ECG based Prediction Model for Cardiac-Related Diseases using Machine Learning Techniques

This dissertation presents research on the construction of predictive models for health conditions through the application of Artificial Intelligence methods. The work is thus focused on the prediction, in the short and long term, of Atrial Fibrillation conditions through the analysis of Electrocardiography exams, with the use of several techniques to reduce noise and interference, as well as their representation through spectrograms and their application in Artificial Intelligence models, specifically Deep Learning. The training and testing processes of the models made use of a publicly available database. In its two approaches, predictive algorithms were obtained with an accuracy of 96.73% for a short horizon prediction and 96.52% for long Atrial Fibrillation prediction horizon. The main objectives of this dissertation are thus the study of works already carried out in the area during the last decade, to present a new methodology of prediction of the presented condition, as well as to present and discuss its results, including suggestions for improvement for future development.Esta dissertação descreve a construção de modelos preditivos de condições de saúde através de aplicação de métodos de Inteligência Artificial. O trabalho é assim focado na predição, a curto e longo prazo, de condições de Fibrilhação Auricular através da análise de exames de Eletrocardiografia, com a utilização de diversas técnicas de redução de ruído e de interferência, bem como a sua representação através de espectrogramas e sua aplicação em modelos de Inteligência Artificial, concretamente de Aprendizagem Profunda (Deep Learning na língua inglesa). Os processos de treino e teste dos modelos obtidos recorreram a uma base de dados publicamente disponível. Nas suas duas abordagens, foram obtidos algoritmos preditivos com uma precisão de 96.73% para uma predição de curto horizonte e 96.52% para longo horizonte de predição de Fibrilhação Auricular. Os objetivos principais da presente dissertação são assim o estudo de trabalhos já realizados na área durante a última década, apresentar uma nova metodologia de predição da condição apresentada, bem como apresentar e discutir os seus resultados, incluindo sugestões de melhoria para futuro desenvolvimento

Electrocardiogram Monitoring Wearable Devices and Artificial-Intelligence-Enabled Diagnostic Capabilities: A Review

Worldwide, population aging and unhealthy lifestyles have increased the incidence of high-risk health conditions such as cardiovascular diseases, sleep apnea, and other conditions. Recently, to facilitate early identification and diagnosis, efforts have been made in the research and development of new wearable devices to make them smaller, more comfortable, more accurate, and increasingly compatible with artificial intelligence technologies. These efforts can pave the way to the longer and continuous health monitoring of different biosignals, including the real-time detection of diseases, thus providing more timely and accurate predictions of health events that can drastically improve the healthcare management of patients. Most recent reviews focus on a specific category of disease, the use of artificial intelligence in 12-lead electrocardiograms, or on wearable technology. However, we present recent advances in the use of electrocardiogram signals acquired with wearable devices or from publicly available databases and the analysis of such signals with artificial intelligence methods to detect and predict diseases. As expected, most of the available research focuses on heart diseases, sleep apnea, and other emerging areas, such as mental stress. From a methodological point of view, although traditional statistical methods and machine learning are still widely used, we observe an increasing use of more advanced deep learning methods, specifically architectures that can handle the complexity of biosignal data. These deep learning methods typically include convolutional and recurrent neural networks. Moreover, when proposing new artificial intelligence methods, we observe that the prevalent choice is to use publicly available databases rather than collecting new data

