Enhancing FoG detection by means of postural context using a waist accelerometer

Postprint (published version

HELP: Optimizing Treatment of Parkinson’s Disease Patients

3rd International Conference on the Elderly and New Technologies. III Jornadas Internacionales de Mayores y Nuevas Tecnologías.This paper presents a novel health monitoring system for Parkinson’s disease (pd) patients called help (Home-based Empowered Living for Parkinson’s disease patients). The help system has been specifically designed and implemented as a health monitoring system in order to optimize treatment and improve quality of life of people with Parkinson’s. This is a challenging goal due to the difficulty in establishing a closed-loop system that is able to detect the outcomes of treatment and react accordingly. In a similar way to diabetes treatment where the plasma glucose level can be measured and can be used to regulate drug doses, the help system’s approach aims to estimate pd symptoms and to adjust the dose of medication in order to reduce symptoms. The proposed health monitoring system is composed of several components: a body sensor & actuator network managed by a smartphone, a remote monitoring platform for doctors and clinical professionals as well as a telecommunication and service infrastructure. The real advantage derives from having constant medical control without dramatically modifying daily life. The help system is going to be evaluated in several cities during the first part of 2012 under daily living conditions with pd patients.En este trabajo se presenta un nuevo sistema de vigilancia de la salud para pacientes con la enfermedad de Parkinson (ep), pacientes llamados help (Fortaleciendo la vida en el hogar de pacientes con la enfermedad Parkinson). El sistema de ayuda ha sido específicamente diseñado e implementado como un sistema de vigilancia de la salud con el fin de optimizar el tratamiento y mejorar la calidad de vida de las personas con Parkinson. Este es un objetivo difícil debido a la dificultad del establecimiento de un sistema de circuito cerrado que es capaz de detectar los resultados del tratamiento y reaccionar en consecuencia. Es una manera similar al tratamiento de la diabetes donde el nivel de glucosa en plasma se puede medir y se puede utilizar para regular las dosis de medicamentos; el enfoque del sistema de ayuda tiene por objeto estimar los síntomas de la ep y ajustar la dosis de la medicación con el fin de reducir los síntomas. El sistema de vigilancia de la salud propuesto se compone de varios componentes: un sensor corporal y un actuador de red gestionado por un smartphone, una plataforma de monitorización remota para los médicos y clínicos profesionales, así como el uso de telecomunicaciones y servicios de infraestructura. La verdadera ventaja deriva de que tiene un constante control médico sin modificar drásticamente la vida cotidiana. El sistema help va a ser evaluado en varias ciudades durante la primera parte del año 2012 en condiciones de vida diaria con pacientes con ep

A kinematic sensor and algorithm to detect motor fluctuations in Parkinson disease: Validation study under real conditions of use

Postprint (published version

Posture transition identification on PD patients through a SVM-based technique and a single waist-worn accelerometer

Identification of activities of daily living is essential in order to evaluate the quality of life both in the elderly and patients with mobility problems. Posture transitions (PT) are one of the most mechanically demanding activities in daily life and,thus, they can lead to falls in patients with mobility problems. This paper deals with PT recognition in Parkinson’s Disease (PD) patients by means of a triaxial accelerometer situated between the anterior and the left lateral part of the waist. Since sensor’s orientation is susceptible to change during long monitoring periods, a hierarchical structure of classifiers is proposed in order to identify PT while allowing such orientation changes. Results are presented based on signals obtained from 20 PD patients and 67 healthy people who wore an inertial sensor on different positions among the anterior and the left lateral part of the waist. The algorithm has been compared to a previous approach in which only the anterior-lateral location was analyzed improving the sensitivity while preserving specificity. Moreover, different supervised machine l earning techniques have been evaluated in distinguishing PT. Results show that the location of the sensor slightly affects method’s performance and, furthermore, PD motor state does not alter its accuracy.Peer ReviewedPostprint (author’s final draft

A heterogeneous database for movement knowledge extraction in Parkinson's disease

This paper presents the design and methodology used to create a heterogeneous database for knowledge movement extraction in Parkinson's Disease. This database is being constructed as part of REM- PARK project and is composed of movement measurements acquired from inertial sensors, standard medical scales as Uni ed Parkinson's Disease Rating Scale, and other information obtained from 90 Parkinson's Disease patients. The signals obtained will be used to create movement disorder detection algorithms using supervised learning techniques. The different sources of information and the need of labelled data pose many challenges which the methodology described in this paper addresses. Some preliminary data obtained are presented.Postprint (published version

