A Review of Atrial Fibrillation Detection Methods as a Service

Atrial Fibrillation (AF) is a common heart arrhythmia that often goes undetected, and even if it is detected, managing the condition may be challenging. In this paper, we review how the RR interval and Electrocardiogram (ECG) signals, incorporated into a monitoring system, can be useful to track AF events. Were such an automated system to be implemented, it could be used to help manage AF and thereby reduce patient morbidity and mortality. The main impetus behind the idea of developing a service is that a greater data volume analyzed can lead to better patient outcomes. Based on the literature review, which we present herein, we introduce the methods that can be used to detect AF efficiently and automatically via the RR interval and ECG signals. A cardiovascular disease monitoring service that incorporates one or multiple of these detection methods could extend event observation to all times, and could therefore become useful to establish any AF occurrence. The development of an automated and efficient method that monitors AF in real time would likely become a key component for meeting public health goals regarding the reduction of fatalities caused by the disease. Yet, at present, significant technological and regulatory obstacles remain, which prevent the development of any proposed system. Establishment of the scientific foundation for monitoring is important to provide effective service to patients and healthcare professionals

Detection of atrial fibrillation episodes in long-term heart rhythm signals using a support vector machine

Atrial fibrillation (AF) is a serious heart arrhythmia leading to a significant increase of the risk for occurrence of ischemic stroke. Clinically, the AF episode is recognized in an electrocardiogram. However, detection of asymptomatic AF, which requires a long-term monitoring, is more efficient when based on irregularity of beat-to-beat intervals estimated by the heart rate (HR) features. Automated classification of heartbeats into AF and non-AF by means of the Lagrangian Support Vector Machine has been proposed. The classifier input vector consisted of sixteen features, including four coefficients very sensitive to beat-to-beat heart changes, taken from the fetal heart rate analysis in perinatal medicine. Effectiveness of the proposed classifier has been verified on the MIT-BIH Atrial Fibrillation Database. Designing of the LSVM classifier using very large number of feature vectors requires extreme computational efforts. Therefore, an original approach has been proposed to determine a training set of the smallest possible size that still would guarantee a high quality of AF detection. It enables to obtain satisfactory results using only 1.39% of all heartbeats as the training data. Post-processing stage based on aggregation of classified heartbeats into AF episodes has been applied to provide more reliable information on patient risk. Results obtained during the testing phase showed the sensitivity of 98.94%, positive predictive value of 98.39%, and classification accuracy of 98.86%.Web of Science203art. no. 76

Novel Approaches to ECG-Based Modeling and Characterization of Atrial Fibrillation

This thesis deals with signal processing algorithms for analysis of the electrocardiogram (ECG) during atrial fibrillation (AF). Such analysis can be used for diagnosing patients, and for monitoring and predicting their response to various treatment. The thesis comprises an introduction and five papers describing methods for ECG-based modeling and characterization of AF. Paper I--IV deal with methods for characterization of the atrial activity, whereas Paper V deals with modeling of the ventricular response, both problems with the assumption that AF is present. In Paper I, a number of measures characterizing the atrial activity in the ECG, obtained using time-frequency analysis as well as nonlinear methods, are evaluated for their ability to predict spontaneous termination of AF. The AF frequency, i.e, the repetition rate of the atrial fibrillatory waves of the ECG, proved to be a significant factor for discrimination between terminating and non-terminating AF. Noise is a common problem in ECG signals, particularly in long-term ambulatory recordings. Hence, robust algorithms for analysis and characterization are required. In Paper II, a robust method for tracking the AF frequency in noisy signals is presented. The method is based on a hidden Markov model (HMM), which takes the harmonic pattern of the atrial activity into account. Using the HMM-based method, the average RMS error of the frequency estimates at high noise levels was significantly lower compared to existing methods. In Paper III, the HMM-based method is employed for analysis of 24-h ambulatory ECG signals in order to explore circadian variation in AF frequency. Circadian variations reflect autonomic modulation; attenuation or absence of such variations may help to diagnose patients. Methods based on curve fitting, autocorrelation, and joint variation, respectively, are employed to quantify circadian variations, showing that it is present in most patients with long-standing persistent AF, although the short-term variation is considerable. In Paper IV, 24-h ambulatory ECG recordings with paroxysmal and persistent AF are analyzed using an entropy-based method for characterization of the atrial activity. Short segments are classified based on these measures, showing that it is feasible to distinguish between patient with paroxysmal and persistent AF from 10-s ECGs; the average classification rate was above 95%. The ventricular response during AF is mainly determined by the AV nodal blocking of atrial impulses. In Paper V, a new model-based approach for analysis of the ventricular response during AF is proposed. The model integrates physiological properties of the AV node and the atrial fibrillatory rate; the model parameters can be estimated from ECG signals. Results show that ventricular response is sufficiently represented by the estimated model in a majority of the recordings; in 85.7% of the analyzed 30-min segments the model fit was considered accurate, and that changes of AV nodal properties caused by autonomic modulation could be tracked through the estimated model parameters. In summary, the work within this thesis contributes with new methods for non-invasive analysis of AF, which can be used to tailor and evaluate different strategies for AF treatment

