Cardiac Inter Beat Interval and Atrial Fibrillation Detection using Video Plethysmography

Facial videoplethysmography provides non-contact measurement of heart activity based on blood volume pulsations detected in facial tissue. Typically, the signal is extracted using a simple webcam followed by elaborated signal processing methods, and provides limited accuracy of time-domain characteristics. In this study, we explore the possibility of providing accurate time-domain pulse and inter-beat interval measurements using a high- quality image sensor camera and various signal processing approaches, and use these measurements to diagnose atrial fibrillation. We capture synchronized signals using a high- quality camera, a simple webcam, an earlobe photoplethysmography sensor, and a body- surface electrocardiogram from a large group of subjects, including subjects diagnosed with cardiac arrhythmias. All signals are processed using both blind source separation and color conversion. We then assess accuracy of IBI detection, heart rate variability estimation, and atrial fibrillation diagnose by comparing to a body-surface electrocardiogram. We present a new heart variability indicator for blood volume pulsating signals. Our results demonstrate that the accuracy of a facial VPG system is greatly improved when using a high-quality camera. Coupling the high-quality camera with color conversion from RGB to Hue provides a level of accuracy equivalent to that of commercially available photoplethysmography sensors, and offers a non-contact alternative to current technology for heart rate variability assessment and atrial fibrillation screening

Detection and measurement and of repolarisation features in atrial fibrillation and healthy subjects

Major cardiac organisations recommended U wave abnormalities should be reported during ECG interpretation. However, U waves cannot be measured in patients with atrial fibrillation (AF) due to the obscuring fibrillatory wave.The first aim of the research was to provide a validated algorithm to clean the ECGs of AF patients by removing the atrial fibrillatory waves so that the characteristics of ventricle repolarisation components, U and T waves, could be detected and measured accurately without fibrillatory wave contamination.Having established a validated algorithm to measure the waveform features, the second aim was to use this algorithm to investigate the effect of beat interval dependency on the repolarisation waves, especially U waves, during AF and to compare them to those in sinus rhythm (SR) of healthy subjects. The research could provide mechanistic insight into the origin of U waves since AF is unique in its rapidly changing ventricular beat intervals. The preceding beat interval has a direct impact on ventricular filling dynamics and hence also on mechano-electrical coupling, one of the leading hypotheses of U wave genesis.Algorithms were developed to remove the contaminating fibrillatory waves in AF recordings and to measure features of the ventricular repolarisation waves.The ventricular repolarisation features, U and T waves, are measurable and dependent on preceding beat interval in AF and SR. The beat interval dependency of repolarisation features, especially the U wave, supported the mechano-electrical hypothesis during AF and SR.The research provides tools to facilitate the detection and reporting of U waves and their abnormalities in AF patients and provides mechanistic insight into rate dependency of ventricular repolarisation features

Characterization of the Substrate Modification in Patients Undergoing Catheter Ablation of Atrial Fibrillation