Cardiac Arrhythmia Monitoring and Severe Event Prediction System

Abnormalities of cardiac rhythms are correlated with significant morbidity. For example, atrial fibrillation, affecting at least 2.3 million people in the United States, is associated with an increased risk of both stroke and mortality; supra-ventricular tachycardia, detected in approximately 90,000 cases annually in the United States, ventricular arrhythmias cause 75% to 80% of the cases of sudden cardiac death; bradyarrhythmias may cause syncope, fatigue from chronotropic incompetence, or sudden death from asystole or ventricular tachycardia. Due to the time-sensitive nature of cardiac events, it is of utmost importance to ensure that medical intervention is provided in a timely manner, which could benefit greatly from a cardiac arrhythmia monitoring system that can detect and preferably also predict abnormal cardiac events. In recent years, with the development of medical monitoring devices, vast amounts of physiological signal data have been collected and become available for analysis. However, the extraction of the relevant information from physiological signals is hindered by the complexity within signal morphology, which leads to vague definitions and ambiguous guidelines, causing difficulties even for medical expert. To address the variability-related issues, most traditional methods depend heavily on pre-processing to identify specific morphology types and extract the related features. Despite many successes, one of the drawbacks of these methods is that they require signal data of high quality and tend to be less effective in the presence of noise. Although not an issue in almost noiseless situations, such pre-processing--based methods have become insufficient with the advent of portable arrhythmia monitoring devices in recent years capable of collecting physiological signals in real time, albeit with more noise. Therefore, to enable automated clinical decision, it is desirable to introduce new methods that require minimal pre-processing prior to analysis and are robust to noise. This thesis aims to develop a cardiac arrhythmia monitoring and prediction system applicable to portable arrhythmia monitoring devices. The analysis is based on a novel algorithm which does not rely on the detailed morphological information contained within each heartbeat, thus minimizing the impact of noise. Instead, the method works by analyzing the similarity and variability within strings of consecutive heartbeats, relying only on the broad morphology type of each heartbeat and utilizing the computer's ability to store and process a large number of heartbeats beyond humanly possible. The novel algorithm is based on deterministic probabilistic finite-state automata which have found great success in the field of natural language processing by studying the relationships among different words in a sentence rather than the detailed structure of the individual words. The proposed algorithm has been employed in experiments on both detection and prediction of various cardiac arrhythmia types and has achieved an AUC in the range of 0.70 to 0.95 for detection and prediction of different types of cardiac arrhythmias and cardiac events with data collected from publicly available databases, hospital bedside database and data collected from portable devices. Comparing with other well-established methods, the proposed algorithm has achieved equal or better classification results. In addition, the performance of the proposed algorithm is almost identical with or without any pre-processing on the data. The work in the thesis could be deployed as a cardiac arrhythmia monitoring and severe event prediction system which could alert patients and clinicians of an impending event, thereby enabling timely medical interventions.PHDBioinformaticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169873/1/zcli_1.pd

Enhancing the Diagnosis and Management of Obstructive Sleep Apnoea in Atrial Fibrillation Patients

Background: Atrial fibrillation (AF), is the most common sustained cardiac arrhythmia, and significantly increases the risk of stroke and cardiovascular mortality. It is strongly associated with obstructive sleep apnoea (OSA). Aims: 1. Examine the epidemiology of OSA in a hospital cohort with AF. 2. Compare the diagnostic accuracy of clinical screening tools for OSA in patients with AF. 3. Compare cardiac autonomic function in AF patients with and without OSA. 4. Conduct a pilot study of mandibular advancement splint (MAS) therapy for OSA in AF patients. Methods: 107 AF patients were recruited. The diagnostic accuracy of screening tools including a level 3 (portable) sleep study device as compared to polysomnography in AF patients was assessed. Cardiac autonomic function as a potential mechanistic link between OSA and AF was assessed using Heart Rate Variability (HRV). A pilot study of OSA treatment in AF patients using MAS therapy was conducted. Results: 62.6% of patients were newly diagnosed with OSA. Patients with moderate to severe OSA showed an increased BMI, neck circumference and Mallampati score, but were not significantly different in terms of daytime somnolence. Oxygen desaturation index (ODI) derived from a Level 3 portable sleep study device performed best for the diagnosis of moderate to severe and severe OSA, with excellent diagnostic accuracy (AUC 0.899, 95% CI 0.838 – 0.960 and AUC 0.925, 95% CI 0.859 – 0.991 respectively). We found a chronic increase in parasympathetic nervous activity in paroxysmal AF patients with OSA. MAS therapy showed high rates of acceptance, compliance and efficacy in AF patients. Conclusions: This thesis contributes to our understanding of the association between AF and OSA across a spectrum o

Algorithms for automated diagnosis of cardiovascular diseases based on ECG data: A comprehensive systematic review

The prevalence of cardiovascular diseases is increasing around the world. However, the technology is evolving and can be monitored with low-cost sensors anywhere at any time. This subject is being researched, and different methods can automatically identify these diseases, helping patients and healthcare professionals with the treatments. This paper presents a systematic review of disease identification, classification, and recognition with ECG sensors. The review was focused on studies published between 2017 and 2022 in different scientific databases, including PubMed Central, Springer, Elsevier, Multidisciplinary Digital Publishing Institute (MDPI), IEEE Xplore, and Frontiers. It results in the quantitative and qualitative analysis of 103 scientific papers. The study demonstrated that different datasets are available online with data related to various diseases. Several ML/DP-based models were identified in the research, where Convolutional Neural Network and Support Vector Machine were the most applied algorithms. This review can allow us to identify the techniques that can be used in a system that promotes the patient’s autonomy.N/