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

A review of automated sleep disorder detection

Automated sleep disorder detection is challenging because physiological symptoms can vary widely. These variations make it difficult to create effective sleep disorder detection models which support hu-man experts during diagnosis and treatment monitoring. From 2010 to 2021, authors of 95 scientific papers have taken up the challenge of automating sleep disorder detection. This paper provides an expert review of this work. We investigated whether digital technology and Artificial Intelligence (AI) can provide automated diagnosis support for sleep disorders. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines during the content discovery phase. We compared the performance of proposed sleep disorder detection methods, involving differ-ent datasets or signals. During the review, we found eight sleep disorders, of which sleep apnea and insomnia were the most studied. These disorders can be diagnosed using several kinds of biomedical signals, such as Electrocardiogram (ECG), Polysomnography (PSG), Electroencephalogram (EEG), Electromyogram (EMG), and snore sound. Subsequently, we established areas of commonality and distinctiveness. Common to all reviewed papers was that AI models were trained and tested with labelled physiological signals. Looking deeper, we discovered that 24 distinct algorithms were used for the detection task. The nature of these algorithms evolved, before 2017 only traditional Machine Learning (ML) was used. From 2018 onward, both ML and Deep Learning (DL) methods were used for sleep disorder detection. The strong emergence of DL algorithms has considerable implications for future detection systems because these algorithms demand significantly more data for training and testing when compared with ML. Based on our review results, we suggest that both type and amount of labelled data is crucial for the design of future sleep disorder detection systems because this will steer the choice of AI algorithm which establishes the desired decision support. As a guiding principle, more labelled data will help to represent the variations in symptoms. DL algorithms can extract information from these larger data quantities more effectively, therefore; we predict that the role of these algorithms will continue to expand

Deep Learning in EEG: Advance of the Last Ten-Year Critical Period

Deep learning has achieved excellent performance in a wide range of domains, especially in speech recognition and computer vision. Relatively less work has been done for EEG, but there is still significant progress attained in the last decade. Due to the lack of a comprehensive and topic widely covered survey for deep learning in EEG, we attempt to summarize recent progress to provide an overview, as well as perspectives for future developments. We first briefly mention the artifacts removal for EEG signal and then introduce deep learning models that have been utilized in EEG processing and classification. Subsequently, the applications of deep learning in EEG are reviewed by categorizing them into groups such as brain-computer interface, disease detection, and emotion recognition. They are followed by the discussion, in which the pros and cons of deep learning are presented and future directions and challenges for deep learning in EEG are proposed. We hope that this paper could serve as a summary of past work for deep learning in EEG and the beginning of further developments and achievements of EEG studies based on deep learning

Intelligent Biosignal Analysis Methods

This book describes recent efforts in improving intelligent systems for automatic biosignal analysis. It focuses on machine learning and deep learning methods used for classification of different organism states and disorders based on biomedical signals such as EEG, ECG, HRV, and others

Reconocimiento de Estados Afectivos a partir de Señales Biomédicas

Las emociones constituyen una parte fundamental de los individuos, influyendo en sucomunicación diaria, la toma de decisiones y el foco de atención. La incorporación de las emociones en la tecnología ha avanzado en losúltimos años, desde estudios exploratorios en la respuesta a los estímulos, a aplicaciones comerciales en interfaces hombre-máquina. Una de las fuentes paraidentificar estados emocionales es la respuesta fisiológica, registrada medianteseñales biomédicas. El uso de estas señales permitiría el desarrollo de dispositivos poco invasivos, como por ejemplo una pulsera, que puedan registrarseñales continuamente, en diferentes condiciones, y manteniendo la privacidad delos usuarios. Existen numerosos enfoques para el reconocimiento de afectos, condiferentes señales, técnicas de procesamiento de la señal y métodos deaprendizaje automático. Entre ellos, la combinación demúltiples señales se utilizó ampliamente para mejorar las tasas de reconocimiento,pero resulta inviable en la práctica por su invasividad. Los desafíosactuales requieren clasificadores que puedan funcionar en tiempo real, enaplicaciones interactivas, y con mayor comodidad para el usuario. En esta tesis doctoral se aborda el desafío del reconocimiento de estadosafectivos en varios aspectos. Se revisan las propiedades de cada señalfisiológica en términos de su practicidad y potencial. Se propone un método paraadaptar un clasificador a nuevos usuarios, estimando parámetros fisiológicosbasales. Luego se presentan dos métodos originales paramejorar las tasas de reconocimiento. El primero es un método supervisado basadoen mapas auto-organizativos (sSOM). Este método permite representar los espacios de características fisiológicas ymodelos emocionales, para analizar las relaciones en los datos. El otro estabasado en máquinas de aprendizaje extremo (ELM),una novedosa familia de redes neuronales artificiales que tiene gran poder degeneralización y puede entrenarse con pocos datos. Los métodos fueron evaluados y comparados con los del estadodel arte, en corpus realistas y de acceso libre. Los resultados obtenidos muestran avances en relación al estado del arte para el problema. Elmétodo de adaptación permite, a partir de pocos segundos,mejorar las tasas de reconocimiento en tiempo real, aproximando los resultados delreconocimiento que se podría hacer con posterioridad, sobre los registros completos. Utilizando una única señal de actividad cardiovascular, en particularla variabilidad del ritmo cardíaco (HRV), se lograron avances prometedores, con diferencias significativasen relación a los resultados obtenidos por los métodos del estado del arte. LasELM obtuvieron excelentes resultados y con bajo costo computacional, por lo queserían útiles para aplicaciones móviles. El sSOMlogra resultados similares, con la ventaja de proveer a la vez una herramientapara representar y analizar los espacios complejos de la fisiología y lasemociones, en una forma compacta.Fil: Bugnon, Leandro Ariel. Universidad Nacional del Litoral; Argentin