Tesis por compendio[ES] La fibrilación auricular (FA) es la arritmia cardíaca más común. A pesar de la gran popularidad de la ablación con catéter (AC) como tratamiento principal, todavía hay margen de mejora. Aunque las venas pulmonares (VPs) son los principales focos de FA, muchos sitios pueden contribuir a su propagación, formando el sustrato de la FA (SFA). El mapeo preciso del SFA y el registro de la modificación del SFA, como marcador positivo después de AC, son fundamentales. Los electrocardiogramas (ECG) y los electrogramas (EGM) se reclutan para este propósito. Los EGM se utilizan para detectar candidatos de AC como áreas que provocan o perpetúan la FA. Por lo tanto, el análisis de EGM es una parte indispensable de AC. Con la capacidad de observar las aurículas globalmente, la principal aplicación de los ECG es evaluar la modificación del SFA analizando las ondas f o P. A pesar del extenso análisis de cualquiera de los tipos de registro, existen algunas brechas. La AC no-VP aumenta el tiempo en quirófano, provocando mayores riesgos y costos. En cuanto al análisis de la modificación del SFA, se utilizan varios umbrales para definir una onda P prolongada. El principal objetivo de la presente Tesis es contribuir al esfuerzo de análisis de SFA y de modificación de SFA. Para ello, la presente Tesis se desarrolló bajo dos hipótesis principales. Que la calidad de la información extraída durante el SFA y el análisis de modificación del SFA se puede mejorar mediante la introducción de pasos innovadores. Además, la combinación de análisis de ECG y EGM puede aumentar la resolución del mapeo y revelar nueva información sobre los mecanismos de FA. Para cumplir con el objetivo principal, el análisis se divide en 4 partes, conformando los 4 capítulos del Compendio de articulos. En primer lugar, se reclutó la dimensión de correlación de grano grueso (DCGG). DCGG localizó de manera confiable EGM complejos y la clasificación por tipos de FA arrojó una precisión del 84 %. Luego, se adoptó un análisis alternativo de la onda P, estudiando por separado su primera y su segunda parte, correspondientes a la aurícula derecha (AD) e izquierda (AI). Los resultados indicaron LA como la principal fuente de modificación del SFA y subrayaron la importancia de estudiar partes integrales de ECG. Los hallazgos de este estudio también sugieren la implementación de partes integrales de ondas P como un posible alivio de las discrepancias en los umbrales de ondas P para definir el tejido fibrótico. Posteriormente, se estudió el efecto diferente del aislamiento de la VP izquierda (AVPI) y derecha (AVPD) sobre la modificación del SFA. AVPI fue la parte crítica, siendo la fuente exclusiva de acortamiento de onda P. El análisis de los registros durante la AC también permitió una observación más cercana de las fluctuaciones de la variabilidad de la frecuencia cardíaca (VFC) a lo largo del procedimiento de CA, lo que reveló información sobre el efecto de la energía de radiofrecuencia (RF) en el tejido auricular. La última parte se centró en el seno coronario (SC), una estructura fundamental en el mapeo de FA para aumentar la resolución de la información. Se definieron los canales más y menos robustos durante el ritmo sinusal (RS) y se investigó la utilidad de SC en la evaluación de la modificación del SFA. Aunque CS no proporcionó una imagen global de la alteración del SFA, pudo registrar con mayor sensibilidad las fluctuaciones en la respuesta auricular durante la AC. Los hallazgos presentados en esta Tesis Doctoral ofrecen una perspectiva alternativa sobre la modificación del SFA y contribuyen al esfuerzo general sobre el mapeo de FA y la evaluación del sustrato posterior a la CAAC, abriendo futuras líneas de investigación hacia una resolución más alta y un mapeo más eficiente de los mecanismos desencadenantes de la FA.[CA] La fibril·lació auricular (FA) és l'arítmia cardíaca més comú. Tot i la gran popularitat de l'ablació amb catèter (AC) com a tractament principal, encara hi ha marge de millora. Tot i que les venes pulmonars (VPs) són els principals focus de FA, molts llocs poden contribuir a la seva propagació, formant el substrat de la FA (SFA). El mapatge precís de l'SFA i el registre de la modificació de l'SFA, com a marcador positiu després d'AC, són fonamentals. Els electrocardiogrames (ECG) i els electrogrames (EGM) es recluten per a aquest propòsit. Els EGM es fan servir per detectar candidats d'AC com a àrees que provoquen o perpetuen la FA. Per tant, lanàlisi dEGM és una part indispensable dAC. Amb la capacitat d'observar les aurícules globalment, la principal aplicació dels ECG és avaluar la modificació de l'SFA analitzant les ones f o P. Tot i l'extensa anàlisi de qualsevol dels tipus de registre, hi ha algunes bretxes. L'AC no-VP augmenta el temps a quiròfan, provocant majors riscos i costos. Pel que fa a l'anàlisi de la modificació de l'SFA, s'utilitzen diversos llindars per definir una ona P perllongada. L'objectiu principal d'aquesta Tesi és contribuir a l'esforç d'anàlisi de SFA i de modificació de SFA. Per això, aquesta Tesi es va desenvolupar sota dues hipòtesis principals. Que la qualitat de la informació extreta durant el SFA i lanàlisi de modificació de lSFA es pot millorar mitjançant la introducció de passos innovadors. A més, la combinació d'anàlisi d'ECG i EGM pot augmentar la resolució del mapatge i revelar informació nova sobre els mecanismes de FA. Per complir amb l'objectiu principal, l'anàlisi es divideix en 4 parts i es conforma els 4 capítols del Compendi d'articles. En primer lloc, es va reclutar la dimensió de correlació de gra gruixut (DCGG). DCGG va localitzar de manera fiable EGM complexos i la classificació per tipus de FA va donar una precisió del 84%. Després, es va adoptar una anàlisi alternativa de l'ona P, estudiant per separat la primera i la segona part corresponents