Models and Analysis of Vocal Emissions for Biomedical Applications

The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies

Tracking Rhythmicity in Biomedical Signals using Sequential Monte Carlo methods

Cyclical patterns are common in signals that originate from natural systems such as the human body and man-made machinery. Often these cyclical patterns are not perfectly periodic. In that case, the signals are called pseudo-periodic or quasi-periodic and can be modeled as a sum of time-varying sinusoids, whose frequencies, phases, and amplitudes change slowly over time. Each time-varying sinusoid represents an individual rhythmical component, called a partial, that can be characterized by three parameters: frequency, phase, and amplitude. Quasi-periodic signals often contain multiple partials that are harmonically related. In that case, the frequencies of other partials become exact integer multiples of that of the slowest partial. These signals are referred to as multi-harmonic signals. Examples of such signals are electrocardiogram (ECG), arterial blood pressure (ABP), and human voice. A Markov process is a mathematical model for a random system whose future and past states are independent conditional on the present state. Multi-harmonic signals can be modeled as a stochastic process with the Markov property. The Markovian representation of multi-harmonic signals enables us to use state-space tracking methods to continuously estimate the frequencies, phases, and amplitudes of the partials. Several research groups have proposed various signal analysis methods such as hidden Markov Models (HMM), short time Fourier transform (STFT), and Wigner-Ville distribution to solve this problem. Recently, a few groups of researchers have proposed Monte Carlo methods which estimate the posterior distribution of the fundamental frequency in multi-harmonic signals sequentially. However, multi-harmonic tracking is more challenging than single-frequency tracking, though the reason for this has not been well understood. The main objectives of this dissertation are to elucidate the fundamental obstacles to multi-harmonic tracking and to develop a reliable multi-harmonic tracker that can track cyclical patterns in multi-harmonic signals

Integration of Local Positioning System & Strapdown Inertial Navigation System for Hand-Held Tool Tracking

This research concerns the development of a smart sensory system for tracking a hand-held moving device to millimeter accuracy, for slow or nearly static applications over extended periods of time. Since different operators in different applications may use the system, the proposed design should provide the accurate position, orientation, and velocity of the object without relying on the knowledge of its operation and environment, and based purely on the motion that the object experiences. This thesis proposes the design of the integration a low-cost Local Positioning System (LPS) and a low-cost StrapDown Inertial Navigation System (SDINS) with the association of the modified EKF to determine 3D position and 3D orientation of a hand-held tool within a required accuracy. A hybrid LPS/SDINS combines and complements the best features of two different navigation systems, providing a unique solution to track and localize a moving object more precisely. SDINS provides continuous estimates of all components of a motion, but SDINS loses its accuracy over time because of inertial sensors drift and inherent noise. LPS has the advantage that it can possibly get absolute position and velocity independent of operation time; however, it is not highly robust, is computationally quite expensive, and exhibits low measurement rate. This research consists of three major parts: developing a multi-camera vision system as a reliable and cost-effective LPS, developing a SDINS for a hand-held tool, and developing a Kalman filter for sensor fusion. Developing the multi-camera vision system includes mounting the cameras around the workspace, calibrating the cameras, capturing images, applying image processing algorithms and features extraction for every single frame from each camera, and estimating the 3D position from 2D images. In this research, the specific configuration for setting up the multi-camera vision system is proposed to reduce the loss of line of sight as much as possible. The number of cameras, the position of the cameras with respect to each other, and the position and the orientation of the cameras with respect to the center of the world coordinate system are the crucial characteristics in this configuration. The proposed multi-camera vision system is implemented by employing four CCD cameras which are fixed in the navigation frame and their lenses placed on semicircle. All cameras are connected to a PC through the frame grabber, which includes four parallel video channels and is able to capture images from four cameras simultaneously. As a result of this arrangement, a wide circular field of view is initiated with less loss of line-of-sight. However, the calibration is more difficult than a monocular or stereo vision system. The calibration of the multi-camera vision system includes the precise camera modeling, single camera calibration for each camera, stereo camera calibration for each two neighboring cameras, defining a unique world coordinate system, and finding the transformation from each camera frame to the world coordinate system. Aside from the calibration procedure, digital image processing is required to be applied into the images captured by all four cameras in order to localize the tool tip. In this research, the digital image processing includes image enhancement, edge detection, boundary detection, and morphologic operations. After detecting the tool tip in each image captured by each camera, triangulation procedure and optimization algorithm are applied in order to find its 3D position with respect to the known navigation frame. In the SDINS, inertial sensors are mounted rigidly and directly to the body of the tracking object and the inertial measurements are transformed computationally to the known navigation frame. Usually, three gyros and three accelerometers, or a three-axis gyro and a three-axis accelerometer are used for implementing SDINS. The inertial sensors are typically integrated in an inertial measurement unit (IMU). IMUs commonly suffer from bias drift, scale-factor error owing to non-linearity and temperature changes, and misalignment as a result of minor manufacturing defects. Since all these errors lead to SDINS drift in position and orientation, a precise calibration procedure is required to compensate for these errors. The precision of the SDINS depends not only on the accuracy of calibration parameters but also on the common motion-dependent errors. The common motion-dependent errors refer to the errors caused by vibration, coning motion, sculling, and rotational motion. Since inertial sensors provide the full range of heading changes, turn rates, and applied forces that the object is experiencing along its movement, accurate 3D kinematics equations are developed to compensate for the common motion-dependent errors. Therefore, finding the complete knowledge of the motion and orientation of the tool tip requires significant computational complexity and challenges relating to resolution of specific forces, attitude computation, gravity compensation, and corrections for common motion-dependent errors. The Kalman filter technique is a powerful method for improving the output estimation and reducing the effect of the sensor drift. In this research, the modified EKF is proposed to reduce the error of position estimation. The proposed multi-camera vision system data with cooperation of the modified EKF assists the SDINS to deal with the drift problem. This configuration guarantees the real-time position and orientation tracking of the instrument. As a result of the proposed Kalman filter, the effect of the gravitational force in the state-space model will be removed and the error which results from inaccurate gravitational force is eliminated. In addition, the resulting position is smooth and ripple-free. The experimental results of the hybrid vision/SDINS design show that the position error of the tool tip in all directions is about one millimeter RMS. If the sampling rate of the vision system decreases from 20 fps to 5 fps, the errors are still acceptable for many applications

