1,053 research outputs found

    ESTIMATION OF STRETCH REFLEX CONTRIBUTIONS OF WRIST USING SYSTEM IDENTIFICATION AND QUANTIFICATION OF TREMOR IN PARKINSON'S DISEASE PATIENTS

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    "The brain's motor control can be studied by characterizing the activity of spinal motor nuclei to brain control, expressed as motor unit activity recordable by surface electrodes". When a specific area is under consideration, the first step in investigation of the motor control system pertinent to it is the system identification of that specific body part or area. The aim of this research is to characterize the working of the brain's motor control system by carrying out system identification of the wrist joint area and quantifying tremor observed in Parkinson's disease patients. We employ the ARMAX system identification technique to gauge the intrinsic and reflexive components of wrist stiffness, in order to facilitate analysis of problems associated with Parkinson's disease. The intrinsic stiffness dynamics comprise majority of the total stiffness in the wrist joint and the reflexive stiffness dynamics contribute to the tremor characteristic commonly found in Parkinson's disease patients. The quantification of PD tremor entails using blind source separation of convolutive mixtures to obtain sources of tremor in patients suffering from movement disorders. The experimental data when treated with blind source separation reveals sources exhibiting the tremor frequency components of 3-8 Hz. System identification of stiffness dynamics and assessment of tremor can reveal the presence of additional abnormal neurological signs and early identification or diagnosis of these symptoms would be very advantageous for clinicians and will be instrumental to pave the way for better treatment of the disease

    On the limits of spectral methods for frequency estimation

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    An algorithm is presented which generates pairs of oscillatory random time series which have identical periodograms but differ in the number of oscillations. This result indicate the intrinsic limitations of spectral methods when it comes to the task of measuring frequencies. Other examples, one from medicine and one from bifurcation theory, are given, which also exhibit these limitations of spectral methods. For two methods of spectral estimation it is verified that the particular way end points are treated, which is specific to each method, is, for long enough time series, not relevant for the main result.Comment: 18 pages, 6 figures (Referee did not like the previous title. Many other changes

    Machine Learning in Tremor Analysis: Critique and Directions

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    Tremor is the most frequent human movement disorder, and its diagnosis is based on clinical assessment. Yet finding the accurate clinical diagnosis is not always straightforward. Fine-tuning of clinical diagnostic criteria over the past few decades, as well as device-based qualitative analysis, has resulted in incremental improvements to diagnostic accuracy. Accelerometric assessments are commonplace, enabling clinicians to capture high-resolution oscillatory properties of tremor, which recently have been the focus of various machine-learning (ML) studies. In this context, the application of ML models to accelerometric recordings provides the potential for less-biased classification and quantification of tremor disorders. However, if implemented incorrectly, ML can result in spurious or nongeneralizable results and misguided conclusions. This work summarizes and highlights recent developments in ML tools for tremor research, with a focus on supervised ML. We aim to highlight the opportunities and limitations of such approaches and provide future directions while simultaneously guiding the reader through the process of applying ML to analyze tremor data. We identify the need for the movement disorder community to take a more proactive role in the application of these novel analytical technologies, which so far have been predominantly pursued by the engineering and data analysis field. Ultimately, big-data approaches offer the possibility to identify generalizable patterns but warrant meaningful translation into clinical practice. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society

    Nonlinear System Identification of Neural Systems from Neurophysiological Signals

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    The human nervous system is one of the most complicated systems in nature. Complex nonlinear behaviours have been shown from the single neuron level to the system level. For decades, linear connectivity analysis methods, such as correlation, coherence and Granger causality, have been extensively used to assess the neural connectivities and input-output interconnections in neural systems. Recent studies indicate that these linear methods can only capture a small amount of neural activities and functional relationships, and therefore cannot describe neural behaviours in a precise or complete way. In this review, we highlight recent advances in nonlinear system identification of neural systems, corresponding time and frequency domain analysis, and novel neural connectivity measures based on nonlinear system identification techniques. We argue that nonlinear modelling and analysis are necessary to study neuronal processing and signal transfer in neural systems quantitatively. These approaches can hopefully provide new insights to advance our understanding of neurophysiological mechanisms underlying neural functions. These nonlinear approaches also have the potential to produce sensitive biomarkers to facilitate the development of precision diagnostic tools for evaluating neurological disorders and the effects of targeted intervention

    Foreword Identification and Control in Biomedical Applications

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    Control engineering (in the broad sense of the term) has become an important enabling technology in many areas of medicine. Prominent examples include the artificial pancreas, closed-loop anesthesia, and personalized drug dosing strategies in neurology, oncology, endocrinology, and psychiatry. It is a testament to the power of control systems that allow individualizing treatment by providing mechanisms for linking treatment goals to treatment regimens, thus achieving a desired therapeutic effect. Consequently, the arrival of control systems engineering to the clinic enables the visionary concept of "treat the patient, not the disease" technologically and economically feasible

