Frequency Analysis of Atrial Fibrillation From the Surface Electrocardiogram

Atrial fibrillation (AF) is the most common arrhythmia encountered in clinical practice. Neither the natural history of AF nor its response to therapy are sufficiently predictable by clinical and echocardiographic parameters. Atrial fibrillatory frequency (or rate) can reliably be assessed from the surface electrocardiogram (ECG) using digital signal processing (filtering, subtraction of averaged QRST complexes, and power spectral analysis) and shows large inter-individual variability. This measurement correlates well with intraatrial cycle length, a parameter which appears to have primary importance in AF domestication and response to therapy. AF with a low fibrillatory rate is more likely to terminate spontaneously, and responds better to antiarrhythmic drugs or cardioversion while high rate AF is more often persistent and refractory to therapy. In conclusion, frequency analysis of AF seems to be useful for non-invasive assessment of electrical remodeling in AF and may subsequently be helpful for guiding AF therapy

Influence of autonomic nervous system in the inducibility of atrial fibrillation.

Cílem této práce je zjištění změn předcházejícím fibrilaci síní. Pozorována je rovnováha mezi sympatikem a parasympatikem. Do experimentu výzkumného ústavu Cleavlendské kliniky bylo zapojeno šest psů různých ras. Signály EKG byly získány Holterovským 24hodinovým monitorováním. Pomocí 40 vysokofrekvenčních impulsů (TI) byla každých 30 minut vyvolávána AF. Z 24hodinového signálu byly extrahovány kratší epizody. Každá z těchto epizod obsahovala 10 minut předcházejících TI a 3 minuty následující po TI. Desetiminutové epizody byly zpracovány automaticky, byly detekovány QRS komplexy a RR intervaly a vypočteny HRV parametry. Přítomnost a délka trvání AF byly zjištěny manuálně z tříminutových intervalů následujících po TI. Byla-li vyvolána AF o délce trvání kratší než 30 sekund došlo ve srovnání s epizodami bez výskytu AF k významným změnám tří HRV parametrů. HF parametr poklesl pro epizody s výskytem AF. LF parametr byl naopak vyšší v epizodách s AF. Pro AF delší než 30 sekund nebyly významné změny pozorovány. Změny v epizodách s krátkou AF mohly být způsobeny změnami vlivu sympatiku a parasympatiku. Ke vzniku dlouhých AF je pravděpodobně zapotřebí i jiného vlivu, který nemusí nutně souviset s nervovým systémem. K dalším analýzám je zapotřebí většího množství signálů.The aim of this study is to investigate changes in sympatho-vagal balance before the initiation of AF. Six mongrel dogs from the Cleveland Clinic foundation were included in this study. ECG was recorded for 24 hours using telemetric Holter monitoring. AF was periodically induced every 30 min. by applying brief bursts of 40 high-frequency atrial train impulses (TI). From the 24 hours signals' traces shorter data episodes were extracted. Each episode consisted of 10 minutes preceding the atrial burst, and 3 minutes following the (TI). The 10 minutes episodes were processed automatically to determine the QRS complexes and RR intervals, and to calculate the HRV parameters. The presence and the duration of AF were determined by manual examination in each of the 3 minutes intervals following the delivery of TI. When the AF was generated, but episodes of AF were shorter than 30 seconds, three HRV parameters were significantly different than when AF was not generated. The HF component was lower in episodes that generated AF. The LF component was higher in episodes that generated AF. No significant differences were found when episodes of AF were longer than 30 seconds. Short episodes of AF could be generated when a certain disorder between sympathetic and parasympathetic tone is present. However in order to be able to generate longer AF episodes it is necessary another component not necessary related to the nervous system. Further analysis with a higher number of dogs should be needed.

Frequency Analysis of Atrial Fibrillation From the Surface Electrocardiogram

Atrial fibrillation (AF) is the most common arrhythmia encountered in clinical practice. Neither the natural history of AF nor its response to therapy are sufficiently predictable by clinical and echocardiographic parameters. Atrial fibrillatory frequency (or rate) can reliably be assessed from the surface electrocardiogram (ECG) using digital signal processing (filtering, subtraction of averaged QRST complexes, and power spectral analysis) and shows large inter-individual variability. This measurement correlates well with intraatrial cycle length, a parameter which appears to have primary importance in AF domestication and response to therapy. AF with a low fibrillatory rate is more likely to terminate spontaneously, and responds better to antiarrhythmic drugs or cardioversion while high rate AF is more often persistent and refractory to therapy. In conclusion, frequency analysis of AF seems to be useful for non-invasive assessment of electrical remodeling in AF and may subsequently be helpful for guiding AF therapy

Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 171

This bibliography lists 186 reports, articles, and other documents introduced into the NASA scientific and technical information system in August 1977

Advances in Digital Processing of Low-Amplitude Components of Electrocardiosignals

This manual has been published within the framework of the BME-ENA project under the responsibility of National Technical University of Ukraine. The BME-ENA “Biomedical Engineering Education Tempus Initiative in Eastern Neighbouring Area”, Project Number: 543904-TEMPUS-1-2013-1-GR-TEMPUS-JPCR is a Joint Project within the TEMPUS IV program. This project has been funded with support from the European Commission.Навчальний посібник присвячено розробці методів та засобів для неінвазивного виявлення та дослідження тонких проявів електричної активності серця. Особлива увага приділяється вдосконаленню інформаційного та алгоритмічного забезпечення систем електрокардіографії високого розрізнення для ранньої діагностики електричної нестабільності міокарда, а також для оцінки функціонального стану плоду під час вагітності. Теоретичні основи супроводжуються прикладами реалізації алгоритмів за допомогою системи MATLAB. Навчальний посібник призначений для студентів, аспірантів, а також фахівців у галузі біомедичної електроніки та медичних працівників.The teaching book is devoted to development and research of methods and tools for non-invasive detection of subtle manifistations of heart electrical activity. Particular attention is paid to the improvement of information and algorithmic support of high resolution electrocardiography for early diagnosis of myocardial electrical instability, as well as for the evaluation of the functional state of the fetus during pregnancy examination. The theoretical basis accompanied by the examples of implementation of the discussed algorithms with the help of MATLAB. The teaching book is intended for students, graduate students, as well as specialists in the field of biomedical electronics and medical professionals

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/

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

Nonlinear physics of electrical wave propagation in the heart: a review

The beating of the heart is a synchronized contraction of muscle cells (myocytes) that are triggered by a periodic sequence of electrical waves (action potentials) originating in the sino-atrial node and propagating over the atria and the ventricles. Cardiac arrhythmias like atrial and ventricular fibrillation (AF,VF) or ventricular tachycardia (VT) are caused by disruptions and instabilities of these electrical excitations, that lead to the emergence of rotating waves (VT) and turbulent wave patterns (AF,VF). Numerous simulation and experimental studies during the last 20 years have addressed these topics. In this review we focus on the nonlinear dynamics of wave propagation in the heart with an emphasis on the theory of pulses, spirals and scroll waves and their instabilities in excitable media and their application to cardiac modeling. After an introduction into electrophysiological models for action potential propagation, the modeling and analysis of spatiotemporal alternans, spiral and scroll meandering, spiral breakup and scroll wave instabilities like negative line tension and sproing are reviewed in depth and discussed with emphasis on their impact in cardiac arrhythmias.Peer ReviewedPreprin