Optimizing Clinical Assessments in Parkinson's Disease Through the Use of Wearable Sensors and Data Driven Modeling

The emergence of motion sensors as a tool that provides objective motor performance data on individuals afflicted with Parkinson's disease offers an opportunity to expand the horizon of clinical care for this neurodegenerative condition. Subjective clinical scales and patient based motor diaries have limited clinometric properties and produce a glimpse rather than continuous real time perspective into motor disability. Furthermore, the expansion of machine learn algorithms is yielding novel classification and probabilistic clinical models that stand to change existing treatment paradigms, refine the application of advance therapeutics, and may facilitate the development and testing of disease modifying agents for this disease. We review the use of inertial sensors and machine learning algorithms in Parkinson's disease

Machine learning for large-scale wearable sensor data in Parkinson disease:concepts, promises, pitfalls, and futures

For the treatment and monitoring of Parkinson's disease (PD) to be scientific, a key requirement is that measurement of disease stages and severity is quantitative, reliable, and repeatable. The last 50 years in PD research have been dominated by qualitative, subjective ratings obtained by human interpretation of the presentation of disease signs and symptoms at clinical visits. More recently, “wearable,” sensor-based, quantitative, objective, and easy-to-use systems for quantifying PD signs for large numbers of participants over extended durations have been developed. This technology has the potential to significantly improve both clinical diagnosis and management in PD and the conduct of clinical studies. However, the large-scale, high-dimensional character of the data captured by these wearable sensors requires sophisticated signal processing and machine-learning algorithms to transform it into scientifically and clinically meaningful information. Such algorithms that “learn” from data have shown remarkable success in making accurate predictions for complex problems in which human skill has been required to date, but they are challenging to evaluate and apply without a basic understanding of the underlying logic on which they are based. This article contains a nontechnical tutorial review of relevant machine-learning algorithms, also describing their limitations and how these can be overcome. It discusses implications of this technology and a practical road map for realizing the full potential of this technology in PD research and practice

Motor patterns evaluation of people with neuromuscular disorders for biomechanical risk management and job integration/reintegration

Neurological diseases are now the most common pathological condition and the leading cause of disability, progressively worsening the quality of life of those affected. Because of their high prevalence, they are also a social issue, burdening both the national health service and the working environment. It is therefore crucial to be able to characterize altered motor patterns in order to develop appropriate rehabilitation treatments with the primary goal of restoring patients' daily lives and optimizing their working abilities. In this thesis, I present a collection of published scientific articles I co-authored as well as two in progress in which we looked for appropriate indices for characterizing motor patterns of people with neuromuscular disorders that could be used to plan rehabilitation and job accommodation programs. We used instrumentation for motion analysis and wearable inertial sensors to compute kinematic, kinetic and electromyographic indices. These indices proved to be a useful tool for not only developing and validating a clinical and ergonomic rehabilitation pathway, but also for designing more ergonomic prosthetic and orthotic devices and controlling collaborative robots

The views and needs of people with Parkinson disease regarding wearable devices for disease monitoring: Mixed methods exploration

Objective: This study aims to understand the views and needs of people with Parkinson disease regarding wearable devices for disease monitoring and management. Methods: This study used a mixed method parallel design, wherein survey and focus groups were concurrently conducted with people living with Parkinson disease in Munster, Ireland. Surveys and focus group schedules were developed with input from people with Parkinson disease. The survey included questions about technology use, wearable device knowledge, and Likert items about potential device features and capabilities. The focus group participants were purposively sampled for variation in age (all were aged >50 years) and sex. The discussions concerned user priorities, perceived benefits of wearable devices, and preferred features. Simple descriptive statistics represented the survey data. The focus groups analyzed common themes using a qualitative thematic approach. The survey and focus group analyses occurred separately, and results were evaluated using a narrative approach. Results: Overall, 32 surveys were completed by individuals with Parkinson disease. Four semistructured focus groups were held with 24 people with Parkinson disease. Overall, the participants were positive about wearable devices and their perceived benefits in the management of symptoms, especially those of motor dexterity. Wearable devices should demonstrate clinical usefulness and be user-friendly and comfortable. Participants tended to see wearable devices mainly in providing data for health care professionals rather than providing feedback for themselves, although this was also important. Barriers to use included poor hand function, average technology confidence, and potential costs. It was felt that wearable device design that considered the user would ensure better compliance and adoption. Conclusions: Wearable devices that allow remote monitoring and assessment could improve health care access for patients living remotely or are unable to travel. COVID-19 has increased the use of remotely delivered health care; therefore, future integration of technology with health care will be crucial. Wearable device designers should be aware of the variability in Parkinson disease symptoms and the unique needs of users. Special consideration should be given to Parkinson disease–related health barriers and the users’ confidence with technology. In this context, a user-centered design approach that includes people with Parkinson disease in the design of technology will likely be rewarded with improved user engagement and the adoption of and compliance with wearable devices, potentially leading to more accurate disease management, including self-management

Remote timed up and go evaluation from activities of daily living reveals changing mobility after surgery

