Algorithms for time series clustering applied to biomedical signals

Thesis submitted in the fulfillment of the requirements for the Degree of Master in Biomedical Engineering

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 Engineering

Tool for biomedical signals processing

[ES]Educational tool for training biomedical engineers in the biomedical signals processing field has been developed. It is software for simulation and study of the results obtained in biomedical signals when different signals processing techniques are applied. The tool has been implemented on a graphical user interface to facilitate the use.

Classification of Biomedical Signals using the Dynamics of the False Nearest Neighbours (DFNN) Algorithm

* This study was supported in part by the Natural Sciences and Engineering Research Council of Canada, and by the Gastrointestinal Motility Laboratory (University of Alberta Hospitals) in Edmonton, Alberta, Canada.

Statistical methods for biomedical signal analysis and processing

Statistical modelling and statistical learning theory are two powerful analytical frameworks for analyzing signals and developing efficient processing and classification algorithms. In this thesis, these frameworks are applied for modelling and processing biomedical signals in two different contexts: ultrasound medical imaging systems and primate neural activity analysis and modelling. In the context of ultrasound medical imaging, two main applications are explored: deconvolution of signals measured from a ultrasonic transducer and automatic image segmentation and classification of prostate ultrasound scans. In the former application a stochastic model of the radio frequency signal measured from a ultrasonic transducer is derived. This model is then employed for developing in a statistical framework a regularized deconvolution procedure, for enhancing signal resolution. In the latter application, different statistical models are used to characterize images of prostate tissues, extracting different features. These features are then uses to segment the images in region of interests by means of an automatic procedure based on a statistical model of the extracted features. Finally, machine learning techniques are used for automatic classification of the different region of interests. In the context of neural activity signals, an example of bio-inspired dynamical network was developed to help in studies of motor-related processes in the brain of primate monkeys. The presented model aims to mimic the abstract functionality of a cell population in 7a parietal region of primate monkeys, during the execution of learned behavioural tasks.

ALS-Sence: sistema de aquisição e processamento de electromiogramas para pacientes com ALS

Dissertação para obtenção do grau de Mestre em Engenharia de Eletrónica e Computadores

SOA-DB: uma arquitetura embarcada orientada a serviço para acesso a dispositivos biomédicos

The great diversity in the architecture of biomedical devices, coupled with their different communication protocols, has hindered the implementation of systems that need to make access to these devices. Given these differences, the need arises to provide access to such a transparent manner. In this sense, this paper proposes an embedded architecture, service-oriented, for access to biomedical devices, as a way to abstract the mechanism for writing and reading data on these devices, thereby contributing to the increase in quality and productivity of biomedical systems so as to enable that, the focus of the development team of biomedical software, is almost exclusively directed to its functional requirements

Desenvolvimento, otimização e caracterização física de um novo sistema multi-sensores de Biosusceptometria de Corrente Alternada (BAC) para obtenção de imagens biomagnéticas

A novel AC Biosusceptometry (ACB) system with thirteen sensors it was implemented and characterized in vitro using magnetic phantoms. The system presenting coils in a coaxial arrangement with one pair of excitation coil outside and thirteen pairs of detection coils inside. A first-order gradiometric configuration was utilized for optimal detection of magnetic signals. Several physical parameters such as baseline, number of turns, excitation field and diameters were studied for improvement of the signal/noise ratio. This system exhibits an enhanced sensitivity and spatial resolution, due to the higher density of sensors/area. In the future those characteristics will turn possible to obtain images of magnetic marker or tracer in the gastrointestinal tract focusing on physiological and pharmaceutical studies. ACB emerged due to its interesting nature, noninvasiveness and low cost to investigate gastrointestinal parameters and this system can contribute for more accurate interpretation of biomedical signals and images

Non uniform embedding based on relevance analysis with reduced computational complexity: application to the detection of pathologies from biosignal recordings

