A method for context-based adaptive QRS clustering in real-time

Continuous follow-up of heart condition through long-term electrocardiogram monitoring is an invaluable tool for diagnosing some cardiac arrhythmias. In such context, providing tools for fast locating alterations of normal conduction patterns is mandatory and still remains an open issue. This work presents a real-time method for adaptive clustering QRS complexes from multilead ECG signals that provides the set of QRS morphologies that appear during an ECG recording. The method processes the QRS complexes sequentially, grouping them into a dynamic set of clusters based on the information content of the temporal context. The clusters are represented by templates which evolve over time and adapt to the QRS morphology changes. Rules to create, merge and remove clusters are defined along with techniques for noise detection in order to avoid their proliferation. To cope with beat misalignment, Derivative Dynamic Time Warping is used. The proposed method has been validated against the MIT-BIH Arrhythmia Database and the AHA ECG Database showing a global purity of 98.56% and 99.56%, respectively. Results show that our proposal not only provides better results than previous offline solutions but also fulfills real-time requirements.Comment: 12 pages, 6 figure

Controlled Exposure Study of Air Pollution and T-Wave Alternans in Volunteers without Cardiovascular Disease

Background: Epidemiological studies have assessed T-wave alternans (TWA) as a possible mechanism of cardiac arrhythmias related to air pollution in high-risk subjects and have reported associations with increased TWA magnitude. Objective: In this controlled human exposure study, we assessed the impact of exposure to concentrated ambient particulate matter (CAP) and ozone (O:3) on T-wave alternans in resting volunteers without preexisting cardiovascular disease. Methods: Seventeen participants without preexisting cardiovascular disease were randomized to filtered air (FA), CAP (150 μg/m3), O3 (120 ppb), or combined CAP + O3 exposures for 2 hr. Continuous electrocardiograms (ECGs) were recorded at rest and T-wave alternans (TWA) was computed by modified moving average analysis with QRS alignment for the artifact-free intervals of 20 beats along the V2 and V5 leads. Exposure-induced changes in the highest TWA magnitude (TWAMax) were estimated for the first and last 5 min of each exposure (TWAMax_Early and TWAMax_Late respectively). ΔTWAMax (Late–Early) were compared among exposure groups using analysis of variance. Results: Mean ± SD values for ΔTWA:Max were –2.1 ± 0.4, –2.7 ± 1.1, –1.9 ± 1.5, and –1.2 ± 1.5 in FA, CAP, O3, and CAP + O3 exposure groups, respectively. No significant differences were observed between pollutant exposures and FA. Conclusion: In our study of 17 volunteers who had no preexisting cardiovascular disease, we did not observe significant changes in T-wave alternans after 2-hr exposures to CAP, O:3, or combined CAP + O3. This finding, however, does not preclude the possibility of pollution-related effects on TWA at elevated heart rates, such as during exercise, or the possibility of delayed responses

Pattern recognition beyond classification: An abductive framework for time series interpretation

Time series interpretation aims to provide an explanation of what is observed in terms of its underlying processes. The present work is based on the assumption that the common classification-based approaches to time series interpretation suffer from a set of inherent weaknesses, whose ultimate cause lies in the monotonic nature of the deductive reasoning paradigm. In this thesis we propose a new approach to this problem, based on the initial hypothesis that abductive reasoning properly accounts for the human ability to identify and characterize the patterns appearing in a time series. The result of this interpretation is a set of conjectures in the form of observations, organized into an abstraction hierarchy and explaining what has been observed. A knowledge-based framework and a set of algorithms for the interpretation task are provided, implementing a hypothesize-and-test cycle guided by an attentional mechanism. As a representative application domain, interpretation of the electrocardiogram allows us to highlight the strengths of the present approach in comparison with traditional classification-based approaches

Positive and Negative Evidence Accumulation Clustering for Sensor Fusion: An Application to Heartbeat Clustering

In this work, a new clustering algorithm especially geared towards merging data arising from multiple sensors is presented. The algorithm, called PN-EAC, is based on the ensemble clustering paradigm and it introduces the novel concept of negative evidence. PN-EAC combines both positive evidence, to gather information about the elements that should be grouped together in the final partition, and negative evidence, which has information about the elements that should not be grouped together. The algorithm has been validated in the electrocardiographic domain for heartbeat clustering, extracting positive evidence from the heartbeat morphology and negative evidence from the distances between heartbeats. The best result obtained on the MIT-BIH Arrhythmia database yielded an error of 1.44%. In the St. Petersburg Institute of Cardiological Technics 12-Lead Arrhythmia Database database (INCARTDB), an error of 0.601% was obtained when using two electrocardiogram (ECG) leads. When increasing the number of leads to 4, 6, 8, 10 and 12, the algorithm obtains better results (statistically significant) than with the previous number of leads, reaching an error of 0.338%. To the best of our knowledge, this is the first clustering algorithm that is able to process simultaneously any number of ECG leads. Our results support the use of PN-EAC to combine different sources of information and the value of the negative evidenceThis research was funded by the Ministry of Science, Innovation and Universities of Spain, and the European Regional Development Fund of the European Commission, Grant Nos. RTI2018-095324-B-I00, RTI2018-097122-A-I00, and RTI2018-099646-B-I00S

Deteção de extra-sístoles ventriculares

Tese de mestrado integrado. Bioengenharia. Área de Especialização de Engenharia Biomédica. Faculdade de Engenharia. Universidade do Porto. 201