Effort Oxygen Saturation and Effort Heart Rate to Detect Exacerbations of Chronic Obstructive Pulmonary Disease or Congestive Heart Failure

Background: current algorithms for the detection of heart failure (HF) and chronic obstructive pulmonary disease (COPD) exacerbations have poor performance. Methods: this study was designed as a prospective longitudinal trial. Physiological parameters were evaluated at rest and effort (walking) in patients who were in the exacerbation or stable phases of HF or COPD. Parameters with relevant discriminatory power (sensitivity (Sn) or specificity (Sp) 80%, and Youden index 0.2) were integrated into diagnostic algorithms. Results: the study included 127 patients (COPD: 56, HF: 54, both: 17). The best algorithm for COPD included: oxygen saturation (SaO(2)) decrease 2% in minutes 1 to 3 of effort, end-of-effort heart rate (HR) increase 10 beats/min and walking distance decrease 35 m (presence of one criterion showed Sn: 0.90 (95%, CI(confidence interval): 0.75-0.97), Sp: 0.89 (95%, CI: 0.72-0.96), and area under the curve (AUC): 0.92 (95%, CI: 0.85-0.995)); and for HF: SaO(2) decrease 2% in the mean-of-effort, HR increase 10 beats/min in the mean-of-effort, and walking distance decrease 40 m (presence of one criterion showed Sn: 0.85 (95%, CI: 0.69-0.93), Sp: 0.75 (95%, CI: 0.57-0.87) and AUC 0.84 (95%, CI: 0.74-0.94)). Conclusions: effort situations improve the validity of physiological parameters for detection of HF and COPD exacerbation episodes

Pain and recurrent falls in the older and oldest-old non-institutionalized population

Background: Recurrent falls represent a priority in geriatric research. In this study we evaluated the influence of pain as a risk factor for recurrent falls (two or more in 1 year) in the older (65-79 years) and oldest-old (80 or more years) non-institutionalized population. Methods: Prospective cohort study. 772 non-institutionalized individuals with ages of 65 years or older (with overrepresentation of people aged 80 years or older [n = 550]) were included through randomized and multistage sampling, stratified according to gender, geographic area and habitat size. Basal evaluation at participant's home including pain evaluation by Face Pain Scale (FPS, range 0-6) and then telephonic contact every 3 months were performed until complete 12 months. Multivariate analysis by logistic regression (recurrent falls as outcome variable) for each age group (older and oldest-old group) were developed considering pain as a quantitative variable (according to FPS score). Models were adjusted for age, gender, balance, muscle strength, depressive symptoms, cognitive decline, number of drugs and number of drugs with risk of falls. Results: 114 (51.35%) and 286 (52%) participants of older and oldest-old group, respectively, reported pain; and recurrent falls occurred in 6.93% (n = 12) of the older group and 12.06% (n = 51) of the oldest-old group. In the older group, pain was associated with recurrent falls, with an associated odds ratio (OR) of 1.47 (95% CI 1.08-2.00; beta 0.3864) for each unit increase in pain intensity (thus, participants with the most severe pain [FPS 6] had OR of 10.16 regarding to participants without pain [FPS 0]). In the oldest-old group, pain was not associated with recurrent falls. Conclusions: Pain, a potentially modifiable and highly prevalent symptom, is a risk factor for recurrent falls in the older people (65-79 years). However, we have not been able to demonstrate that this relationship is maintained in the oldest-old population (80 or more years). Keywords: Oldest-old people, Falls, Risk factor

Demonstration of near infrared gas sensing using gold nanodisks on functionalized silicon