Towards an accurate sleep apnea detection based on ECG signal: The quintessential of a wise feature selection

A wise feature selection from minute-to-minute Electrocardiogram (ECG) signal is a challenging task for many reasons, but mostly because of the promise of the accurate detection of clinical disorders, such as the sleep apnea. In this study, the ECG signal was modeled in order to obtain the Heart Rate Variability (HRV) and the ECG-Derived Respiration (EDR). Selected features techniques were used for benchmark with different classifiers such as Artificial Neural Networks (ANN) and Support Vector Machine(SVM), among others. The results evidence that the best accuracy was 82.12%, with a sensitivity and specificity of 88.41% and 72.29%, respectively. In addition, experiments revealed that a wise feature selection may improve the system accuracy. Therefore, the proposed model revealed to be reliable and simpler alternative to classical solutions for the sleep apnea detection, for example the ones based on the Polysomnography.info:eu-repo/semantics/publishedVersio

A systematic comparison of deep learning methods for EEG time series analysis

Analyzing time series data like EEG or MEG is challenging due to noisy, high-dimensional, and patient-specific signals. Deep learning methods have been demonstrated to be superior in analyzing time series data compared to shallow learning methods which utilize handcrafted and often subjective features. Especially, recurrent deep neural networks (RNN) are considered suitable to analyze such continuous data. However, previous studies show that they are computationally expensive and difficult to train. In contrast, feed-forward networks (FFN) have previously mostly been considered in combination with hand-crafted and problem-specific feature extractions, such as short time Fourier and discrete wavelet transform. A sought-after are easily applicable methods that efficiently analyze raw data to remove the need for problem-specific adaptations. In this work, we systematically compare RNN and FFN topologies as well as advanced architectural concepts on multiple datasets with the same data preprocessing pipeline. We examine the behavior of those approaches to provide an update and guideline for researchers who deal with automated analysis of EEG time series data. To ensure that the results are meaningful, it is important to compare the presented approaches while keeping the same experimental setup, which to our knowledge was never done before. This paper is a first step toward a fairer comparison of different methodologies with EEG time series data. Our results indicate that a recurrent LSTM architecture with attention performs best on less complex tasks, while the temporal convolutional network (TCN) outperforms all the recurrent architectures on the most complex dataset yielding a 8.61% accuracy improvement. In general, we found the attention mechanism to substantially improve classification results of RNNs. Toward a light-weight and online learning-ready approach, we found extreme learning machines (ELM) to yield comparable results for the less complex tasks

Functional and cognitive outcomes in patients with covert cognition during acute intensive rehabilitation

Background: Disorders of consciousness (DOC) result from focal or extensive brain lesions. Patients suffering from DOC go through neurobehavioral assessments and are classified in different categories: coma, unresponsive wakefulness syndrome (UWS) (also known as vegetative state) and minimally conscious state (MCS). Recently, the broader use of technologies, such as functional neuroimaging and electroencephalography, has allowed the highlighting of preserved cognitive capacities in patients behaviourally categorized as UWS or MCS. Such condition is called cognitive motor dissociation (CMD). Objectives: 1) To investigate the consciousness/functional recovery in patients with disorders of consciousness (DOC) as well as those presenting with cognitive motor dissociation (CMD), 2) to compare the different functional outcomes to see whether those with preserved cognitive capacities differ and 3) to evaluate the patients’ clinical evolution between admission and discharge. Method: We retrospectively included 141 patients admitted to the Acute Neurorehabilitation Unit (NRA) of the University Hospital of Lausanne (CHUV, Lausanne, Switzerland) from November 2011 to August 2018 and investigated their functional outcomes at admission and discharge using 6 different outcome scales. Univariate analyses were then performed to compare the different functional outcomes. Results: Patients presenting with CMD were significantly associated with better functional outcomes and potential of improvement than the patients suffering from DOC. Conclusion: Our findings support the fact that CMD patients constitute a separate category of patients with different potential of improvement and functional outcomes than patients suffering from DOC. This reinforces the need for them to be recognized as soon as possible, as it could have a direct impact on patient care and influence life and death decisions