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

Early Detection and Continuous Monitoring of Atrial Fibrillation from ECG Signals with a Novel Beat-Wise Severity Ranking Approach

Irregularities in heartbeats and cardiac functioning outside of clinical settings are often not available to the clinicians, and thus ignored. But monitoring these with high-risk population might assist in early detection and continuous monitoring of Atrial Fibrillation(AF). Wearable devices like smart watches and wristbands, which can collect Electrocardigraph(ECG) signals, can monitor and warn users of unusual signs in a timely manner. Thus, there is a need to develop a real-time monitoring system for AF from ECG. We propose an algorithm for a simple beat-by-beat ECG signal multilevel classifier for AF detection and a quantitative severity scale (between 0 to 1) for user feedback. For this study, we used ECG recordings from MIT BIH Atrial Fibrillation, MIT BIH Long-term Atrial Fibrillation Database. All ECG signals are preprocessed for reducing noise using filter. Preprocessed signal is analyzed for extracting 39 features including 20 of amplitude type and 19 of interval type. The feature space for all ECG recordings is considered for Classification. Training and testing data include all classes of data i.e., beats to identify various episodes for severity. Feature space from the test data is fed to the classifier which determines the class label based on trained model. A class label is determined based on number of occurences of AF and other arrhythmia episodes such as AB(Atrial Bigeminy), SBR(Sinus Bradycardia), SVTA(Supra Ventricular Tacchyarrhythmia). Accuracy of 96.7764% is attained with Random Forest algorithm, Furthermore, precision and recall are determined based on correct and incorrect classifications for each class. Precision and recall on average of Random Forest Classifier are obtained as 0.968 and 0.968 respectievely. This work provides a novel approach to enhance existing method of AF detection by identifying heartbeat class and calculates a quantitative severity metric that might help in early detection and continuous monitoring of AF

Prediction of postoperative atrial fibrillation using the electrocardiogram: A proof of concept

