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

Quality Assessment of Ambulatory Electrocardiogram Signals by Noise Detection using Optimal Binary Classification

In order to improve the diagnostic capability in Ambulatory Electrocardiogram signal and to reduce the noise signal impacts, there is a need for more robust models in place. In terms of improvising to the existing solutions, this article explores a novel binary classifier that learns from the features optimized by fusion of diversity assessment measures, which performs Quality Assessment of Ambulatory Electrocardiogram Signals (QAAES) by Noise Detection. The performance of the proposed model QAAES has been scaled by comparing it with contemporary models. Concerning performance analysis, the 10-fold cross-validation has been carried on a benchmark dataset. The results obtained from experiments carried on proposed and other contemporary models for cross-validation metrics have been compared to signify the sensitivity, specificity, and noise detection accuracy

Flexible Time Series Matching for Clinical and Behavioral Data

Time Series data became broadly applied by the research community in the last decades after a massive explosion of its availability. Nonetheless, this rise required an improvement in the existing analysis techniques which, in the medical domain, would help specialists to evaluate their patients condition. One of the key tasks in time series analysis is pattern recognition (segmentation and classification). Traditional methods typically perform subsequence matching, making use of a pattern template and a similarity metric to search for similar sequences throughout time series. However, real-world data is noisy and variable (morphological distortions), making a template-based exact matching an elementary approach. Intending to increase flexibility and generalize the pattern searching tasks across domains, this dissertation proposes two Deep Learning-based frameworks to solve pattern segmentation and anomaly detection problems. Regarding pattern segmentation, a Convolution/Deconvolution Neural Network is proposed, learning to distinguish, point-by-point, desired sub-patterns from background content within a time series. The proposed framework was validated in two use-cases: electrocardiogram (ECG) and inertial sensor-based human activity (IMU) signals. It outperformed two conventional matching techniques, being capable of notably detecting the targeted cycles even in noise-corrupted or extremely distorted signals, without using any reference template nor hand-coded similarity scores. Concerning anomaly detection, the proposed unsupervised framework uses the reconstruction ability of Variational Autoencoders and a local similarity score to identify non-labeled abnormalities. The proposal was validated in two public ECG datasets (MITBIH Arrhythmia and ECG5000), performing cardiac arrhythmia identification. Results indicated competitiveness relative to recent techniques, achieving detection AUC scores of 98.84% (ECG5000) and 93.32% (MIT-BIH Arrhythmia).Dados de séries temporais tornaram-se largamente aplicados pela comunidade científica nas últimas decadas após um aumento massivo da sua disponibilidade. Contudo, este aumento exigiu uma melhoria das atuais técnicas de análise que, no domínio clínico, auxiliaria os especialistas na avaliação da condição dos seus pacientes. Um dos principais tipos de análise em séries temporais é o reconhecimento de padrões (segmentação e classificação). Métodos tradicionais assentam, tipicamente, em técnicas de correspondência em subsequências, fazendo uso de um padrão de referência e uma métrica de similaridade para procurar por subsequências similares ao longo de séries temporais. Todavia, dados do mundo real são ruidosos e variáveis (morfologicamente), tornando uma correspondência exata baseada num padrão de referência uma abordagem rudimentar. Pretendendo aumentar a flexibilidade da análise de séries temporais e generalizar tarefas de procura de padrões entre domínios, esta dissertação propõe duas abordagens baseadas em Deep Learning para solucionar problemas de segmentação de padrões e deteção de anomalias. Acerca da segmentação de padrões, a rede neuronal de Convolução/Deconvolução proposta aprende a distinguir, ponto a ponto, sub-padrões pretendidos de conteúdo de fundo numa série temporal. O modelo proposto foi validado em dois casos de uso: sinais eletrocardiográficos (ECG) e de sensores inerciais em atividade humana (IMU). Este superou duas técnicas convencionais, sendo capaz de detetar os ciclos-alvo notavelmente, mesmo em sinais corrompidos por ruído ou extremamente distorcidos, sem o uso de nenhum padrão de referência nem métricas de similaridade codificadas manualmente. A respeito da deteção de anomalias, a técnica não supervisionada proposta usa a capacidade de reconstrução dos Variational Autoencoders e uma métrica de similaridade local para identificar anomalias desconhecidas. A proposta foi validada na identificação de arritmias cardíacas em duas bases de dados públicas de ECG (MIT-BIH Arrhythmia e ECG5000). Os resultados revelam competitividade face a técnicas recentes, alcançando métricas AUC de deteção de 93.32% (MIT-BIH Arrhythmia) e 98.84% (ECG5000)

