Development and Evaluation of AI-based Parkinson's Disease Related Motor Symptom Detection Algorithms

Parkinson's Disease (PD) is a chronic, progressive, neurodegenerative disorder that is typically characterized by a loss of (motor) function, increased slowness and rigidity. Due to a lack of feasible biomarkers, progression cannot easily be quantified with objective measures. For the same reason, neurologists have to revert to monitoring of (motor) symptoms (i.e. by means of subjective and often inaccurate patient diaries) in order to evaluate a medication's effectiveness. Replacing or supplementing these diaries with an automatic and objective assessment of symptoms and side effects could drastically reduce manual efforts and potentially help in personalizing and improving medication regime. In turn, appearance of symptoms could be reduced and the patient's quality of life increased. The objective of this thesis is two-fold: (1) development and improvement of algorithms for detecting PD related motor symptoms and (2) to develop a software framework for time series analysis

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 double closed loop to enhance the quality of life of Parkinson's disease patients: REMPARK system

This paper presents REMPARK system, a novel approach to deal with Parkinson's Disease (PD). REMPARK system comprises two closed loops of actuation onto PD. The first loop consists in a wearable system that, based on a belt-worn movement sensor, detects movement alterations that activate an auditory cueing system controlled by a smartphone in order to improve patient's gait. The belt-worn sensor analyzes patient's movement through real-time learning algorithms that were developed on the basis of a database previously collected from 93 PD patients. The second loop consists in disease management based on the data collected during long periods and that enables neurologists to tailor medication of their PD patients and follow the disease evolution. REMPARK system is going to be tested in 40 PD patients in Spain, Ireland, Italy and Israel. This paper describes the approach followed to obtain this system, its components, functionalities and trials in which the system will be validated.Postprint (published version

Entwicklung und Auswertung von KI-basierten Algorithmen zur Erkennung von Motorsymptomen der Parkinson Krankheit

Parkinson's Disease (PD) is a chronic, progressive, neurodegenerative disorder that is typically characterized by a loss of (motor) function, increased slowness and rigidity. Due to a lack of feasible biomarkers, progression cannot easily be quantified with objective measures. For the same reason, neurologists have to revert to monitoring of (motor) symptoms (i.e. by means of subjective and often inaccurate patient diaries) in order to evaluate a medication's effectiveness. Replacing or supplementing these diaries with an automatic and objective assessment of symptoms and side effects could drastically reduce manual efforts and potentially help in personalizing and improving medication regime. In turn, appearance of symptoms could be reduced and the patient's quality of life increased. The objective of this thesis is two-fold: (1) development and improvement of algorithms for detecting PD related motor symptoms and (2) to develop a software framework for time series analysis

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Is "frequency distribution" enough to detect tremor in PD patients using a wrist worn accelerometer?

This paper presents two approaches on detecting tremor in patients with Parkinson’s Disease by means of a wrist-worn accelerometer. Both approaches are evaluated in terms of specificity and sensitivity as well as their applicability for a real-time implementation. One approach is solely based on the frequency distribution of a windowed time series, while the second approach utilizes commonly employed features found in the literature (e.g. FFT, entropy, peak frequency, correlation). The two algorithms detect tremor at rest in windowed time series. The effects of varying window lengths and detection thresholds are studied. The results indicate that an SVM with a linear kernel, in combination with the frequency distribution, may already be enough to accurately and reliably detect tremor in windowed time series. The approach, after being trained with a first dataset of signals obtained from 12 patients, achieved a sensitivity of 88.4% and specificity of 89.4% in a second dataset from 64 PD patients

A double closed loop to enhance the quality of life of Parkinson's disease patients: REMPARK system

This paper presents REMPARK system, a novel approach to deal with Parkinson's Disease (PD). REMPARK system comprises two closed loops of actuation onto PD. The first loop consists in a wearable system that, based on a belt-worn movement sensor, detects movement alterations that activate an auditory cueing system controlled by a smartphone in order to improve patient's gait. The belt-worn sensor analyzes patient's movement through real-time learning algorithms that were developed on the basis of a database previously collected from 93 PD patients. The second loop consists in disease management based on the data collected during long periods and that enables neurologists to tailor medication of their PD patients and follow the disease evolution. REMPARK system is going to be tested in 40 PD patients in Spain, Ireland, Italy and Israel. This paper describes the approach followed to obtain this system, its components, functionalities and trials in which the system will be validated

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Detecting freezing of gait with a tri-axial accelerometer in Parkinson’s disease patients

Freezing of gait (FOG) is a common motor symptom of Parkinson’s disease (PD), which presents itself as an inability to initiate or continue gait. This paper presents a method to monitor FOG episodes based only on acceleration measurements obtained from a waist-worn device. Three approximations of this method are tested. Initially, FOG is directly detected by a support vector machine (SVM). Then, classifier’s outputs are aggregated over time to determine a confidence value, which is used for the final classification of freezing (i.e., second and third approach). All variations are trained with signals of 15 patients and evaluated with signals from another 5 patients. Using a linear SVM kernel, the third approach provides 98.7 % accuracy and a geometric mean of 96.1 %. Moreover, it is investigated whether frequency features are enough to reliably detect FOG. Results show that these features allow the method to detect FOG with accuracies above 90 % and that frequency features enable a reliable monitoring of FOG by using simply a waist sensor