Time series morphological analysis applied to biomedical signals events detection

Dissertation submitted in the fufillment of the requirements for the Degree of Master in Biomedical EngineeringAutomated techniques for biosignal data acquisition and analysis have become increasingly powerful, particularly at the Biomedical Engineering research field. Nevertheless, it is verified the need to improve tools for signal pattern recognition and classification systems, in which the detection of specific events and the automatic signal segmentation are preliminary processing steps. The present dissertation introduces a signal-independent algorithm, which detects significant events in a biosignal. From a time series morphological analysis, the algorithm computes the instants when the most significant standard deviation discontinuities occur, segmenting the signal. An iterative optimization step is then applied. This assures that a minimal error is achieved when modeling these segments with polynomial regressions. The adjustment of a scale factor gives different detail levels of events detection. An accurate and objective algorithm performance evaluation procedure was designed. When applied on a set of synthetic signals, with known and quantitatively predefined events, an overall mean error of 20 samples between the detected and the actual events showed the high accuracy of the proposed algorithm. Its ability to perform the detection of signal activation onsets and transient waveshapes was also assessed, resulting in higher reliability than signal-specific standard methods. Some case studies, with signal processing requirements for which the developed algorithm can be suitably applied, were approached. The algorithm implementation in real-time, as part of an application developed during this research work, is also reported. The proposed algorithm detects significant signal events with accuracy and significant noise immunity. Its versatile design allows the application in different signals without previous knowledge on their statistical properties or specific preprocessing steps. It also brings added objectivity when compared with the exhaustive and time-consuming examiner analysis. The tool introduced in this dissertation represents a relevant contribution in events detection, a particularly important issue within the wide digital biosignal processing research field

User friendly knowledge acquisition system for medical devices actuation

Dissertação para obtenção do Grau de Mestre em Engenharia BiomédicaInternet provides a new environment to develop a variety of applications. Hence, large amounts of data, increasing every day, are stored and transferred through the internet. These data are normally weakly structured making information disperse, uncorrelated, non-transparent and difficult to access and share. Semantic Web, proposed by theWorldWideWeb Consortium (W3C), addresses this problem by promoting semantic structured data, like ontologies, enabling machines to perform more work involved in finding, combining, and acting upon information on theWeb. Pursuing this vision, a Knowledge Acquisition System (KAS) was created, written in JavaScript using JavaScript Object Notation (JSON) as the data structure and JSON Schema to define that structure. It grants new ways to acquire and store knowledge semantically structured and human readable. Plus, structuring data with a Schema generates a software robust and error – free. A novel Human Computer Interaction (HCI) framework was constructed employing this KAS, allowing the end user to configure and control medical devices. To demonstrate the potential of this tool, we present the configuration and control of an electrostimulator. Nowadays, most of the software for Electrostimulation is made with specific purposes, and in some cases they have complicated user interfaces and large, bulky designs that deter usability and acceptability. The HCI concedes the opportunity to configure and control an electrostimulator that surpasses the specific use of several electrostimulator software. In the configuration the user is able to compile different types of electrical impulses (modes) in a temporal session, automating the control, making it simple and user-friendly

Desenvolvimento de uma ferramenta de avaliação da performance de atletas

Dissertação para obtenção do Grau de Mestre em Engenharia BiomédicaDevido à crescente necessidade de obtenção de resultados por parte dos atletas profissionais e não-profissionais, aumentou também a necessidade de acompanhamento do atleta para maximizar a performance e a condição física sem por em causa a saúde do mesmo. Esta avaliação deverá ser o mais completa possível baseada na aquisição dos parˆametros fisiológicos relevantes em cada actividade física. Na presente dissertação desenvolveu-se uma nova ferramenta de avaliação da intensidade da performance do atleta, PLUX Real-Time Sports Evaluation, com a finalidade de responder a actuais necessidades de profissionais do Desporto. As principais características desta ferramenta são a capacidade de aquisição, visualização, processamento e gravação de sinais fisiológicos com a respectiva avaliação da performance do atleta em tempo-real. Esta avaliação pode ser efectuada com base na frequência cardíaca e/ou no dispêndio energético. Para garantir o correcto funcionamento dos algoritmos que efectuam a avaliação da performance, foram efectuados testes de validação. Desta forma, a validação incidiu sobre o algoritmo de detecção dos picos-R em tempo-real, o algoritmo de intensidade com base nos valores de picos-R obtidos e o algoritmo para a estimativa do valor de METs em tempo-real. Verificou-se o correcto funcionamento dos algoritmos de METs, de intensidade baseada nos valores de picos-R e de detecção de picos-R com uma eficácia de cerca 95%. Desta forma, pode-se concluir que o PLUX Real-Time Sports Evaluation possibilita uma avaliação através de vários parâmetros da intensidade da performance do atleta, constituindo uma ferramenta versátil e capaz de ser utilizada por atletas profissionais e não-profissionais