Blind Source Separation for the Processing of Contact-Less Biosignals

(Spatio-temporale) Blind Source Separation (BSS) eignet sich für die Verarbeitung von Multikanal-Messungen im Bereich der kontaktlosen Biosignalerfassung. Ziel der BSS ist dabei die Trennung von (z.B. kardialen) Nutzsignalen und Störsignalen typisch für die kontaktlosen Messtechniken. Das Potential der BSS kann praktisch nur ausgeschöpft werden, wenn (1) ein geeignetes BSS-Modell verwendet wird, welches der Komplexität der Multikanal-Messung gerecht wird und (2) die unbestimmte Permutation unter den BSS-Ausgangssignalen gelöst wird, d.h. das Nutzsignal praktisch automatisiert identifiziert werden kann. Die vorliegende Arbeit entwirft ein Framework, mit dessen Hilfe die Effizienz von BSS-Algorithmen im Kontext des kamera-basierten Photoplethysmogramms bewertet werden kann. Empfehlungen zur Auswahl bestimmter Algorithmen im Zusammenhang mit spezifischen Signal-Charakteristiken werden abgeleitet. Außerdem werden im Rahmen der Arbeit Konzepte für die automatisierte Kanalauswahl nach BSS im Bereich der kontaktlosen Messung des Elektrokardiogramms entwickelt und bewertet. Neuartige Algorithmen basierend auf Sparse Coding erwiesen sich dabei als besonders effizient im Vergleich zu Standard-Methoden.(Spatio-temporal) Blind Source Separation (BSS) provides a large potential to process distorted multichannel biosignal measurements in the context of novel contact-less recording techniques for separating distortions from the cardiac signal of interest. This potential can only be practically utilized (1) if a BSS model is applied that matches the complexity of the measurement, i.e. the signal mixture and (2) if permutation indeterminacy is solved among the BSS output components, i.e the component of interest can be practically selected. The present work, first, designs a framework to assess the efficacy of BSS algorithms in the context of the camera-based photoplethysmogram (cbPPG) and characterizes multiple BSS algorithms, accordingly. Algorithm selection recommendations for certain mixture characteristics are derived. Second, the present work develops and evaluates concepts to solve permutation indeterminacy for BSS outputs of contact-less electrocardiogram (ECG) recordings. The novel approach based on sparse coding is shown to outperform the existing concepts of higher order moments and frequency-domain features

Estrategias estáticas y dinámicas para el agrupamiento de latidos mediante acumulación de evidencia

Esta tesis presenta un conjunto de soluciones de procesado eficiente del ECG para su subsiguiente interpretación clínica. Estas soluciones ponen el acento en la representación del latido y su agrupamiento morfológico, destacando las siguientes contribuciones: 1. Se ha llevado a cabo un estudio riguroso sobre la representación óptima de complejos QRS mediante funciones de Hermite, empleando criterios derivados de la teoría de la información, y se ha implementado un cálculo eficiente de esta representación explotando el paralelismo de las GPUs. 2. Se ha diseñado una estrategia de agrupamiento estático basada en acumulación de evidencia (PN-EAC, Positive and Negative Evidence Accumulation), así como una versión dinámica (EPN-EAC, Evolving Positive and Negative Evidence Accumulation). Una vez validadas sobre las bases de datos de referencia, los resultados son comparables o incluso mejores que los resultados previamente publicados. Cabe resaltar que ambas estrategias son independientes del dominio y, por tanto, aplicables a otros problemas

Agrupamiento dinámico de complejos QRS en tiempo real

Esta tesis se encuadra dentro del ámbito del análisis automático de las señales electrocardiográficas (ECG) y, en ella, se presenta un método de agrupamiento de latidos en tiempo real de carácter adaptativo, cuyo objetivo es facilitar la separación de los latidos presentes en un registro electrocardiográfico multicanal en función de su ritmo y patrón de activación/propagación en el tejido cardíaco, representándolos mediante un conjunto dinámico de grupos. Se ha desarrollado una implementación que ha permitido verificar el cumplimiento de las restricciones temporales necesarias para su ejecución en tiempo real , y realizar una validación sobre las bases de datos de referencia "MIT-BIH Arrhythmia Database" y "AHA ECG Database" siguiendo las recomendaciones de los estándares internacionales. En la bibliografía no se ha encontrado referencia a ningún trabajo que aborde el objetivo del agrupamiento dinámico, por lo que se han comparado los resultados obtenido con los publicados para métodos de agrupamiento estático, mostrando un rendimiento igual o superior

Towards a better understanding of the precordial leads : an engineering point of view

This thesis provides comprehensive literature review of the electrocardiography evolution to highlight the important theories behind the development of the electrocardiography device. More importantly, it discusses different electrode placement on the chest, and their clinical advantages. This work presents a technical detail of a new ECG device which was developed at MARCS institute and can record the Wilson Central Terminal (WCT) components in addition to the standard 12-lead ECG. This ECG device was used to record from 147 patients at Campbelltown hospital over three years. The first two years of recording contain 92 patients which was published in the Physionet platform under the name of Wilson Central Terminal ECG database (WCTECGdb). This novel dataset was used to demonstrate the WCT signal characterisation and investigate how WCT impacts the precordial leads. Furthermore, the clinical influence of the WCT on precordial leads in patients diagnosed with non-ST segment elevation myocardial infarction (NSTEMI) is discussed. The work presented in this research is intended to revisit some of the ECG theories and investigate the validity of them using the recorded data. Furthermore, the influence of the left leg potential on recording the precordial leads is presented, which lead to investigate whether the WCT and augmented vector foot (aVF) are proportional. Finally, a machine learning approach is proposed to minimise the Wilson Central Terminal