a l'aurícula dreta (AD) i esquerra (AI). Els resultats van indicar LA com la font principal de modificació de l'SFA i van subratllar la importància d'estudiar parts integrals d'ECG. Les troballes d'aquest estudi també suggereixen la implementació de parts integrals d'ones P com a possible alleugeriment de les discrepàncies als llindars d'ones P per definir el teixit fibròtic. Posteriorment, es va estudiar l'efecte diferent de l'aïllament de la VP esquerra (AVPI) i la dreta (AVPD) sobre la modificació de l'SFA. AVPI va ser la part crítica, sent la font exclusiva d'escurçament d'ona P. L'anàlisi dels registres durant l'AC també va permetre una observació més propera de les fluctuacions de la variabilitat de la freqüència cardíaca (VFC) al llarg del procediment de CA , cosa que va revelar informació sobre l'efecte de l'energia de radiofreqüència (RF) en el teixit auricular. L'última part es va centrar al si coronari (SC), una estructura fonamental al mapeig de FA per augmentar la resolució de la informació. Es van definir els canals més i menys robustos durant el ritme sinusal (RS) i es va investigar la utilitat de SC a l'avaluació de la modificació de l'SFA. Tot i que CS no va proporcionar una imatge global de l'alteració de l'SFA, va poder registrar amb més sensibilitat les fluctuacions a la resposta auricular durant l'AC. Les troballes presentades en aquesta Tesi Doctoral ofereixen una perspectiva alternativa sobre la modificació de l'SFA i contribueixen a l'esforç general sobre el mapeig de FA i l'avaluació del substrat posterior a la CAAC, obrint futures línies de recerca cap a una resolució més alta i un mapeig més eficient dels mecanismes desencadenants de la FA.[EN] Atrial fibrillation (AF) is the commonest cardiac arrhythmia. Despite the high popularity of catheter ablation (CA) as the main treatment, there is still room for improvement. Time spent in AF affects the AF confrontation and evolution, with 1,15% of paroxysmal AF patients progressing to persistent annually. Therefore, from diagnosis to follow-up, every aspect that contributes to the AF confrontation is of utmost importance. Although pulmonary veins (PVs) are the main AF foci, many sites may contribute to the AF propagation, by triggering or sustaining the AF, forming the AF substrate. Precise AF substrate mapping and recording of the AF substrate modification, as a positive marker after CA sessions, are critical. Electrocardiograms (ECGs) and electrograms (EGMs) are vastly recruited for this purpose. EGMs are used to detect candidate CA targets as areas that provoke or perpetuate AF. Hence, EGMs analysis is an indispensable part of the CA procedure. With the ability to observe the atria globally, ECGs' main application is to assess the AF substrate modification by analyzing f- or P-waves from recordings before and after CA. Despite the extensive analysis on either recording types, some gaps exist. Non-PV CA increases the time in operation room, provoking higher risks and costs. Furthermore, whether non-PV CA is beneficial is under dispute. As for the AF substrate modification analysis, various thresholds are used to define a prolonged P-wave, related with poor CA prognostics. The main objective of the present Thesis is to contribute to the effort of AF substrate and AF substrate modification analysis. For this purpose, the present Thesis was developed under two main hypotheses. That the information quality extracted during AF substrate and AF substrate modification analysis can be improved by introducing innovative steps. Also, that combining ECG and EGM analysis can augment the mapping resolution and reveal new information regarding AF mechanisms. To accomplish the main objective, the analysis is split in 4 parts, forming the 4 chapters of the Compendium of publications. Firstly, coarse-grained correlation dimension (CGCD) was recruited. CGCD reliably localized highly complex EGMs and classification by AF types yielded 84% accuracy. Then, an alternative P-wave analysis was suggested, studying separately the first and second P-wave parts, corresponding to the right (RA) and left (LA) atrium. The findings indicated LA as the main AF substrate modification source and underlined the importance of studying integral ECG parts. The findings of this study additionally suggest the implementation of integral P-wave parts as a possible alleviation for the discrepancies in P-wave thresholds to define fibrotic tissue. Afterwards, the different effect of left (LPVI) and right pulmonary vein isolation (RPVI) on the AF substrate modification was studied. LPVI was the critical part, being the exclusive source of P-wave shortening. Analysis of recordings during CA also allowed a closer observation of the heart rate variability (HRV) fluctuations throughout the CA procedure, revealing information on the effect of radiofrequency (RF) energy on the atrial tissue. The last part was focused on coronary sinus (CS), a fundamental structure in AF mapping to increase the information resolution. The most and least robust channels during sinus rhythm (SR) were defined and the utility of CS in AF substrate modification evaluation was investigated. Although CS did not provide a global picture of the AF substrate alteration, it was able to record with higher sensitivity the fluctuations in the atrial response during the application of RF energy. The findings presented in this Doctoral Thesis offer an alternative perspective on the AF substrate modification and contribute to the overall effort on AF mapping and post-CA substrate evaluation, opening future lines of research towards a higher resolution and more efficient mapping of the AF drivers.Vraka, A. (2022). Characterization of the Substrate Modification in Patients Undergoing Catheter Ablation of Atrial Fibrillation [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/191410Compendi

