaches for diagnostic aid and therapy. Understanding fibrillatory processes is key to develop physiologically and clinically relevant scores and tools for early diagnostic aid. Specifically, spectral and time-frequency properties of fibrillatory processes have shown to highlight major deterministic behaviour of intrinsic mechanisms underlying the arrhythmias and the impact of such arrhythmic events. Using prior knowledge, signal processing, machine learning techniques and data analytics, we aimed at developing a reliable mechanistic risk-score for comatose survivors of cardiac arrest due to VF. Cardiac optical mapping and electrophysiological mapping techniques have shown to be unvaluable resources to shape new hypotheses and develop novel mechanistic approaches and therapeutic strategies. This technology has allowed for many years testing new pharmacological or ablative therapeutic strategies, and developing multidomain methods to accurately track arrhymia dynamics identigying dominant sources and attractors. Even though, panoramic mapping is the primary method for simultaneously tracking electrophysiological parameters, its adoption by the multidisciplinary cardiovascular research community is limited mainly due to the cost of the technology. Taking advantage of recent technological advances, we focus on developing and validating low-cost optical mapping systems for panoramic imaging using clinically relevant models for basic research and bioengineering.Calvo Saiz, CJ. (2022). Novel Cardiac Mapping Approaches and Multimodal Techniques to Unravel Multidomain Dynamics of Complex Arrhythmias Towards a Framework for Translational Mechanistic-Based Therapeutic Strategies [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/182329TESI

Case-Based-Reasoning System for Feature Selection and Diagnosing Disease; Case Study: Asthma

Asthma is a chronic informatory disease of the respiratory canals in which it has not become obvious what is the reason for the reports argumentation on the ground of asthma prevalence. In the present research, the purpose would be to design a case-based-reasoning (CBR) model in order to assist a physician to diagnose the type of disease and also the needed therapy. At first for designing this system, the disease variables were discriminated and were at the patients' disposal as a questionnaire, and after gathering the relevant data (CBR) algorithm was rendered on the data which led to the asthma diagnosis. The system was tested on 325 asthmatic and non asthmatic adult cases and was accessed with eighty percent accuracy. The consequences were promising. With regard to the fact that the factors of the disease are different in various countries, This study was performed in order to determine risk factors for asthma in Iranian society and the results of research showed that the most important variables of asthma disease in Iran are symptoms heperresponsivity, frequency of cough, cough. Key words: data mining, case based reasoning, asthma, diagnosis

Artificial Intelligence in Medicine: A New Way to Diagnose and Treat Disease

Artificial intelligence (AI) has immense potential to transform medicine by improving diagnostic accuracy and enabling personalized treatments. This paper explores how AI systems analyze medical images, lab tests, genetic data, and patient histories to detect disease earlier and guide therapy selection. Though still an emerging field, impressive results demonstrate AI can surpass human clinicians on diagnostic tasks. For example, an AI system detected breast cancer from mammograms more accurately than expert radiologists. In ophthalmology, AI outperformed ophthalmologists in diagnosing diabetic retinopathy. By finding subtle patterns in complex datasets, AI promises to catch diseases like cancer in early, more treatable stages. Beyond diagnosis, AI can identify optimal treatments for individual patients based on their genetic makeup and lifestyle factors. Researchers are also using AI to design new medications. While AI offers many benefits, challenges remain regarding clinician displacement, legal liability, data privacy, and the "black box" nature of AI reasoning. More research is needed, but it is clear that AI will fundamentally alter medical practice. AI empowers clinicians to provide earlier, more precise diagnoses and tailored therapies for patients. Though it will not replace doctors, by automating routine tasks and uncovering hidden insights, AI can free physicians to focus on holistic care. The future of medicine lies in humans and smart machines working together

Mobile Solution for data mining and decision support: Weight monitoring and early prediction of cardiac arrest.

The daily accumulation of data through various means has led to the popularity of data mining in recent times. Through the use of the data mining techniques, data that are collected are used for problem-solving and other purposes. In data mining, patterns and trends of large datasets are studied using computer-based techniques. This thesis is using an Android mobile application as a data sampling tool for data mining purposes. Using this application, a predictive machine learning model, was developed to predict the probability of occurrence of cardiac of arrest in users of a mobile app over a ten- year span. The designed mobile application also functions as a support tool for weight management and fitness. The mobile application was connected to a real-time database and a machine learning tool using a Python program to perform prediction. The machine learning was based on Logistic Regression that is one of the predominant models used in the healthcare sector for predictions. The system used the user’s age, height, weight, activity level and diabetes status to predict the user’s chances of getting a Sudden Cardiac Arrest (SCA) over a ten-year period. A detailed account of the implementation processes and principles are discussed throughout this work.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format