Algorithms for time series clustering applied to biomedical signals

Thesis submitted in the fulfillment of the requirements for the Degree of Master in Biomedical EngineeringThe increasing number of biomedical systems and applications for human body understanding creates a need for information extraction tools to use in biosignals. It’s important to comprehend the changes in the biosignal’s morphology over time, as they often contain critical information on the condition of the subject or the status of the experiment. The creation of tools that automatically analyze and extract relevant attributes from biosignals, providing important information to the user, has a significant value in the biosignal’s processing field. The present dissertation introduces new algorithms for time series clustering, where we are able to separate and organize unlabeled data into different groups whose signals are similar to each other. Signal processing algorithms were developed for the detection of a meanwave, which represents the signal’s morphology and behavior. The algorithm designed computes the meanwave by separating and averaging all cycles of a cyclic continuous signal. To increase the quality of information given by the meanwave, a set of wave-alignment techniques was also developed and its relevance was evaluated in a real database. To evaluate our algorithm’s applicability in time series clustering, a distance metric created with the information of the automatic meanwave was designed and its measurements were given as input to a K-Means clustering algorithm. With that purpose, we collected a series of data with two different modes in it. The produced algorithm successfully separates two modes in the collected data with 99.3% of efficiency. The results of this clustering procedure were compared to a mechanism widely used in this area, which models the data and uses the distance between its cepstral coefficients to measure the similarity between the time series.The algorithms were also validated in different study projects. These projects show the variety of contexts in which our algorithms have high applicability and are suitable answers to overcome the problems of exhaustive signal analysis and expert intervention. The algorithms produced are signal-independent, and therefore can be applied to any type of signal providing it is a cyclic signal. The fact that this approach doesn’t require any prior information and the preliminary good performance make these algorithms powerful tools for biosignals analysis and classification

Human activity recognition for an intelligent knee orthosis

Dissertação para obtenção do Grau de Mestre em Engenharia BiomédicaActivity recognition with body-worn sensors is a large and growing field of research. In this thesis we evaluate the possibility to recognize human activities based on data from biosignal sensors solely placed on or under an existing passive knee orthosis, which will produce the needed information to integrate sensors into the orthosis in the future. The development of active orthotic knee devices will allow population to ambulate in a more natural, efficient and less painful manner than they might with a traditional orthosis. Thus, the term ’active orthosis’ refers to a device intended to increase the ambulatory ability of a person suffering from a knee pathology by applying forces to correct the position only when necessary and thereby make usable over longer periods of time. The contribution of this work is the evaluation of the ability to recognize activities with these restrictions on sensor placement as well as providing a proof-of-concept for the development of an activity recognition system for an intelligent orthosis. We use accelerometers and a goniometer placed on the orthosis and Electromyography (EMG) sensors placed on the skin under the orthosis to measure motion and muscle activity respectively. We segment signals in motion primitives semi-automatically and apply Hidden-Markov-Models (HMM) to classify the isolated motion primitives. We discriminate between seven activities like for example walking stairs up and ascend a hill. In a user study with six participants, we evaluate the systems performance for each of the different biosignal modalities alone as well as the combinations of them. For the best performing combination, we reach an average person-dependent accuracy of 98% and a person-independent accuracy of 79%