Hospital patients recovering from major cardiac surgery are at high risk of postoperative atrial fibrillation (POAF), an arrhythmia which can be life-threatening. With the development of a tool to predict POAF early enough, the development of the arrhythmia could be potentially prevented using prophylactic treatments, thus reducing risks and hospital costs. To date, no reliable method suitable for autonomous clinical integration has been proposed yet. This thesis presents a study on the prediction of POAF using the electrocardiogram. A novel P-wave quality assessment tool to automatically identify high-quality P-waves was designed, and its clinical utility was assessed. Prediction of paroxysmal atrial fibrillation (AF) was performed by implementing and improving a selection of previously proposed methods. This allowed to perform a systematic comparison of those methods, and to test if their combination improved prediction of AF. Finally, prediction of POAF was tested in a clinically relevant scenario. This included studying the 48 hours preceding POAF, and automatically excluding noise-corrupted P-waves using the quality assessment tool. The P-wave quality assessment tool identified high-quality P-waves with high sensitivity (0.93) and good specificity (0.84). In addition, this tool improved the ability to predict AF, since it improved the precision of P-wave measurements. The best predictors of AF and POAF were measurements of the variability in P-wave time- and morphological features. Paroxysmal AF could be predicted with high specificity (0.93) and good sensitivity (0.82) when several predictors were combined. Furthermore, POAF could be predicted 48 hours before its onset with good sensitivity (0.74) and specificity (0.70). This leaves time for prophylactic treatments to be administered and possibly prevent POAF. Despite being promising, further work is required for these techniques to be useful in the clinical setting

Characterization and processing of atrial fibrillation episodes by convolutive blind source separation algorithms and nonlinear analysis of spectral features

Las arritmias supraventriculares, en particular la fibrilación auricular (FA), son las enfermedades cardíacas más comúnmente encontradas en la práctica clínica rutinaria. La prevalencia de la FA es inferior al 1\% en la población menor de 60 años, pero aumenta de manera significativa a partir de los 70 años, acercándose al 10\% en los mayores de 80. El padecimiento de un episodio de FA sostenida, además de estar ligado a una mayor tasa de mortalidad, aumenta la probabilidad de sufrir tromboembolismo, infarto de miocardio y accidentes cerebrovasculares. Por otro lado, los episodios de FA paroxística, aquella que termina de manera espontánea, son los precursores de la FA sostenida, lo que suscita un alto interés entre la comunidad científica por conocer los mecanismos responsables de perpetuar o conducir a la terminación espontánea de los episodios de FA. El análisis del ECG de superficie es la técnica no invasiva más extendida en la diagnosis médica de las patologías cardíacas. Para utilizar el ECG como herramienta de estudio de la FA, se necesita separar la actividad auricular (AA) de las demás señales cardioeléctricas. En este sentido, las técnicas de Separación Ciega de Fuentes (BSS) son capaces de realizar un análisis estadístico multiderivación con el objetivo de recuperar un conjunto de fuentes cardioeléctricas independientes, entre las cuales se encuentra la AA. A la hora de abordar un problema de BSS, se hace necesario considerar un modelo de mezcla de las fuentes lo más ajustado posible a la realidad para poder desarrollar algoritmos matemáticos que lo resuelvan. Un modelo viable es aquel que supone mezclas lineales. Dentro del modelo de mezclas lineales se puede además hacer la restricción de que estas sean instantáneas. Este modelo de mezcla lineal instantánea es el utilizado en el Análisis de Componentes Independientes (ICA).Vayá Salort, C. (2010). Characterization and processing of atrial fibrillation episodes by convolutive blind source separation algorithms and nonlinear analysis of spectral features [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/8416Palanci

