Extraction of the atrial activity from the ECG based on independent component analysis with prior knowledge of the source kurtosis signs

In this work it will be shown that a contrast for independent component analysis based on prior knowledge of the source kurtosis signs (ica-sks) is able to extract atrial activity from the electrocardiogram when a constrained updating is introduced. A spectral concentration measure is used, only allowing signal pair updates when spectral concentration augments. This strategy proves to be valid for independent source extraction with priors on the spectral concentration. Moreover, the method is computationally attractive with a very low complexity compared to the recently proposed methods based on spatiotemporal extraction of the atrial fibrillation signal

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

An Atrial Activity Based Algorithm for the Single-Beat Rate-Independent Detection of Atrial Fibrillation

Atrial fibrillation (AF) is the most common cardiac arrhythmia and is the cause of 15% of all ischemic strokes in the United States. In many cases, AF is episodic and/or asymptomatic and as a result, there is a strong need for algorithms capable of quickly and reliably detecting AF, even in cases where the heart rate is controlled through medication or with a pacemaker. Current RR interval (RRI)-based algorithms do not directly target atrial activity, cannot detect AF when the heart rate is controlled, and analyze relatively long intervals of the electrocardiography (ECG) to make an AF determination. This work proposes an algorithm for patient-specific, single-beat, rate-independent AF identification based on atrial activity (AA) analysis. The proposed algorithm develops a statistical model to describe the distribution of features extracted from AA during normal sinus rhythm (NSR). First, ECG segments preceding QRS complexes are identified potential P waves. A total of nine features - three higher order statistics (HOS) features and six features obtained through downsampling - are extracted from the P wave segment under consideration. The Expectation-Maximization algorithm is applied to a training set to create a multivariate Gaussian Mixture Model (GMM) of the feature space. This model is used to identify P wave absence (PWA) and, in turn, AF. An optional post-processing stage which takes a majority vote of successive outputs is applied to improve classifier performance. To evaluate the performance of the classifier, the algorithm was tested on 20 records in the MIT-BIH Atrial Fibrillation Database. Single-beat classification showed a sensitivity(Se) of 91.98%, a specificity(Sp) of 86.18%, a positive predictive value(PPV) of 70.70% and an error rate(Err) of 13.02%. Classification combining seven beats showed a Se of 99.28%, a Sp of 90.21%, a PPV of 80.42% and an Err of 7.12%. The presented algorithm has a classification performance comparable to current RRI-based algorithms yet is rate-independent and capable of making an AF determination in a single beat

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

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

Relationship between body surface potential maps and atrial electrograms in patients with atrial fibrillation

PhD ThesisAtrial fibrillation (AF) is the most common cardiac arrhythmia. It is distinguished by fibrillating or trembling of the atrial muscle instead of normal contraction. Patients in AF have a much higher risk of stroke. AF is often driven by the left atrium (LA) and the diagnosis of AF is normally made from lead V1 in a 12-lead electrocardiogram (ECG). However, lead V1 is dominated by right atrial activity due to its proximal location to the right atrium (RA). Consequently it is not well understood how electrical activity from the LA contributes to the ECG. Studies of the AF mechanisms from the LA are typically based on invasive recording techniques. From a clinical point of view it is highly desirable to have an alternative, non-invasive characterisation of AF. The aim of this study was to investigate how the LA electrical activity was expressed on the body surface, and if it could be observed preferentially in different sites on the body surface. For this purpose, electrical activity of the heart from 20 patients in AF were recorded simultaneously using 64-lead body surface potential mapping (BSPM) and bipolar 10-electrode catheters located in the LA and coronary sinus (CS). Established AF characteristics such as amplitude, dominant frequency (DF) and spectral concentration (SC) were estimated and analysed. Furthermore, two novel AF characteristics (intracardiac DF power distribution, and body surface spectral peak type) were proposed to investigate the relationship between the BSPM and electrogram (EGM) recordings. The results showed that although in individual patients there were body surface sites that preferentially represented the AF characteristics estimated from the LA, those sites were not consistent across all patients. It was found that the left atrial activity could be detected in all body surface sites such that all sites had a dominant or non-dominant spectral peak corresponding to EGM DF. However, overall the results suggested that body surface site 22 (close to lead V1) was more closely representative of the CS activity, and site 49 (close to the posterior lower central right) was more closely representative of the left atrial activity. There was evidence of more accurate estimation of AF characteristics using additional electrodes to lead V1

