A Kinematic Sensor and Algorithm to Detect Motor Fluctuations in Parkinson Disease : Validation Study Under Real Conditions of Use

A new algorithm has been developed, which combines information on gait bradykinesia and dyskinesia provided by a single kinematic sensor located on the waist of Parkinson disease (PD) patients to detect motor fluctuations (On- and Off-periods). The goal of this study was to analyze the accuracy of this algorithm under real conditions of use. This validation study of a motor-fluctuation detection algorithm was conducted on a sample of 23 patients with advanced PD. Patients were asked to wear the kinematic sensor for 1 to 3 days at home, while simultaneously keeping a diary of their On- and Off-periods. During this testing, researchers were not present, and patients continued to carry on their usual daily activities in their natural environment. The algorithm's outputs were compared with the patients' records, which were used as the gold standard. The algorithm produced 37% more results than the patients' records (671 vs 489). The positive predictive value of the algorithm to detect Off-periods, as compared with the patients' records, was 92% (95% CI 87.33%-97.3%) and the negative predictive value was 94% (95% CI 90.71%-97.1%); the overall classification accuracy was 92.20%. The kinematic sensor and the algorithm for detection of motor-fluctuations validated in this study are an accurate and useful tool for monitoring PD patients with difficult-to-control motor fluctuations in the outpatient setting

Technological advances in deep brain stimulation:Towards an adaptive therapy

Parkinson's disease (PD) is neurodegenerative movement disorder and a treatment method called deep brain stimulation (DBS) may considerably reduce the patient’s motor symptoms. The clinical procedure involves the implantation of a DBS lead, consisting of multiple electrode contacts, through which continuous high frequency (around 130 Hz) electric pulses are delivered in the brain. In this thesis, I presented the research which had the goal to improve current DBS technology, focusing on bringing the conventional DBS system a step closer to adaptive DBS, a personalized DBS therapy. The chapters in this thesis can be seen as individual building blocks for such an adaptive DBS system. After the general introduction, the first two chapters, two novel DBS lead designs are studied in a computational model. The model showed that both studied leads were able to exploit the novel distribution of the electrode contacts to shape and steer the stimulation field to activate more neurons in the chosen target compared to the conventional lead, and to counteract lead displacement. In the fourth chapter, an inverse current source density (CSD) method is applied on local field potentials (LFP) measured in a rat model. The pattern of CSD sources can act as a landmark within the STN to locate the potential stimulation target. The fifth and final chapter described the last building block of the DBS system. We introduced an inertial sensors and force sensor based measurement system, which can record hand kinematics and joint stiffness of PD patients. A system which can act as a feedback signal in an adaptive DBS system

Quantifying the Effects of Systematic STN-DBS Programming on Rest and Postural Tremor in Idiopathic Parkinson Disease Patients

Parkinson’s disease (PD) is a complex neurodegenerative disorder that encompasses both motor and non-motor symptoms. These symptoms and their severity are typically assessed by scale based measures in a clinical setting. Scale- based assessments of PD patients undergoing bilateral subthalamic nucleus deep brain stimulation surgery (STN-DBS) such as the Unified Parkinson Disease Rating Scale (UPDRS) are commonly used in a clinical setting to assess symptom severity and progression. However, the subjective nature of these and other clinical scales call into question both the sensitivity and accuracy of patient assessment over time. An objective quantification of rest and postural tremor of PD patients who have undergone STN-DBS has never been conducted. Furthermore, objective technologies that quantitatively assess the effects of STN-DBS programming on full body rest and postural tremor have not yet been fully explored. The study employed the use of a full body kinematic Inertial Motion Unit (IMU) based technology in order to study the short term and long term effects of Deep Brain Stimulation (DBS) on idiopathic PD patients. Not surprisingly both whole body rest and upper postural tremor reduced by six months following DBS surgery. An average best setting was identified for tremor reduction

The Effect of Balance Training on Postural Control in Patients with Parkinson s Disease Using a Virtual Rehabilitation System