    Towards the Development of a Wearable Tremor Suppression Glove

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    Patients diagnosed with Parkinson’s disease (PD) often associate with tremor. Among other symptoms of PD, tremor is the most aggressive symptom and it is difficult to control with traditional treatments. This thesis presents the assessment of Parkinsonian hand tremor in both the time domain and the frequency domain, the performance of a tremor estimator using different tremor models, and the development of a novel mechatronic transmission system for a wearable tremor suppression device. This transmission system functions as a mechatronic splitter that allows a single power source to support multiple independent applications. Unique features of this transmission system include low power consumption and adjustability in size and weight. Tremor assessment results showed that the hand tremor signal often presents a multi-harmonics pattern. The use of a multi-harmonics tremor model produced a better estimation result than using a monoharmonic tremor model

    Hydrothermal Fluid Circulation and its Effect on Caldera Unrest

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    This paper focuses on the role that hydrothermal systems may play in caldera unrest. Changes in the fluid chemistry, temperature, and discharge rate of hydrothermal systems are commonly detected at the surface during volcanic unrest, as hydrothermal fluids adjust to changing subsurface conditions. Geochemical monitoring is carried out to observe the evolving system conditions. Circulating fluids can also generate signals that affect geophysical parameters monitored at the surface. Effective hazard evaluation requires a proper understanding of unrest phenomena and correct interpretation of their causes. Physical modeling of fluid circulation allows quantification of the evolution of a hydrothermal system, and hence evaluation of the potential role of hydrothermal fluids during caldera unrest. Modeling results can be compared with monitoring data, and then contribute to the interpretation of the recent caldera evolution. This paper: 1) describes the main features of hydrothermal systems; 2) briefly reviews numerical modeling of heat and fluid flow through porous media; 3) highlight the effects of hydrothermal fluids on unrest processes; and 4) describes some model applications to the Phlegrean Fields caldera. Simultaneous modeling of different independent parameters has proved to be a powerful tool for understanding caldera unrest. The results highlight the importance of comprehensive conceptual models that incorporate all the available geochemical and geophysical information, and they also stress the need for high-quality, multi-parameter monitoring and modeling of volcanic activity

    Hydrothermal fluid circulation and its effect on caldera unrest

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    This paper focuses on the role that hydrothermal systems may play in caldera unrest. Changes in the fluid chemistry, temperature, and discharge rate of hydrothermal systems are commonly detected at the surface during volcanic unrest, as hydrothermal fluids adjust to changing subsurface conditions. Geochemical monitoring is carried out to observe the evolving system conditions. Circulating fluids can also generate signals that affect geophysical parameters monitored at the surface. Effective hazard evaluation requires a proper understanding of unrest phenomena and correct interpretation of their causes. Physical modeling of fluid circulation allows quantification of the evolution of a hydrothermal system, and hence evaluation of the potential role of hydrothermal fluids during caldera unrest. Modeling results can be compared with monitoring data, and then contribute to the interpretation of the recent caldera evolution. This paper: 1) describes the main features of hydrothermal systems; 2) briefly reviews numerical modeling of heat and fluid flow through porous media; 3) highlight the effects of hydrothermal fluids on unrest processes; and 4) describes some model applications to the Phlegrean Fields caldera. Simultaneous modeling of different independent parameters has proved to be a powerful tool for understanding caldera unrest. The results highlight the importance of comprehensive conceptual models that incorporate all the available geochemical and geophysical information, and they also stress the need for high-quality, multi-parameter monitoring and modeling of volcanic activity

    Analysis of Fine Motor Skills in Essential Tremor: Combining Neuroimaging and Handwriting Biomarkers for Early Management

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    Essential tremor (ET) is a highly prevalent neurological disorder characterized by action-induced tremors involving the hand, voice, head, and/or face. Importantly, hand tremor is present in nearly all forms of ET, resulting in impaired fine motor skills and diminished quality of life. To advance early diagnostic approaches for ET, automated handwriting tasks and magnetic resonance imaging (MRI) offer an opportunity to develop early essential clinical biomarkers. In this study, we present a novel approach for the early clinical diagnosis and monitoring of ET based on integrating handwriting and neuroimaging analysis. We demonstrate how the analysis of fine motor skills, as measured by an automated Archimedes’ spiral task, is correlated with neuroimaging biomarkers for ET. Together, we present a novel modeling approach that can serve as a complementary and promising support tool for the clinical diagnosis of ET and a large range of tremors.This work was supported in part by the Universidad del País Vasco/Euskal Herriko Unibertsitatea, the University of Cambridge, PPG 17/51 and GIU 092/19, the Basque government (Saiotek SA-2010/00028, ELEKIN, Engineering and Society and Bioengineering Research Groups, GIC18/136, and ELKARTEK 18/99, 20/81), ‘‘Ministerio de Ciencia e Innovación’’ (SAF201677758R), FEDER funds, DomusVi Foundation (FP18/76), and the government of Gipuzkoa (HELENA, SABRINA, DG18/14-23, DG19/29, DG20/25 projects). This work is also based upon the work from COST Actions CA18106 and CA15225, supported by COST (European Cooperation in Science and Technology)
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