Atrial fibrillation subtypes classification using the General Fourier-family Transform

Atrial fibrillation patients can be classified into paroxysmal, persistent and permanent attending to the temporal pattern of this arrhythmia. The surface electrocardiogram hides this differentiation. A classification method to discriminate between the different subtypes of atrial fibrillation by using short segments of electrocardiograms recordings is presented. We will process the electrocardiograms (ECGs) using time-frequency techniques with a global accuracy of 80%. Real cases are evaluated showing promising results for an implementation in a semiautomated diagnostic system.This work was supported by grants MTM2010-15200, PrometeoII/2013/013 and UPV-IIS La Fe, 2012/0468.Ortigosa, N.; Cano, O.; Ayala Gallego, G.; Galbis Verdu, A.; Fernandez Rosell, C. (2014). Atrial fibrillation subtypes classification using the General Fourier-family Transform. Medical Engineering and Physics. 36(4):554-560. https://doi.org/10.1016/j.medengphy.2013.12.005S55456036

Combined Nonlinear Analysis of Atrial and Ventricular Series for Automated Screening of Atrial Fibrillation

[EN] Atrial fibrillation (AF) is the most common cardiac arrhythmia in clinical practice. It often starts with asymptomatic and short episodes, which are difficult to detect without the assistance of automatic monitoring tools. The vast majority of methods proposed for this purpose are based on quantifying the irregular ventricular response (i.e., RR series) during the arrhythmia. However, although AF totally alters the atrial activity (AA) reflected on the electrocardiogram(ECG), replacing stable P-waves by chaotic and time-variant fibrillatory waves, this information has still not been explored for automated screening of AF. Hence, a pioneering AF detector based on quantifying the variability over time of the AA morphological pattern is here proposed. Results from two public reference databases have proven that the proposed method outperforms current state-of-the-art algorithms, reporting accuracy higher than 90%. A less false positive rate in the presence of other arrhythmias different from AF was also noticed. Finally, the combination of this algorithm with the classical analysis of RR series variability also yielded a promising trade-off between AF accuracy and detection delay. Indeed, this combination provided similar accuracy than RR-based methods, but with a significantly shorter delay of 10 beats.This work was supported by the Spanish Ministry of Economy and Competitiveness (Project TEC2014-52250-R).Rodenas, J.; Garcia, M.; Alcaraz, R.; Rieta, JJ. (2017). Combined Nonlinear Analysis of Atrial and Ventricular Series for Automated Screening of Atrial Fibrillation. Complexity. (2163610):1-13. doi:10.1155/2017/2163610S113216361

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

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

The Contribution of Nonlinear Methods in the Understanding of Atrial Fibrillation

This work was supported by the projects TEC2010–20633 from the Spanish Ministry of Science and Innovation and PPII11–0194–8121 and PII1C09–0036–3237 from Junta de Comunidades de Castilla-La Mancha.Alcaraz Martínez, R.; Rieta Ibañez, JJ. (2013). The Contribution of Nonlinear Methods in the Understanding of Atrial Fibrillation. En Atrial Fibrillation - Mechanisms and Treatment. InTech. 181-204. https://doi.org/10.5772/53407S18120

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/

Development of new signal analysis methods as preoperative predictors of the Cox-Maze procedure outcome in atrial fibrillation