Quality Control in ECG-based Atrial Fibrillation Screening

This thesis comprises an introductory chapter and four papers related to quality control in ECG-based atrial fibrillation (AF) screening. Atrial fibrillation is a cardiac arrhythmia characterized by an irregular rhythm and constitutes a major risk factor for stroke. Anticoagulation therapy significantly reduces this risk, and therefore, AF screening is motivated. Atrial fibrillation screening is often done using ECGs recorded outside the clinical environment. However, the higher susceptibility of such ECGs to noise and artifacts makes the identification of patients with AF challenging. The present thesis addresses these challenges at different levels in the data analysis chain. Paper I presents a convolutional neural network (CNN)-based approach to identify transient noise and artifacts in the detected beat sequence before AF detection. The results show that by inserting a CNN, prior to the AF detector, the number of false AF detections is reduced by 22.5% without any loss in the sensitivity, suggesting that the number of recordings requiring expert review can be significantly reduced. Paper II investigates the signal quality of a novel wet electrode technology, and how the improved signal quality translates to improved beat detection and AF detection performance. The novel electrode technology is designed for reduction of motion artifacts typically present in Holter ECG recordings. The novel electrode technology shows a better signal quality and detection performance when compared to a commercially available counterpart, especially when the subject becomes more active. Thus, it has the potential to reduce the review burden and costs associated with ambulatory monitoring.Paper III introduces a detector for short-episode supraventricular tachycardia (sSVT) in AF screening recordings, which has been shown to be associated with an increased risk for future AF. Therefore, the identification of subjects with suchepisodes may increase the usefulness of AF screening. The proposed detector is based on the assumption that the beats in an sSVT episode display similar morphology, and that episodes including detections of deviating morphology should be excluded. The results show that the number of false sSVT detections can be significantly reduced (by a factor of 6) using the proposed detector.Paper IV introduces a novel ECG simulation tool, which is capable of producing ECGs with various arrhythmia patterns and with several different types of noise and artifacts. Specifically, the ECG simulator includes models to generate noise observed in ambulatory recordings, and when recording using handheld recording devices. The usefulness of the simulator is illustrated in terms of AF detection performance when the CNN training in Paper I is performed using simulated data. The results show a very similar performance when training with simulated data compared to when training with real data. Thus, the proposed simulator is a valuable tool in the development and training of automated ECG processing algorithms. Together, the four parts, in different ways, contribute to improved algorithmic efficiency in AF screening

A Deep Learning Classifier for Detecting Atrial Fibrillation in Hospital Settings Applicable to Various Sensing Modalities

Cardiac signals provide variety of information related to the patient\u27s health. One of the most important is for medical experts to diagnose the functionality of a patient’s heart. This information helps the medical experts monitor heart disease such as atrial fibrillation and heart failure. Atrial fibrillation (AF) is one of the most major diseases that are threatening patients’ health. Medical experts measure cardiac signals usng the Electrocardiogram (ECG or EKG), the Photoplethysmogram (PPG), and more recently the Videoplethysmogram (VPG). Then they can use these measurements to analyze the heart functionality to detect heart diseases. In this study, these three major cardiac signals were used with different classification methodologies such as Basic Thresholding Classifiers (BTC), Machine Learning (SVM) classifiers, and deep learning classifiers based on Convolutional Neural Networks (CNN) to detect AF. To support the work, cardiac signals were acquired from forty-six AF subjects scheduled for cardioversion who were enrolled in a clinical study that was approved by the Internal Review Committees to protect human subjects at the University of Rochester Medical Center (URMC, Rochester, NY), and the Rochester Institute of Technology (RIT, Rochester, NY). The study included synchronized measurements of 5 minutes and 30 seconds of ECG, PPG, VPG 180Hz (High-quality camera), VPG 30 Hz (low quality webcam), taken before and after cardioversion of AF subjects receiving treatment at the AF Clinic of URMC. These data are subjected to BTC, SVM, and CNN classifiers to detect AF and compare the result for each classifier depending on the signal type. We propose a deep learning approach that is applicable to different kinds of cardiac signals to detect AF in a similar manner. By building this technique for different sensors we aim to provide a framework to implement a technique that can be used for most devices, such as, phones, tablets, PCs, ECG devices, and wearable PPG sensors. This conversion of the different sensing platforms provides a single AF detection classifier that can support a complete monitoring cycle that is referring to screen the patient whether at a hospital or home. By using that, the risk factor of heart attack, stroke, or other kind of heart complications can be reduced to a low level to prevent major dangers, since increasing monitoring AF patients helps to predict the disease at an early stage as well as track its progress. We show that the proposed approach provides around 99% accuracy for each type of classifier on the test dataset, thereby helping generalize AF detection by simplifying implementation using a sensor-agnostic deep learning model