Applications of Signal Analysis to Atrial Fibrillation

This work was supported by projects TEC2010–20633 from the Spanish Ministry of Science and Innovation and PPII11–0194–8121 from Junta de Comunidades de Castilla-La ManchaRieta Ibañez, JJ.; Alcaraz Martínez, R. (2013). Applications of Signal Analysis to Atrial Fibrillation. En Atrial Fibrillation - Mechanisms and Treatment. InTech. 155-180. https://doi.org/10.5772/5340915518

Löwner-Based Tensor Decomposition for Blind Source Separation in Atrial Fibrillation ECGs

International audienceThe estimation of the atrial activity (AA) signal in electrocardiogram (ECG) recordings is an important step in the noninvasive analysis of atrial fibrillation (AF), the most common sustained cardiac arrhythmia in clinical practice. Recently, this blind source separation (BSS) problem has been formulated as a tensor factorization, based on the block term decomposition (BTD) of a data tensor built from Hankel matrices of the observed ECG. However, this tensor factorization technique was precisely assessed only in segments with long R-R intervals and with the AA well defined in the TQ segment, where ventricular activity (VA) is absent. Due to the chaotic nature of AA in AF, segments with disorganized or weak AA and with short R-R intervals are quite more common in persistent AF, posing some difficulties to the BSS methods to extract the AA signal, regarding performance and computational cost. In this paper, the BTD built from Löwner matrices is proposed as a method to separate VA from AA in these challenging scenarios. Experimental results obtained in a population of 10 patients show that the Löwner-based BTD outperforms the Hankel-based BTD and two well-known matrix-based methods in terms of atrial signal estimation quality and computational cost

Detection of Atrial Fibrillation using Autoregressive modeling

A‎atrial fibrillation (AF) is the arrhythmia that commonly causes death in the adults. We measured AR coefficients using Burg’s method for each 15 second segment of ECG. These features are classified using the different statistical classifiers: kernel SVM and KNN classifier. The performance of the algorithm was evaluated on signals from MIT Physionet database.. The effect of AR model order and data length was tested on the classification results. This method shows better results can be used for practical use in the clinics.

Reference database and performance evaluation of methods for extraction of atrial fibrillatory waves in the ECG

[EN] Objective: This study proposes a reference database, composed of a large number of simulated ECG signals in atrial fibrillation (AF), for investigating the performance of methods for extraction of atrial fibrillatory waves (f -waves). Approach: The simulated signals are produced using a recently published and validated model of 12-lead ECGs in AF. The database is composed of eight signal sets together accounting for a wide range of characteristics known to represent major challenges in f -wave extraction, including high heart rates, high morphological QRST variability, and the presence of ventricular premature beats. Each set contains 30 5 min signals with different f -wave amplitudes. The database is used for the purpose of investigating the statistical association between different indices, designed for use with either real or simulated signals. Main results: Using the database, available at the PhysioNet repository of physiological signals, the performance indices unnormalized ventricular residue (uVR), designed for real signals, and the root mean square error, designed for simulated signals, were found to exhibit the strongest association, leading to the recommendation that uVR should be used when characterizing performance in real signals. Significance: The proposed database facilitates comparison of the performance of different f -wave extraction methods and makes it possible to express performance in terms of the error between simulated and extracted f -wave signals.This work was supported by project DPI2017-83952-C3 of the Spanish Ministry of Economy, Industry and Competitiveness, project SBPLY/17/180501/000411 of the Junta de Comunidades de Castilla-La Mancha, Grant 'Jose Castillejo' (CAS17/00436) from the Spanish Ministry of Education, Culture and Sport, Grant No. BEST/2017/028 from the Education, Research, Culture and Sports Department of Generalitat Valenciana, European Regional Development Fund, and Grant No. 03382/2016 from the Swedish Research Council.Alcaraz, R.; Sornmo, L.; Rieta, JJ. (2019). Reference database and performance evaluation of methods for extraction of atrial fibrillatory waves in the ECG. Physiological Measurement. 40(7):1-11. https://doi.org/10.1088/1361-6579/ab2b17S111407Chugh, S. 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L., Lecannelier, E. A., Pino, E. J., & Rojas, A. J. (2013). Atrial activity selection for atrial fibrillation ECG recordings. Computers in Biology and Medicine, 43(10), 1628-1636. doi:10.1016/j.compbiomed.2013.08.002Fauchier, L., Villejoubert, O., Clementy, N., Bernard, A., Pierre, B., Angoulvant, D., … Lip, G. Y. H. (2016). Causes of Death and Influencing Factors in Patients with Atrial Fibrillation. The American Journal of Medicine, 129(12), 1278-1287. doi:10.1016/j.amjmed.2016.06.045Fujiki, A., Sakabe, M., Nishida, K., Mizumaki, K., & Inoue, H. (2003). Role of Fibrillation Cycle Length in Spontaneous and Drug-Induced Termination of Human Atrial Fibrillation. Circulation Journal, 67(5), 391-395. doi:10.1253/circj.67.391Goldberger, A. L., Amaral, L. A. N., Glass, L., Hausdorff, J. M., Ivanov, P. C., Mark, R. G., … Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet. Circulation, 101(23). doi:10.1161/01.cir.101.23.e215Roonizi, E. K., & Sassi, R. (2017). 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ECG modeling for simulation of arrhythmias in time-varying conditions