This paper was published in OPTICS EXPRESS and is made available as an electronic reprint with the permission of OSA. The paper can be found at the following URL on the OSA website: http://dx.doi.org/10.1364/OE.19.007664. Systematic or multiple reproduction or distribution to multiple locations via electronic or other means is prohibited and is subject to penalties under law[EN] In this work, we demonstrate experimentally the use of an array of gold nanodisks on functionalized silicon for chemosensing purposes. The metallic nanostructures are designed to display a very strong plasmonic resonance in the infrared regime, which results in highly sensitive sensing. Unlike usual experiments which are based on the functionalization of the metal surface, we functionalized here the silicon substrate. This silicon surface was modified chemically by buildup of an organosilane self-assembled monolayer (SAM) containing isocyanate as functional group. These groups allow for an easy surface regeneration by simple heating, thanks to the thermally reversible interaction isocyanate-analyte, which allows the cyclic use of the sensor. The technique showed a high sensitivity to surface binding events in gas and allowed the surface regeneration by heating of the sensor at 150°C. A relative wavelength shift ¿¿max/¿0 = 0.027 was obtained when the saturation level was reached. © 2011 Optical Society of America.Financial support by the Spanish MICINN under contracts CONSOLIDER EMET (CSD2008-00066) and TEC2008-06871-C02-02 and European Commission FP7 under the FET-Open project TAILPHOX 233833 is gratefully acknowledged.Rodríguez Cantó, PJ.; Martínez Marco, ML.; Rodríguez Fortuño, FJ.; Tomás Navarro, B.; Ortuño Molinero, R.; Peransi Llopis, SM.; Martínez Abietar, AJ. (2011). Demonstration of near infrared gas sensing using gold nanodisks on functionalized silicon. 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Machine learning for the development of diagnostic models of decompensated heart failure or exacerbation of chronic obstructive pulmonary disease

Heart failure (HF) and chronic obstructive pulmonary disease (COPD) are two chronic diseases with the greatest adverse impact on the general population, and early detection of their decompensation is an important objective. However, very few diagnostic models have achieved adequate diagnostic performance. The aim of this trial was to develop diagnostic models of decompensated heart failure or COPD exacerbation with machine learning techniques based on physiological parameters. A total of 135 patients hospitalized for decompensated heart failure and/or COPD exacerbation were recruited. Each patient underwent three evaluations: one in the decompensated phase (during hospital admission) and two more consecutively in the compensated phase (at home, 30 days after discharge). In each evaluation, heart rate (HR) and oxygen saturation (Ox) were recorded continuously (with a pulse oximeter) during a period of walking for 6 min, followed by a recovery period of 4 min. To develop the diagnostic models, predictive characteristics related to HR and Ox were initially selected through classification algorithms. Potential predictors included age, sex and baseline disease (heart failure or COPD). Next, diagnostic classification models (compensated vs. decompensated phase) were developed through different machine learning techniques. The diagnostic performance of the developed models was evaluated according to sensitivity (S), specificity (E) and accuracy (A). Data from 22 patients with decompensated heart failure, 25 with COPD exacerbation and 13 with both decompensated pathologies were included in the analyses. Of the 96 characteristics of HR and Ox initially evaluated, 19 were selected. Age, sex and baseline disease did not provide greater discriminative power to the models. The techniques with S and E values above 80% were the logistic regression (S: 80.83%; E: 86.25%; A: 83.61%) and support vector machine (S: 81.67%; E: 85%; A: 82.78%) techniques. The diagnostic models developed achieved good diagnostic performance for decompensated HF or COPD exacerbation. To our knowledge, this study is the first to report diagnostic models of decompensation potentially applicable to both COPD and HF patients. However, these results are preliminary and warrant further investigation to be confirmed

A two-question tool to assess the risk of repeated falls in the elderly

Introduction Older adults' perception of their own risk of fall has never been included into screening tools. The goal of this study was to evaluate the predictive validity of questions on subjects' self-perception of their own risk of fall. Methods This prospective study was conducted on a probabilistic sample of 772 Spanish community-dwelling older adults, who were followed-up for a one year period. At a baseline visit, subjects were asked about their recent history of falls (question 1: 'Have you fallen in the last 6 months?'), as well as on their perception of their own risk of fall by using two questions (question 2: 'Do you think you may fall in the next few months?' possible answers: yes/no; question 3: 'What is the probability that you fall in the next few months?' possible answers: low/intermediate/high). The follow-up consisted of quarterly telephone calls, where the number of falls occurred in that period was recorded. Results A short questionnaire built with questions 1 and 3 showed 70% sensitivity (95% CI: 56%-84%), 72% specificity (95% CI: 68%-76%) and 0.74 area under the ROC curve (95% CI: 0.66-0.82) for prediction of repeated falls in the subsequent year. Conclusions The estimation of one's own risk of fall has predictive validity for the occurrence of repeated falls in older adults. A short questionnaire including a question on perception of one's own risk of fall and a question on the recent history of falls had good predictive validity