Nonlinear analysis tools for studying and characterizing the dynamics of physiological signals have gained popularity, mainly because tracking sudden alterations of the inherent complexity of biological processes might be an indicator of altered physiological states. Typically, in order to perform an analysis with such tools, the physiological variables that describe the biological process under study are used to reconstruct the underlying dynamics of the biological processes. For that goal, a procedure called time-delay or uniform embedding is usually employed. Nonetheless, there is evidence of its inability for dealing with non-stationary signals, as those recorded from many physiological processes. To handle with such a drawback, this paper evaluates the utility of non-conventional time series reconstruction procedures based on non uniform embedding, applying them to automatic pattern recognition tasks. The paper compares a state of the art non uniform approach with a novel scheme which fuses embedding and feature selection at once, searching for better reconstructions of the dynamics of the system. Moreover, results are also compared with two classic uniform embedding techniques. Thus, the goal is comparing uniform and non uniform reconstruction techniques, including the one proposed in this work, for pattern recognition in biomedical signal processing tasks. Once the state space is reconstructed, the scheme followed characterizes with three classic nonlinear dynamic features (Largest Lyapunov Exponent, Correlation Dimension and Recurrence Period Density Entropy), while classification is carried out by means of a simple k-nn classifier. In order to test its generalization capabilities, the approach was tested with three different physiological databases (Speech Pathologies, Epilepsy and Heart Murmurs). In terms of the accuracy obtained to automatically detect the presence of pathologies, and for the three types of biosignals analyzed, the non uniform techniques used in this work lightly outperformed the results obtained using the uniform methods, suggesting their usefulness to characterize non-stationary biomedical signals in pattern recognition applications. On the other hand, in view of the results obtained and its low computational load, the proposed technique suggests its applicability for the applications under study.

A wearable device for recording of biopotentials and body movements

Long term recording of biomedical signals such as ECG, EMG, respiration and other information (e.g. body motion) can improve diagnosis and potentially monitor the evolution of many widespread diseases. However, long term monitoring requires specific solutions, portable and wearable equipment that should be particularly comfortable for patients. The key-issues of portable biomedical instrumentation are: power consumption, long-term sensor stability, comfortable wearing and wireless connectivity. In this scenario, it would be valuable to realize prototypes using available technologies to assess long-term personal monitoring and foster new ways to provide healthcare services. The aim of this work is to discuss the advantages and the drawbacks in long term monitoring of biopotentials and body movements using textile electrodes embedded in clothes. The textile electrodes were embedded into garments; tiny shirt and short were used to acquire electrocardiographic and electromyographic signals. The garment was equipped with low power electronics for signal acquisition and data wireless transmission via Bluetooth. A small, battery powered, biopotential amplifier and three-axes acceleration body monitor was realized. Patient monitor incorporates a microcontroller, analog-to-digital signal conversion at programmable sampling frequencies. The system was able to acquire and to transmit real-time signals, within 10 m range, to any Bluetooth device (including PDA or cellular phone). The electronics were embedded in the shirt resulting comfortable to wear for patients. Small size MEMS 3-axes accelerometers were also integrated. © 2011 IEEE.

Regularity analysis of nocturnal oximetry recordings to assist in the diagnosis of sleep apnoea syndrome

The relationship between sleep apnoea–hypopnoea syndrome (SAHS) severity and the regularity of nocturnal oxygen saturation (SaO2) recordings was analysed. Three different methods were proposed to quantify regularity: approximate entropy (AEn), sample entropy (SEn) and kernel entropy (KEn). A total of 240 subjects suspected of suffering from SAHS took part in the study. They were randomly divided into a training set (96 subjects) and a test set (144 subjects) for the adjustment and assessment of the proposed methods, respectively. According to the measurements provided by AEn, SEn and KEn, higher irregularity of oximetry signals is associated with SAHS-positive patients. Receiver operating characteristic (ROC) and Pearson correlation analyses showed that KEn was the most reliable predictor of SAHS. It provided an area under the ROC curve of 0.91 in two-class classification of subjects as SAHS-negative or SAHS-positive. Moreover, KEn measurements from oximetry data exhibited a linear dependence on the apnoea–hypopnoea index, as shown by a correlation coefficient of 0.87. Therefore, these measurements could be used for the development of simplified diagnostic techniques in order to reduce the demand for polysomnographies. Furthermore, KEn represents a convincing alternative to AEn and SEn for the diagnostic analysis of noisy biomedical signals.

Predictability Analysis of Voice Signals Analyzing Healthy and Pathologic Samples

Research Foundation of the State of Sao Paulo (FAPESP)

Normal Versus Pathological Voice Signals Using Wavelet Analysis and Least Squares Support-Vector Machines

State of Sao Paulo Research Foundation (FAPESP)

Noise reduction in biomedical speech signal processing based on time and frequency Kalman filtering combined with spectral subtraction

The goal of this paper is to study and propose a new technique for noise reduction used during the reconstruction of speech signals, particularly for biomedical applications. The proposed method is based on Kalman filtering in the time domain combined with spectral subtraction. Comparison with discrete Kalman filter in the frequency domain shows better performance of the proposed technique. The performance is evaluated by using the segmental signal-to-noise ratio and the Itakura-Saito`s distance. Results have shown that Kalman`s filter in time combined with spectral subtraction is more robust and efficient, improving the Itakura-Saito`s distance by up to four times. (C) 2007 Elsevier Ltd. All rights reserved.

Parallel programming in biomedical signal processing

Dissertação para obtenção do Grau de Mestre em Engenharia Biomédica