Fatigue Evaluation through Machine Learning and a Global Fatigue Descriptor

Research in physiology and sports science has shown that fatigue, a complex psychophysiological phenomenon, has a relevant impact in performance and in the correct functioning of our motricity system, potentially being a cause of damage to the human organism. Fatigue can be seen as a subjective or objective phenomenon. Subjective fatigue corresponds to a mental and cognitive event, while fatigue referred as objective is a physical phenomenon. Despite the fact that subjective fatigue is often undervalued, only a physically and mentally healthy athlete is able to achieve top performance in a discipline. )erefore, we argue that physical training programs should address the preventive assessment of both subjective and objective fatigue mechanisms in order to minimize the risk of injuries. In this context, our paper presents a machine-learning system capable of extracting individual fatigue descriptors (IFDs) from electromyographic (EMG) and heart rate variability (HRV) measurements. Our novel approach, using two types of biosignals so that a global (mental and physical) fatigue assessment is taken into account, reflects the onset of fatigue by implementing a combination of a dimensionless (0-1) global fatigue descriptor (GFD) and a support vector machine (SVM) classifier. )e system, based on 9 main combined features, achieves fatigue regime classification performances of 0.82 ± 0.24, ensuring a successful preventive assessment when dangerous fatigue levels are reached. Training data were acquired in a constant work rate test (executed by 14 subjects using a cycloergometry device), where the variable under study (fatigue) gradually increased until the volunteer reached an objective exhaustion state

Modulation of electrical stimulation applied to human physiology and clinical diagnostic

The use, manipulation and application of electrical currents, as a controlled interference mechanism in the human body system, is currently a strong source of motivation to researchers in areas such as clinical, sports, neuroscience, amongst others. In electrical stimulation (ES), the current applied to tissue is traditionally controlled concerning stimulation amplitude, frequency and pulse-width. The main drawbacks of the transcutaneous ES are the rapid fatigue induction and the high discomfort induced by the non-selective activation of nervous fibers. There are, however, electrophysiological parameters whose response, like the response to different stimulation waveforms, polarity or a personalized charge control, is still unknown. The study of the following questions is of great importance: What is the physiological effect of the electric pulse parametrization concerning charge, waveform and polarity? Does the effect change with the clinical condition of the subjects? The parametrization influence on muscle recruitment can retard fatigue onset? Can parametrization enable fiber selectivity, optimizing the motor fibers recruitment rather than the nervous fibers, reducing contraction discomfort? Current hardware solutions lack flexibility at the level of stimulation control and physiological response assessment. To answer these questions, a miniaturized, portable and wireless controlled device with ES functions and full integration with a generic biosignals acquisition platform has been created. Hardware was also developed to provide complete freedom for controlling the applied current with respect to the waveform, polarity, frequency, amplitude, pulse-width and duration. The impact of the methodologies developed is successfully applied and evaluated in the contexts of fundamental electrophysiology, psycho-motor rehabilitation and neuromuscular disorders diagnosis. This PhD project was carried out in the Physics Department of Faculty of Sciences and Technology (FCT-UNL), in straight collaboration with PLUX - Wireless Biosignals S.A. company and co-funded by the Foundation for Science and Technology.Fundação para a Ciência e Tecnologia (FCT); PLUX - Wireless Biosignals, S.A.; FCT-UNL- CEFITE

Algorithms for information extraction and signal annotation on long-term biosignals using clustering techniques