Personalizing Simulations of the Human Atria : Intracardiac Measurements, Tissue Conductivities, and Cellular Electrophysiology

This work addresses major challenges of heart model personalization. Analysis techniques for clinical intracardiac electrograms determine wave direction and conduction velocity from single beats. Electrophysiological measurements are simulated to validate the models. Uncertainties in tissue conductivities impact on simulated ECGs. A minimal model of cardiac myocytes is adapted to the atria. This makes personalized cardiac models a promising technique to improve treatment of atrial arrhythmias

Multiscale Modeling of the Ventricles: From Cellular Electrophysiology to Body Surface Electrocardiograms

This work is focused on different aspects within the loop of multiscale modeling: On the cellular level, effects of adrenergic regulation and the Long-QT syndrome have been investigated. On the organ level, a model for the excitation conduction system was developed and the role of electrophysiological heterogeneities was analyzed. On the torso level a dynamic model of a deforming heart was created and the effects of tissue conductivities on the solution of the forward problem were evaluated

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

Sensing and Signal Processing in Smart Healthcare

In the last decade, we have witnessed the rapid development of electronic technologies that are transforming our daily lives. Such technologies are often integrated with various sensors that facilitate the collection of human motion and physiological data and are equipped with wireless communication modules such as Bluetooth, radio frequency identification, and near-field communication. In smart healthcare applications, designing ergonomic and intuitive human–computer interfaces is crucial because a system that is not easy to use will create a huge obstacle to adoption and may significantly reduce the efficacy of the solution. Signal and data processing is another important consideration in smart healthcare applications because it must ensure high accuracy with a high level of confidence in order for the applications to be useful for clinicians in making diagnosis and treatment decisions. This Special Issue is a collection of 10 articles selected from a total of 26 contributions. These contributions span the areas of signal processing and smart healthcare systems mostly contributed by authors from Europe, including Italy, Spain, France, Portugal, Romania, Sweden, and Netherlands. Authors from China, Korea, Taiwan, Indonesia, and Ecuador are also included

Development of whole-heart myocardial perfusion magnetic resonance imaging

Myocardial perfusion imaging is of huge importance for the detection of coronary artery disease (CAD), one of the leading causes of morbidity and mortality worldwide, as it can provide non-invasive detection at the early stages of the disease. Magnetic resonance imaging (MRI) can assess myocardial perfusion by capturing the rst-pass perfusion (FPP) of a gadolinium-based contrast agent (GBCA), which is now a well-established technique and compares well with other modalities. However, current MRI methods are restricted by their limited coverage of the left ventricle. Interest has therefore grown in 3D volumetric \whole-heart" FPP by MRI, although many challenges currently limit this. For this thesis, myocardial perfusion assessment in general, and 3D whole-heart FPP in particular, were reviewed in depth, alongside MRI techniques important for achieving 3D FPP. From this, a 3D `stack-of-stars' (SOS) FPP sequence was developed with the aim of addressing some current limitations. These included the breath-hold requirement during GBCA rst-pass, long 3D shot durations corrupted by cardiac motion, and a propensity for artefacts in FPP. Parallel imaging and compressed sensing were investigated for accelerating whole-heart FPP, with modi cations presented to potentially improve robustness to free-breathing. Novel sequences were developed that were capable of individually improving some current sequence limits, including spatial resolution and signal-to-noise ratio, although with some sacri ces. A nal 3D SOS FPP technique was developed and tested at stress during free-breathing examinations of CAD patients and healthy volunteers. This enabled the rst known detection of an inducible perfusion defect with a free-breathing, compressed sensing, 3D FPP sequence; however, further investigation into the diagnostic performance is required. Simulations were performed to analyse potential artefacts in 3D FPP, as well as to examine ways towards further optimisation of 3D SOS FPP. The nal chapter discusses some limitations of the work and proposes opportunities for further investigation.Open Acces