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

Recurring patterns of atrial fibrillation in surface ECG predict restoration of sinus rhythm by catheter ablation

Background Non-invasive tools to help identify patients likely to benefit from catheter ablation (CA) of atrial fibrillation (AF) would facilitate personalised treatment planning. Aim To investigate atrial waveform organisation through recurrence plot indices (RPI) and their ability to predict CA outcome. Methods One minute 12-lead ECG was recorded before CA from 62 patients with AF (32 paroxysmal AF; 45 men; age 57±10 years). Organisation of atrial waveforms from i) TQ intervals in V1 and ii) QRST suppressed continuous AF waveforms (CAFW), were quantified using RPI: percentage recurrence (PR), percentage determinism (PD), entropy of recurrence (ER). Ability to predict acute (terminating vs. non-terminating AF), 3-month and 6-month postoperative outcome (AF vs. AF free) were assessed. Results RPI either by TQ or CAFW analysis did not change significantly with acute outcome. Patients arrhythmia-free at 6-month follow-up had higher organisation in TQ intervals by PD (

Study on the non-linear metrics contribution to estimate atrial fibrillation organization from the surface electrocardiogram

[EN] Atrial fibrillation (AF) is the most frequently diagnosed arrhythmia, characterized by an uncoordinated atrial electrical activation, thus causing the atria to be unable to pump blood effectively. The prevalence of AF is expected to increase significantly in the next decades as the population ages. However, both the knowledge and the treatment of this arrhythmia still have to experiment a significant progress. Previous studies have reported that AF organization, which can be defined as the repetitiveness degree of the atrial activity pattern, correlates with the arrhythmia status as well as with the therapy outcome. Thus, estimating AF organization from surface electrocardiographic (ECG) recordings constitutes a very interesting approach because ECG recordings are easy and cheap to obtain. The objective of this doctoral thesis is to assess the use of a variety of nonlinear indices in the estimation of AF organization from single-lead noninvasive ECG recordings. Apart from the most common noninvasive AF organization estimators, such as Sample Entropy (SampEn) and the dominant atrial frequency (DAF), the following nonlinear indices have been studied: Fuzzy Entropy, Spectral Entropy, Lempel-Ziv Complexity and Hurst Exponents. Moreover, since the presence of noise and ventricular residuals affects the performance of nonlinear methods, the application of a strategy aimed at reducing these nuisances has been evaluated. Therefore, the application of these metrics over the atrial activity fundamental waveform, named the main atrial wave (MAW), has been proposed. In this doctoral thesis, the following scenarios involving AF organization have been considered: the prediction of paroxysmal AF spontaneous termination, the study of the earlier signs anticipating AF termination and the classification between paroxysmal and persistent AF from short ECG recordings. Firstly, the performance of the studied metrics discriminating events related to AF organization was tested making use of a reference database aimed at predicting AF spontaneous termination. In this study, most of the proposed indices provided higher accuracy than traditional AF organization estimators. Accuracy values higher than 90% were obtained with several indices. In particular, the generalized Hurst exponents of order 1 and 2, H(1) and H(2), achieved outstanding results, thus being selected for later studies in this thesis. Furthermore, the computation of H(2) depends on two critical parameters, namely, the analyzed interval length (L) and the maximum search window for self-similarities (tau). Hence, a study with 660 combinations on these two parameters was performed, together with the sampling frequency (fs) of the recording, in order to obtain their optimal combination in computing AF organization. On the other hand, previous works analyzing the spontaneous termination of AF have been only focused on the last 2 minutes preceding the termination. In contrast, a different scenario considering longer recordings to detect the earlier signs anticipating paroxysmal AF termination has been analyzed for the first time in this thesis. H(2) was