[EN] Objectives: Parkinson's disease (PD) is a progressive neurodegenerative disorder characterized by motor clinical alterations among others. Postural problems have serious consequences for patients, not only limiting their daily life but also increasing some risks, like the risk of fall. Inadequate postural control and postural instability is a major problem in PD patients. A Virtual Motor Rehabilitation System (VMR) has been tested in patients with PD in the intervention period. Our pur-pose was to analyze the evolution of the spatial postural control during the intervention period, to see if there are any changes caused precisely by this intervention. Methods: Ten people with PD carried out 15 virtual rehabilitation sessions. We tested a groundbreaking system based on Virtual Motor Rehabilitation in two periods of time (baseline evaluation and final evaluation). In the training sessions, the participants performed a customizable treatment using a low-cost system, the Active Balance Rehabilitation system (ABAR). We stored the pressure performed by the participants every five hundredths of a second, and we analyzed the patients' pressure when they maintained their body on the left, on the right, and in the center in sitting position. Our system was able to measure postural control in every patient in each of the virtual rehabilitation sessions. Results: There are no significant differences in the performance of postural control in any of the positions evaluated throughout the sessions. Moreover, the results show a trend to an improvement in all positions. This improvement is especially remarkable in the left/right positions, which are the most important positions in order to avoid problems such as the risk of fall. With regard to the suitability of the ABAR system, we have found outstanding results in enjoyment, success, clarity, and helpfulness. Conclusions: Although PD is a progressive neurodegenerative disorder, the results demonstrate that patients with PD maintain or even improve their postural control in all positions. We think that the main factor influencing these results is that patients use more of their available cognitive processing to improve their postural control. The ABAR system allows us to make this assumption because the system requires the continuous attention of patients, promoting cognitive processing.This contribution was partially funded by the Gobierno de Aragon, Departamento de Industria e Innovacion, y Fondo Social Europeo "Construyendo Europa desde Aragon" and by the Programa Ibercaja-CAI de Estancias de Investigacion.Albiol-Perez, S.; Gil-Gómez, J.; Muñoz-Tomás, M.; Gil Gómez, H.; Vial Escolano, R.; Lozano Quilis, JA. (2017). 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Empowering patients in self-management of parkinson's disease through cooperative ICT systems

The objective of this chapter is to demonstrate the technical feasibility and medical effectiveness of personalised services and care programmes for Parkinson's disease, based on the combination of mHealth applications, cooperative ICTs, cloud technologies and wearable integrated devices, which empower patients to manage their health and disease in cooperation with their formal and informal caregivers, and with professional medical staff across different care settings, such as hospital and home. The presented service revolves around the use of two wearable inertial sensors, i.e. SensFoot and SensHand, for measuring foot and hand performance in the MDS-UPDRS III motor exercises. The devices were tested in medical settings with eight patients, eight hyposmic subjects and eight healthy controls, and the results demonstrated that this approach allows quantitative metrics for objective evaluation to be measured, in order to identify pre-motor/pre-clinical diagnosis and to provide a complete service of tele-health with remote control provided by cloud technologies. © 2016, IGI Global. All rights reserved

Objective assessment of upper limb motor symptoms in Parkinson's Disease using body-worn sensors

MD ThesisBackground There is a need for an objective method of symptom assessment in Parkinson's disease (PD) to enable better treatment decisions and to aid evaluation of new treatments. Current assessment methods; patient-completed symptom diaries and clinical rating scales, have limitations. Accelerometers (sensors capable of capturing data on human movement) and analysis using artificial neural networks (ANNs) have shown potential as a method of motor symptom evaluation in PD. It is unknown whether symptom monitoring with body-worn sensors is acceptable to PD patients due to a lack of previous research. Methods 34 participants with PD wore bilateral wrist-worn accelerometers for 4 hours in a research facility (phase 1) and then for 7 days in their homes (phase 2) whilst also completing symptom diaries. An ANN designed to predict a patient’s motor status, was developed and trained based on accelerometer data during phase 2. ANN performance was evaluated (leave-one-out approach) against patient-completed symptom diaries during phase 2, and against clinician rating of disease state during phase 1 observations. Participants’ views regarding the sensors were obtained via a Likert-style questionnaire completed after each phase. Differences in responses between phases were assessed for using the Wilcoxon rank-sum test. Results ANN-derived values of the proportion of time in each disease state (phase 2), showed strong, significant correlations with values derived from patient-completed symptom diaries. ANN disease state recognition during phase 1 was sub-optimal. High concordance with sensors was seen. Prolonged wearing of the sensors did not adversely affect participants’ opinions on the wearability of the sensors, when compared to their responses following phase 1 Conclusions Accelerometers and ANNs produced results comparable to those of symptom diaries. Our findings suggest that long-term monitoring with wrist-worn sensors is acceptable to PD patients