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

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)

Automatic Analysis of Archimedes’ Spiral for Characterization of Genetic Essential Tremor Based on Shannon’s Entropy and Fractal Dimension

Among neural disorders related to movement, essential tremor has the highest prevalence; in fact, it is twenty times more common than Parkinson's disease. The drawing of the Archimedes' spiral is the gold standard test to distinguish between both pathologies. The aim of this paper is to select non-linear biomarkers based on the analysis of digital drawings. It belongs to a larger cross study for early diagnosis of essential tremor that also includes genetic information. The proposed automatic analysis system consists in a hybrid solution: Machine Learning paradigms and automatic selection of features based on statistical tests using medical criteria. Moreover, the selected biomarkers comprise not only commonly used linear features (static and dynamic), but also other non-linear ones: Shannon entropy and Fractal Dimension. The results are hopeful, and the developed tool can easily be adapted to users; and taking into account social and economic points of view, it could be very helpful in real complex environments.This research was partially funded by the Basque Goverment, the University of the Basque Country by the IT1115-16 project-ELEKIN, Diputacion Foral de Gipuzkoa, University of Vic-Central University of Catalonia under the research grant R0947, and the Spanish Ministry of Science and Innovation TEC2016-77791-C04-R

Feature selection for automatic analysis of emotional response based on nonlinear speech modeling suitable for diagnosis of Alzheimer׳s disease

Alzheimer׳s disease (AD) is the most common type of dementia among the elderly. This work is part of a larger study that aims to identify novel technologies and biomarkers or features for the early detection of AD and its degree of severity. The diagnosis is made by analyzing several biomarkers and conducting a variety of tests (although only a post-mortem examination of the patients’ brain tissue is considered to provide definitive confirmation). Non-invasive intelligent diagnosis techniques would be a very valuable diagnostic aid. This paper concerns the Automatic Analysis of Emotional Response (AAER) in spontaneous speech based on classical and new emotional speech features: Emotional Temperature (ET) and fractal dimension (FD). This is a pre-clinical study aiming to validate tests and biomarkers for future diagnostic use. The method has the great advantage of being non-invasive, low cost, and without any side effects. The AAER shows very promising results for the definition of features useful in the early diagnosis of AD

International Conference on NONLINEAR SPEECH PROCESSING, NOLISP 2015

e Non-Linear Speech Processing (NOLISP) workshop is a biennial international workshop aiming at presenting and discussing new ideas, techniques and results related to alternative approaches in speech processing. New and innovative approaches and their applications are welcome to participate in this workshop. All fields of speech processing are targeted by the workshop, namely: Speech Production Speech Analysis and Modeling Speech Coding Speech Synthesis Speech Recognition Speaker Identification / Verification Speech Enhancement / Separation Speech Perception Others Contributions are expected in (though not restricted to) the following domains: Non-Linear Approximation Non-Linear Oscillators and Predictors Higher-Order Statistics Independent Component Analysis Nearest Neighbours Neural Networks Decision Trees Non-Parametric Models Dynamics of Non-Linear Systems Fractal Methods Chaos Modeling Non-Linear Differential Equation