Dissertação para obtenção do Grau de Mestre em Engenharia BiomédicaOne of the biggest challenges when analysing data is to extract information from it, especially if we dealing with very large sized data, which brings a new set of barriers to be overcome. The extracted information can be used to aid physicians in their diagnosis since biosignals often carry vital information on the subjects. In this research work, we present a signal-independent algorithm with two main goals: perform events detection in biosignals and, with those events, extract information using a set of distance measures which will be used as input to a parallel version of the k-means clustering algorithm. The first goal is achieved by using two different approaches. Events can be found based on peaks detection through an adaptive threshold defined as the signal’s root mean square (RMS) or by morphological analysis through the computation of the signal’s meanwave. The final goal is achieved by dividing the distance measures into n parts and by performing k-means individually. In order to improve speed performance, parallel computing techniques were applied. For this study, a set of different types of signals was acquired and annotated by our algorithm. By visual inspection, the L1 and L2 Minkowski distances returned an output that allowed clustering signals’ cycles with an efficiency of 97:5% and 97:3%, respectively. Using the meanwave distance, our algorithm achieved an accuracy of 97:4%. For the downloaded ECGs from the Physionet databases, the developed algorithm detected 638 out of 644 manually annotated events provided by physicians. The fact that this algorithm can be applied to long-term raw biosignals and without requiring any prior information about them makes it an important contribution in biosignals’ information extraction and annotation

An ambient assisted living solution for mobile environments

An Ambient Assisted Living (AAL) mobile health application solution with biofeedback based on body sensors is very useful to perform a data collection for diagnosis in patients whose clinical conditions are not favourable. This system allows comfort, mobility, and efficiency in all the process of data collection providing more confidence and operability. A physical fall may be considered something natural in the life span of a human being from birth to death. In a perfect scenario it would be possible to predict when a fall will occur in order to avoid it. Falls represent a high risk for senior people health. Those falls can cause fractures or injuries causing great dependence and debilitation to the elderly and even death in extreme cases. Falls can be detected by the accelerometer included in most of the available mobile phones or portable digital assistants (PDAs). To reverse this tendency, it can be obtained more accurate data for patients monitoring from the body sensors attached to the human body (such as, electrocardiogram (ECG), electromyography (EMG), blood volume pulse (BVP), electro dermal activity (EDA), and galvanic skin response (GSR)). Then, this dissertation reviews the related literature on this topic and introduces a mobile solution for falls prevention, detection, and biofeedback monitoring. The proposed system collects sensed data that is sent to a smartphone or tablet through Bluetooth. Mobile devices are used to process and display information graphically to users. The falls prevention system uses collected data from sensors in order to control and advice the patient or even to give instructions to treat an abnormal condition to reduce the falls risk. In cases of symptoms that last more time it can even detect a possible disease. The signal processing algorithms plays a key role in the fall prevention system. These algorithms in real time, through the capture of biofeedback data, are needed to extract relevant information from the signals detected to warn the patient. Monitoring and processing data from sensors is realized by a smartphone or tablet that will send warnings to users. All the process is performed in real time. These mobile devices are also used as a gateway to send the collected data to a Web service, which subsequently allows data storage and consultation. The proposed system is evaluated, demonstrated, and validated through a prototype and it is ready for use

EMG Signal Processing in Amateur and Professional Sports with Performance Evaluation and Injury Prevention

Physical activity is a constant in life, prolonging since the primordial times until now as an intrinsic element of human condition, though his character have suffered a transmutation, going from a need, by the predatory nature of the human being, for an option in escaping sedentary habits of contemporary society. Despite the enormous benefits of sports practice, there are also some negative consequences associated, namely the emergence of muscular injuries provided by the installation of fatigue, due to an overload on time or in the intensity of training. The consequences of an injury are drastic, conditioning the quotidian of the injured and carrying high costs for the health system, establishing this problem as the starting point of the present work. Although investigations on this subject have recently appeared, yet is not common to find commercial solutions for evaluating fatigue and with the capability of warning the user about the risk of injury. In order to avoid the fatigue consequences, is proposed the implementation of a computational system for physiological signal processing - Electromyographic (EMG) and Electrocardiographic (ECG) - extracting multiple indexes with informative potential at fatigue level. There is provided an automatic evaluation of the state of fatigue assured by the definition of a Global Fatigue Index that synthesises information from distinct individual fatigue indexes and implementation of a Classification System, with the capability of giving to the user the indication if the physical activity is originating the approximation or deviation from fatigue state. The computer system was built for a future integration as a plugin on a signal acquisition software. This framework is a specialized tool for acquiring and processing of the physiological signals collected in equipments such as bitalino and biosignalsplux, being directed to the practice of indoor cycling