Nervous system differences between the sexes and across the menstrual cycle

Sex hormones have in vitro effects on the nervous system. Furthermore, the effects of estradiol and progesterone metabolites have neurologic responses on the motor system, evidenced by transcranial magnetic stimulation studies. Sex hormone effects on the nervous system may underlie some of the sex discrepancies seen in athletic performance, injury and cardiovascular events. Investigating sex differences is often complicated by hormonal oscillations across the menstrual cycle; therefore, the aims of this research were to investigate sex and menstrual cycle effects on the motor and cardiovascular systems and the interaction of these two systems. In study 1, motor unit (MU) recruitment patterns of the vastus medialis (VM) and vastus medialis oblique (VMO) were examined in males and females at five menstrual cycle phases. Initial discharge rate between the VM and VMO were different only in females. This VM/VMO discharge discrepancy was only evident in females during the ovulatory and mid luteal menstrual phases. Study 2 examined the frequency domain relationship of the VM and VMO MUs between the sexes and across the menstrual cycle. Males have 256% to 741% greater odds of having coherent MU oscillations in the common drive band than females, indicating a greater common rate modulation. Further evidence indicated MU pairs from the VMO and VM/VMO have 228% and 212% greater odds of having beta band oscillations than the VM, indicating control of those muscle groups has a common cortical modulation. Study 3 looked at changes in the autonomic nervous system across the menstrual cycle via heart rate variability analysis. Heart rate variability decreases from the follicular to the luteal phases of the menstrual cycle, indicating a decrease in parasympathetic control. In study 4, the time and frequency domain relationship between electrocardiogram and MU discharge timing was examined between the sexes and across the menstrual cycle. The time domain relationship indicated that both males and females have MU time lag centered between 20-25 milliseconds, with an apparent modulation of this relationship across the menstrual cycle. The findings from this series of studies indicate that there are differences between the sexes which are often modified by the menstrual cycle in females.Kinesiology and Health Educatio

Modelo biomecânico de punho para a simulação computacional de tremores patológicos em malha fechada utilizando Controle H∞

In the last decades, the world’s been through an expressive raise in life expectancy and consequently increasing age-related diseases such as pathological tremor. Assistive technology development may be benefited by individualized mathematical models capable of reproducing affected limb behavior. This work aims to develop a biomechanical model of pathological tremor, using realistic musculoskeletal representations to serve as a plant for testing suppression strategies. In order, a MATLAB forward dynamics interface was implemented allowing the use of modeling and closed-loop control techniques to reproduce, in a statistical manner, the pathological tremor observed on an individual. As a result, a two degree-of-freedom biomechanical model of pathological tremor was obtained, which successfully reproduces tremor kinematics as well as tremor frequency and its variability with K-L divergence of 1 × 10−7.O mundo, nas últimas décadas, passa por um aumento expressivo da expectativa de vida, com um crescimento na incidência de males associados, como os tremores patológicos. O desenvolvimento de tecnologias assistias para a supressão dos tremores pode ser beneficiado pelo desenvolvimento de modelos matemáticos individualizados capazes de reproduzir o comportamento do membro afetado. Este trabalho tem como objetivo principal desenvolver um modelo biomecânico de tremor baseado em representações realistas da dinâmica musculoesquelética, levando-se em consideração os mecanismos centrais de produção de tremores patológicos e que possa servir como base para testes de estratégias de supressão. Para isso, implementou-se uma interface de simulação de dinâmica direta em ambiente MATLAB permitindo a utilização de técnicas de modelagem e controle de malha fechada para reproduzir estatisticamente os tremores patológicos observados em um indivíduo. Desta implementação, resultou um modelo biomecânico de tremor patológico de dois graus de liberdade, com representação satisfatória das variáveis cinemáticas comparadas a valores experimentais e que representa tanto a frequência do tremor quanto sua variabilidade com divergência K-L de 1 × 10−7