Electrocardiogram pattern recognition and analysis based on artificial neural networks and support vector machines: a review.

Computer systems for Electrocardiogram (ECG) analysis support the clinician in tedious tasks (e.g., Holter ECG monitored in Intensive Care Units) or in prompt detection of dangerous events (e.g., ventricular fibrillation). Together with clinical applications (arrhythmia detection and heart rate variability analysis), ECG is currently being investigated in biometrics (human identification), an emerging area receiving increasing attention. Methodologies for clinical applications can have both differences and similarities with respect to biometrics. This paper reviews methods of ECG processing from a pattern recognition perspective. In particular, we focus on features commonly used for heartbeat classification. Considering the vast literature in the field and the limited space of this review, we dedicated a detailed discussion only to a few classifiers (Artificial Neural Networks and Support Vector Machines) because of their popularity; however, other techniques such as Hidden Markov Models and Kalman Filtering will be also mentioned

Multimodal Signal Processing for Diagnosis of Cardiorespiratory Disorders

This thesis addresses the use of multimodal signal processing to develop algorithms for the automated processing of two cardiorespiratory disorders. The aim of the first application of this thesis was to reduce false alarm rate in an intensive care unit. The goal was to detect five critical arrhythmias using processing of multimodal signals including photoplethysmography, arterial blood pressure, Lead II and augmented right arm electrocardiogram (ECG). A hierarchical approach was used to process the signals as well as a custom signal processing technique for each arrhythmia type. Sleep disorders are a prevalent health issue, currently costly and inconvenient to diagnose, as they normally require an overnight hospital stay by the patient. In the second application of this project, we designed automated signal processing algorithms for the diagnosis of sleep apnoea with a main focus on the ECG signal processing. We estimated the ECG-derived respiratory (EDR) signal using different methods: QRS-complex area, principal component analysis (PCA) and kernel PCA. We proposed two algorithms (segmented PCA and approximated PCA) for EDR estimation to enable applying the PCA method to overnight recordings and rectify the computational issues and memory requirement. We compared the EDR information against the chest respiratory effort signals. The performance was evaluated using three automated machine learning algorithms of linear discriminant analysis (LDA), extreme learning machine (ELM) and support vector machine (SVM) on two databases: the MIT PhysioNet database and the St. Vincent’s database. The results showed that the QRS area method for EDR estimation combined with the LDA classifier was the highest performing method and the EDR signals contain respiratory information useful for discriminating sleep apnoea. As a final step, heart rate variability (HRV) and cardiopulmonary coupling (CPC) features were extracted and combined with the EDR features and temporal optimisation techniques were applied. The cross-validation results of the minute-by-minute apnoea classification achieved an accuracy of 89%, a sensitivity of 90%, a specificity of 88%, and an AUC of 0.95 which is comparable to the best results reported in the literature

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/

Identification of cardiac signals in ambulatory ECG data

The Electrocardiogram (ECG) is the primary tool for monitoring heart function. ECG signals contain vital information about the heart which informs diagnosis and treatment of cardiac conditions. The diagnosis of many cardiac arrhythmias require long term and continuous ECG data, often while the participant engages in activity. Wearable ambulatory ECG (AECG) systems, such as the common Holter system, allow heart monitoring for hours or days. The technological trajectory of AECG systems aims towards continuous monitoring during a wide range of activities with data processed locally in real time and transmitted to a monitoring centre for further analysis. Furthermore, hierarchical decision systems will allow wearable systems to produce alerts or even interventions. These functions could be integrated into smartphones.A fundamental limitation of this technology is the ability to identify heart signal characteristics in ECG signals contaminated with high amplitude and non-stationary noise. Noise processing become more severe as activity levels increase, and this is also when many heart problems are present.This thesis focuses on the identification of heart signals in AECG data recorded during participant activity. In particular, it explored ECG filters to identify major heart conditions in noisy AECG data. Gold standard methods use Extended Kalman filters with extrapolation based on sum of Gaussian models. New methods are developed using linear Kalman filtering and extrapolation based on a sum of Principal Component basis signals. Unlike the gold standard methods, extrapolation is heartcycle by heartcycle. Several variants are explored where basic signals span one or two heartcycles, and applied to single or multi-channel ECG data.The proposed methods are extensively tested against standard databases or normal and abnormal ECG data and the performance is compared to gold standard methods. Two performance metrics are used: improvement in signal to noise ratio and the observability of clinically important features in the heart signal. In all tests the proposed method performs better, and often significantly better, than the gold standard methods. It is demonstrated that abnormal ECG signals can be identified in noisy AECG data