selected for the study because of its highest accuracy in AF termination prediction. Additionally, the DAF and SampEn were also computed as references. Through this study it has been corroborated that AF organization only varies significantly within the last 3 minutes before spontaneous termination. As a consequence, the early prediction of paroxysmal AF spontaneous termination does not seem feasible through the current signal analysis tools. Finally, H(2) was applied in the classification between paroxysmal and persistent AF from short ECG recordings, achieving a higher diagnostic accuracy than DAF and SampEn. This result suggests that the analysis of ambulatory ECG recordings through H(2) could be a future alternative to the use of Holter ECG recordings in the classification between paroxysmal and persistent AF.[ES] La fibrilación auricular (FA) es la arritmia más frecuente y se caracteriza por una actividad auricular descoordinada, que impide que las aurículas bombeen sangre de manera eficaz. Se espera que la prevalencia de la FA aumente significativamente en las próximas décadas debido al envejecimiento de la población. Sin embargo, tanto el conocimiento relativo a esta arritmia como su tratamiento son todavía mejorables. Estudios previos han relacionado la organización de la FA, que se puede definir como el grado de repetitividad de la actividad auricular, con el estado de la arritmia o su respuesta al tratamiento. Además, la estimación de la organización de la FA a partir de registros electrocardiográficos (ECG) de superficie resulta especialmente interesante porque su obtención es sencilla y barata. El objetivo de esta tesis doctoral es evaluar el uso de distintos índices no lineales para estimar la organización de la FA a partir del ECG. Además de los estimadores no invasivos de organización más comunes, como la entropía muestral (SampEn) y la frecuencia auricular dominante (DAF), se han estudiado los siguientes métodos no lineales: la entropía borrosa, la entropía espectral, la complejidad Lempel-Ziv y los exponentes de Hurst. Además, se ha estudiado el uso de una estrategia destinada a la reducción del ruido y los residuos de actividad ventricular para mejorar el desempeño de métodos no lineales. Así, los índices estudiados también se han aplicado sobre la forma de onda fundamental de la actividad auricular, conocida como la onda auricular principal (MAW). Se han considerado los siguientes escenarios relacionados con la organización de la FA: la predicción de la terminación espontánea de la FA paroxística, el estudio de los primeros indicios de terminación espontánea de la FA y la clasificación entre FA paroxística y FA persistente a partir de registros ECG de corta duración. Primero, se estudió la capacidad de los índices estudiados para distinguir eventos relacionados con la organización de la FA mediante el análisis de una base de datos de referencia para la predicción de su terminación espontánea. La mayoría de los índices propuestos consiguieron una mayor precisión que los estimadores tradicionales de organización. Así, varios de los índices obtuvieron una precisión superior al 90% en la predicción de la terminación espontánea de la FA. En particular, los exponentes de Hurst generalizados de orden 1 y 2, H(1) y H(2), lograron los mejores resultados de clasificación. Puesto que el cálculo de H(2) depende de dos parámetros críticos, la longitud del intervalo analizado (L) y el tamaño máximo de la ventana donde buscar similitudes (tau), se llevó a cabo un estudio con 660 combinaciones de esos dos parámetros junto con la frecuencia de muestreo (fs) del registro para determinar el uso óptimo de este índice. Por otra parte, los trabajos previos que han estudiado la terminación espontánea de la FA se han centrado en los últimos 2 minutos antes de la terminación. Por contra, en esta tesis doctoral se han estudiado por primera vez registros de mayor duración para detectar los primeros indicios de la terminación de la FA. Para ello, se eligió el uso de H(2) por su alta precisión en la predicción de la terminación de la FA. Además, la DAF y SampEn se calcularon como referencias. En este estudio se ha comprobado que la organización de la FA solamente presenta variaciones significativas en los últimos 3 minutos antes de su terminación espontánea. Por ello, la predicción temprana de la terminación no parece posible con los medios actuales