Atrial fibrilation (AF) is the most common cardiac arrhythmia, however, the knowledge about its causes and mechanisms is still uncompleted. Several studies suggest that atrial structural and electrophysiological remodeling are directly related to its development and perpetuation. To this respect, ECG and preoperative clinical data have been studied to analyze different aspects of atrial remodeling. Nonetheless, there is a lack of studies using ECG parameters to provide valuable clinical information in the study of AF aggressive treatments, such as the Cox-Maze surgery. In this work, ECG parameters such as fibrillatory (f) waves organization and amplitude are studied to predict patient's rhythm from the discharge after the Cox-Maze surgery until a twelve months follow up period. On the other hand, widely used clinical parameters such as age, AF duration and left atrial size (LA size) are studied to assess electrocardiographic results. In addition, clinical information known as a risk factor to develop AF such as weight and body mass index has also been analyze. After assess the individual indices, classification models were created in order to optimize the prediction capability. The results obtained reported that the ECG indices outperform the cinical indices. Nevertheless, the information contained in both types of indices is complementary as the generation of a classification model combining the indices shows. This model exceeded 90% accuracy in each period analyzed. In conclusion, studying the AF information contained in an ECG could provide new data to understand the AF and also could help to develop a reliable method to predict preoperatively the Cox-Maze outcome.La fibrilación auricular (FA) es la arritmia cardiaca más comúnmente encontrada en la práctica clínica diaria, sin embargo, todavía no se comprenden completamente los mecanismos fisiológicos que causan el inicio y la perpetuación de la FA. Diversos estudios sugieren que el remodelado estructural y electrofisiológico de la aurícula está relacionado directamente con el desarrollo y perpetuación de la FA. En este sentido, se ha estudiado el ECG e información clínica preoperatoria para analizar distintos aspectos del remodelado. Sin embargo, hay una falta de estudios usando parámetros electrocardiográficos para proporcionar información clínica valiosa en el estudio de tratamientos agresivos de la FA como la cirugía Cox-Maze. En este trabajo, se estudian parámetros electrocardiográficos como la organización de las ondas fibrilatorias y su amplitud para predecir el ritmo de los pacientes desde el momento del alta, tras la cirugía Cox-Maze hasta 12 meses después de la operación. Por otro lado, para evaluar la capacidad de dichos índices, se han utilizado parámetros clínicos ampliamente utilizados como la edad, el tamaño de la aurícula izquierda y el tiempo en FA. Además, se han estudiado también parámetros clínicos conocidos como factores de riesgo para desarrollar FA como son el peso y el índice de masa corporal. Tras analizar la capacidad predictiva de los índices individualmente, éstos se han combinado mediante la generación de modelos de predicción para optimizar la precisión de las predicciones. Los resultados obtenidos señalan que la información contenida en el ECG obtuvo resultados estadísticamente significativos y predicciones más precisas que los índices clínicos. No obstante, el desarrollo de modelos de predicción combinando ambos tipos de índices superó al uso de éstos por separado, con resultados por encima del 90% en todos los períodos estudiados. En conclusión, el análisis del ECG podría aportar nuevos enfoques a la hora de estudiar la FA, y su uso como herramienta de predicción podría ayudar a desarrollar tratamientos más eficientes y personalizados.La fibril·lació auricular (FA) és l'arítmia cardíaca més comunament trobada en la pràctica clínica diària, no obstant això, encara no es comprenen completament els mecanismes fisiològics que causen l'inici i la perpetuació de la FA. Diversos estudis suggerixen que el remodelat estructural i electrofisiològic de l'aurícula està relacionat directament amb el desenrotllament i perpetuació de la FA. En este sentit, s'ha estudiat l'ECG i informació clínica preoperatòria per a analitzar distints aspectes del remodelat. No obstant això, hi ha una falta d'estudis usant paràmetres electrocardiográficos per a proporcionar informació clínica valuosa en l'estudi de tractaments agressius de la FA com la cirurgia Cox-Maze. En este treball, s'estudien paràmetres electrocardiográficos com l'organització de les ones fibrilatorias i la seua amplitud per a predir el ritme dels pacients des del moment de l'alta, després de la cirurgia Cox-Maze fins a 12 mesos després de l'operació. Per un altre costat per a avaluar la capacitat dels dits índexs, s'han utilitzat paràmetres clínics àmpliament utilitzats com l'edat, la grandària de l'aurícula esquerra i el temps en FA. A més, s'han estudiat també paràmetres clínics coneguts com a factors de risc per a desenrotllar FA com són el pes i l'índex de massa corporal. Després d'analitzar la capacitat predictiva dels índexs individualment, estos s'han combinat per mitjà de la generació de models de predicció per a optimitzar la precisió de les prediccions. Els resultats obtinguts assenyalen que la informació continguda en l'ECG va obtindre resultats estadísticament significatius i prediccions més precises que els índexs clínics. No obstant això, el desenrotllament de models de predicció combinant ambdós tipus d'índexs va superar a l'ús d'estos per separat, amb resultats per damunt del 90% en tots els períodes estudiats. En conclusió, l'anàlisi de l'ECG podria aportar nous enfocaments a l'hora d'estudiar la FA, i el seu ús com a ferramenta de predicció podria ajudar a desenrotllar tractaments més eficients i personalitzats.Hernández Alonso, A. (2017). Development of new signal analysis methods as preoperative predictors of the Cox-Maze procedure outcome in atrial fibrillation [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/90491TESI

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