Artefacts Detection in EEG Signals

Chapter 11 demonstrates the potential of artefacts detection approach in electro- encephalography, using the Hampel filter to correct different types of artefacts. Also, a complete state-of-the-art is introduced along with a recommended bibliography to research these topics

Neural activity inspired asymmetric basis function TV-NARX model for the identification of time-varying dynamic systems

Inspired by the unique neuronal activities, a new time-varying nonlinear autoregressive with exogenous input (TV-NARX) model is proposed for modelling nonstationary processes. The NARX nonlinear process mimics the action potential initiation and the time-varying parameters are approximated with a series of postsynaptic current like asymmetric basis functions to mimic the ion channels of the inter-neuron propagation. In the model, the time-varying parameters of the process terms are sparsely represented as the superposition of a series of asymmetric alpha basis functions in an over-complete frame. Combining the alpha basis functions with the model process terms, the system identification of the TV-NARX model from observed input and output can equivalently be treated as the system identification of a corresponding time-invariant system. The locally regularised orthogonal forward regression (LROFR) algorithm is then employed to detect the sparse model structure and estimate the associated coefficients. The excellent performance in both numerical studies and modelling of real physiological signals showed that the TV-NARX model with asymmetric basis function is more powerful and efficient in tracking both smooth trends and capturing the abrupt changes in the time-varying parameters than its symmetric counterparts

PT-Net: A Multi-Model Machine Learning Approach for Smarter Next-Generation Wearable Tremor Suppression Devices for Parkinson\u27s Disease Tremor

According to the World Health Organization (WHO), Parkinson\u27s Disease (PD) is the second most common neurodegenerative condition that can cause tremors and other motor and non motor related symptoms. Medication and deep brain stimulation (DBS) are often used to treat tremor; however, medication is not always effective and has adverse effects, and DBS is invasive and carries a significant risk of complications. Wearable tremor suppression devices (WTSDs) have been proposed as a possible alternative, but their effectiveness is limited by the tremor models they use, which introduce a phase delay that decreases the performance of the devices. Additionally, the availability of tremor datasets is limited, which prevents the rapid advancement of these devices. To address the challenges facing the WTSDs, PD tremor data were collected at the Wearable Biomechatronics Laboratory (WearMe Lab) to develop methods and data-driven models to improve the performance of WTSDs in managing tremor, and potentially to be integrated with the wearable tremor suppression glove that is being developed at the WearMe Lab. A predictive model was introduced and showed improved motion estimation with an average estimation accuracy of 99.2%. The model was also able to predict motion with multiple steps ahead, negating the phase delay introduced by previous models and achieving prediction accuracies of 97%, 94%, 92%, and 90\% for predicting voluntary motion 10, 20, 50, and 100 steps ahead, respectively. Tremor and task classification models were also developed, with mean classification accuracies of 91.2% and 91.1%, respectively. These models can be used to fine-tune the parameters of existing estimators based on the type of tremor and task, increasing their suppression capabilities. To address the absence of a mathematical model for generating tremor data and limited access to existing PD tremor datasets, an open-source generative model was developed to produce data with similar characteristics, distribution, and patterns to real data. The reliability of the generated data was evaluated using four different methods, showing that the generative model can produce data with similar distribution, patterns, and characteristics to real data. The development of data-driven models and methods to improve the performance of wearable tremor suppression devices for Parkinson\u27s disease can potentially offer a noninvasive and effective alternative to medication and deep brain stimulation. The proposed predictive model, classification model, and the open-source generative model provide a promising framework for the advancement of wearable technology for tremor suppression, potentially leading to a significant improvement in the quality of life for individuals with Parkinson\u27s disease

REAL-TIME ADAPTIVE PULSE COMPRESSION ON RECONFIGURABLE, SYSTEM-ON-CHIP (SOC) PLATFORMS

New radar applications need to perform complex algorithms and process a large quantity of data to generate useful information for the users. This situation has motivated the search for better processing solutions that include low-power high-performance processors, efficient algorithms, and high-speed interfaces. In this work, hardware implementation of adaptive pulse compression algorithms for real-time transceiver optimization is presented, and is based on a System-on-Chip architecture for reconfigurable hardware devices. This study also evaluates the performance of dedicated coprocessors as hardware accelerator units to speed up and improve the computation of computing-intensive tasks such matrix multiplication and matrix inversion, which are essential units to solve the covariance matrix. The tradeoffs between latency and hardware utilization are also presented. Moreover, the system architecture takes advantage of the embedded processor, which is interconnected with the logic resources through high-performance buses, to perform floating-point operations, control the processing blocks, and communicate with an external PC through a customized software interface. The overall system functionality is demonstrated and tested for real-time operations using a Ku-band testbed together with a low-cost channel emulator for different types of waveforms