de análisis de la señal. Por último, se aplicó H(2) para clasificar entre FA paroxística y FA persistente a partir de ECGs de corta duración, obteniendo una mayor precisión diagnóstica que la DAF y SampEn. Este resultado sugiere que el análisis de ECGs ambulatorios por medio de H(2) puede ser en el futuro una alte[CA] La fibril·lació auricular (FA) és l'arítmia més freqüent i es caracteritza per una activitat auricular descoordinada, que impedix que les aurícules bomben sang de manera eficaç. S'espera que la prevalença de la FA augmente significativament en les pròximes dècades a causa de l'envelliment de la població. No obstant això, tant el coneixement relatiu a esta arítmia com el seu tractament són encara millorables. Estudis previs han relacionat l'organització de la FA, que es pot definir com el grau de repetitivitat de l'activitat auricular, amb l'estat de l'arítmia o la seua resposta al tractament. A més, l'estimació de l'organització de la FA a partir de registres electrocardiogràfics (ECG) de superfície resulta especialment interessant perquè la seua obtenció és senzilla i barata. L'objectiu d'esta tesi doctoral és avaluar l'ús de distints índexs no lineals en l'estimació de l'organització de la FA a partir de l'ECG de superfície. A més dels estimadors no invasius d'organització més comuns, com l'entropia mostral (SampEn) i la freqüència auricular dominant (DAF), s'han estudiat els següents mètodes no lineals: l'entropia borrosa, l'entropia espectral, la complexitat Lempel-Ziv i els exponents de Hurst. A més, s'ha estudiat l'ús d'una estratègia destinada a la reducció del soroll i els residus d'activitat ventricular per a millorar la seua capacitat d'estimar l'organització. Així, doncs, els índexs estudiats també s'han aplicat sobre la forma d'onda fonamental de l'activitat auricular, coneguda com l'onda auricular principal (MAW). S'han considerat els següents escenaris relacionats amb l'organització de la FA: la predicció de la terminació espontània de la FA paroxística, l'estudi dels primers indicis de terminació espontània de la FA i la classificació entre FA paroxística i FA persistent a partir de registres ECG de curta duració. Primer, es va estudiar la capacitat dels índexs estudiats per a distingir esdeveniments relacionats amb l'organització de la FA per mitjà de l'anàlisi d'una base de dades de referència per a la predicció de la seua terminació espontània. La majoria dels índexs proposats van aconseguir una major precisió que els estimadors tradicionals d'organització de la FA. Així, alguns dels índexs van obtindre una precisió superior al 90% en la predicció de la terminació espontània de la FA. En particular, els exponents de Hurst generalitzats d'orde 1 i 2, H(1) i H(2), van aconseguir els millors resultats de classificació. Com el càlcul de H(2) depén de dos paràmetres crítics, la longitud de l'interval analitzat (L) i la grandària màxima de la finestra on buscar similituds (tau), es va dur a terme un estudi amb 660 combinacions d'eixos dos paràmetres junt amb la freqüència de mostratge (fs) del registre per a determinar la combinació òptima de valors per a estimar l'organització de la FA. D'altra banda, els treballs previs que han estudiat la terminació espontània de la FA s'han centrat en els últims 2 minuts abans de la terminació. Per contra, en esta tesi doctoral s'han estudiat per primera vegada registres de major duració amb l'objectiu de detectar els primers indicis de la terminació de la FA. Es va triar l'ús de H(2) per a este estudi per la seua alta precisió en la predicció de la terminació de la FA. A més, la DAF i SampEn es van calcular com a referències. En este estudi s'ha comprovat que l'organització de la FA només presenta variacions significatives en els últims 3 minuts abans de la seua terminació espontània. Per això, la predicció primerenca de la terminació no pareix possible amb els mitjans actuals d'anàlisi del senyal. Finalment, es va aplicar H(2) per a classificar entre FA paroxística i FA persistent a partir d'ECGs de curta duració, obtenint una millor precisió diagnòstica que amb la DAF i SampEn. Este resultat suggerix que l'anàlisi d'ECGs ambulatoris per mitjà de H(2) pot ser en eJulián Seguí, M. (2015). Study on the non-linear metrics contribution to estimate atrial fibrillation organization from the surface electrocardiogram [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/56150TESI