The present paper proposes an ECG simulator that advances modeling of arrhythmias and noise by introducing time-varying signal characteristics. The simulator is built around a discrete-time Markov chain model for simulating atrial and ventricular arrhythmias of particular relevance when analyzing atrial fibrillation (AF). Each state is associated with statistical information on episode duration and heartbeat characteristics. Statistical, time-varying modeling of muscle noise, motion artifacts, and the influence of respiration is introduced to increase the complexity of simulated ECGs, making the simulator well suited for data augmentation in machine learning. Modeling of how the PQ and QT intervals depend on heart rate is also introduced. The realism of simulated ECGs is assessed by three experienced doctors, showing that simulated ECGs are difficult to distinguish from real ECGs. Simulator usefulness is illustrated in terms of AF detection performance when either simulated or real ECGs are used to train a neural network for signal quality control. The results show that both types of training lead to similar performance

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

Classification of De novo post-operative and persistent atrial fibrillation using multi-channel ECG recordings

Atrial fibrillation (AF) is the most sustained arrhythmia in the heart and also the most common complication developed after cardiac surgery. Due to its progressive nature, timely detection of AF is important. Currently, physicians use a surface electrocardiogram (ECG) for AF diagnosis. However, when the patient develops AF, its various development stages are not distinguishable for cardiologists based on visual inspection of the surface ECG signals. Therefore, severity detection of AF could start from differentiating between short-lasting AF and long-lasting AF. Here, de novo post-operative AF (POAF) is a good model for short-lasting AF while long-lasting AF can be represented by persistent AF. Therefore, we address in this paper a binary severity detection of AF for two specific types of AF. We focus on the differentiation of these two types as de novo POAF is the first time that a patient develops AF. Hence, comparing its development to a more severe stage of AF (e.g., persistent AF) could be beneficial in unveiling the electrical changes in the atrium. To the best of our knowledge, this is the first paper that aims to differentiate these different AF stages. We propose a method that consists of three sets of discriminative features based on fundamentally different aspects of the multi-channel ECG data, namely based on the analysis of RR intervals, a greyscale image representation of the vectorcardiogram, and the frequency domain representation of the ECG. Due to the nature of AF, these features are able to capture both morphological and rhythmic changes in the ECGs. Our classification system consists of a random forest classifier, after a feature selection stage using the ReliefF method. The detection efficiency is tested on 151 patients using 5-fold cross-validation. We achieved 89.07% accuracy in the classification of de novo POAF and persistent AF. The results show that the features are discriminative to reveal the severity of AF. Moreover, inspection of the most important features sheds light on the different characteristics of de novo post-operative and persistent AF.</p

ADAPTIVE MODELS-BASED CARDIAC SIGNALS ANALYSIS AND FEATURE EXTRACTION

Signal modeling and feature extraction are among the most crucial and important steps for stochastic signal processing. In this thesis, a general framework that employs adaptive model-based recursive Bayesian state estimation for signal processing and feature extraction is described. As a case study, the proposed framework is studied for the problem of cardiac signal analysis. The main objective is to improve the signal processing aspects of cardiac signals by developing new techniques based on adaptive modelling of electrocardiogram (ECG) wave-forms. Specially several novel and improved approaches to model-based ECG decomposition, waveform characterization and feature extraction are proposed and studied in detail. In the concept of ECG decomposition and wave-forms characterization, the main idea is to extend and improve the signal dynamical models (i.e. reducing the non-linearity of the state model with respect to previous solutions) while combining with Kalman smoother to increase the accuracy of the model in order to split the ECG signal into its waveform components, as it is proved that Kalman filter/smoother is an optimal estimator in minimum mean square error (MMSE) for linear dynamical systems. The framework is used for many real applications, such as: ECG components extraction, ST segment analysis (estimation of a possible marker of ventricular repolarization known as T/QRS ratio) and T-wave Alternans (TWA) detection, and its extension to many other applications is straightforward. Based on the proposed framework, a novel model to characterization of Atrial Fibrillation (AF) is presented which is more effective when compared with other methods proposed with the same aims. In this model, ventricular activity (VA) is represented by a sum of Gaussian kernels, while a sinusoidal model is employed for atrial activity (AA). This new model is able to track AA, VA and fibrillatory frequency simultaneously against other methods which try to analyze the atrial fibrillatory waves (f-waves) after VA cancellation. Furthermore we study a new ECG processing method for assessing the spatial dispersion of ventricular repolarization (SHVR) using V-index and a novel algorithm to estimate the index is presented, leading to more accurate estimates. The proposed algorithm was used to study the diagnostic and prognostic value of the V-index in patients with symptoms suggestive of Acute Myocardial Infraction (AMI)