Background: Mobility impairment is common in older adults and negatively influences the quality of life. Mobility level may change rapidly following surgery or hospitalization in the elderly. The timed up and go (TUG) is a simple, frequently used clinical test for functional mobility; however, TUG requires supervision from a trained clinician, resulting in infrequent assessments. Additionally, assessment by TUG in clinic settings may not be completely representative of the individual's mobility in their home environment. Objective: In this paper, we introduce a method to estimate TUG from activities detected in free-living, enabling continuous remote mobility monitoring without expert supervision. The method is used to monitor changes in mobility following total hip arthroplasty (THA). Methods: Community-living elderly (n = 239, 65-91 years) performed a standardized TUG in a laboratory and wore a wearable pendant device that recorded accelerometer and barometric sensor data for at least three days. Activities of daily living (ADLs), including walks and sit-to-stand transitions, and their related mobility features were extracted and used to develop a regularized linear model for remote TUG test estimation. Changes in the remote TUG were evaluated in orthopaedic patients (n = 15, 55-75 years), during 12-weeks period following THA. Main results: In leave-one-out-cross-validation (LOOCV), a strong correlation (p = 0.70) was observed between the new remote TUG and standardized TUG times. Test-retest reliability of 3-days estimates was high (ICC = 0.94). Compared to week 2 post-THA, remote TUG was significantly improved at week 6 (11.7 +/- 3.9 s versus 8.0 +/- 1.8 s,p &lt;0.001), with no further change at 12-weeks (8.1 +/- 3.9s, p = 0.37). Significance: Remote TUG can be estimated in older adults using 3-days of ADLs data recorded using a wearable pendant. Remote TUG has discriminatory potential for identifying frail elderly and may provide a convenient way to monitor changes in mobility in unsupervised settings.</p

Auf dem Weg zur automatisierten Programmierung der tiefen Hirnstimulation

Background: Deep Brain Stimulation (DBS) of the subthalamic nucleus (STN) is an effective treatment option for patients with Parkinson’s Disease (PD). To maximize treatment benefit, stimulation parameters need to be adjusted individually. Currently, this is performed following a trial-and-error approach, which is time-consuming, costly, and challenging for both patients and medical personnel. The recent introduction of directional electrodes has aggravated those difficulties, highlighting the need for more elaborate procedures to tailor DBS parameter selection to the individual patient. Recent studies suggested that the anatomical location of DBS electrodes could be used to predict beneficial stimulation parameters and guide DBS programming procedures. Methods: We developed StimFit, a software to automatically suggest optimal stimulation parameters in PD patients treated with STN-DBS based on reconstructed electrode locations. The software was trained on a dataset of 612 stimulation settings (applied in 31 patients) to predict motor improvement and side-effect probabilities with respect to electrode location and stimulation parameters. Model performance was retrospectively validated within the training cohort and tested on an independent dataset of 19 PD patients. The predictive models were then embedded in a non-linear optimization algorithm to find parameter combinations which would maximize predicted therapeutic benefit. A graphical user interface was designed to allow for a streamlined use of StimFit and the software was made publicly available. Next, StimFit was prospectively applied in 35 PD patients in a double-blind, cross-over trial to assess whether motor benefit of StimFit stimulation parameters would be non-inferior to patients’ standard of care treatment (SoC). Motor performance was evaluated according to the MDS-UPDRS-III under StimFit and SoC stimulation, randomizing the sequence of both conditions in a 1:1 ratio. Results: Motor outcome predictions of the data-driven model integrated in StimFit correlated well with observed outcome within the training cohort (R = 0.57, p < 0.001) as well as in the retrospective test cohort (R = 0.53, p < 0.001). In our prospective clinical trial StimFit and SoC stimulation resulted in clinically significant average motor improvement of 43 and 48 %, respectively. Mean absolute difference of motor outcome between both conditions was -1.6 ± 7.1 (95% CI: [-4.0, 0.9]) establishing non-inferiority of StimFit at the pre-defined margin of -5 points (p = 0.004).Conclusion: Beneficial stimulation parameters can be automatically derived from electrode location using data-driven approaches. Our results hold promise for more efficient and streamlined DBS programming procedures, but additional prospective studies are required to assess the effects of image-based DBS programming on non-motor domains and long-term quality of life.Hintergrund: Die Tiefe Hirnstimulation (THS) des Nucleus subthalamicus (STN) ist eine effektive Therapieoption zur Behandlung des idiopathischen Parkinson-Syndroms (IPS). Hierbei müssen die Stimulationsparameter individuell angepasst werden, was derzeit durch zeit- und ressourcenintensives Austesten erfolgt. Jüngste Studienergebnisse legen nahe, dass Informationen über die anatomische Lage der THS-Elektroden dafür genutzt werden könnten, vorteilhafte Stimulationseinstellungen zu identifizieren und somit die THS-Programmierung zu erleichtern. Methoden: Wir entwickelten eine Software (StimFit), durch welche optimale Stimulationseinstellungen für Patient*innen mit STN-THS auf Basis ihrer individuellen Elektrodenlagen vorgeschlagen werden können. Hierbei wurde ein Trainingsdatensatz von 612 Stimulationseinstellungen (31 Patient*innen) genutzt, um THS-Effekte in Abhängigkeit von Elektrodenlage und Stimulationsparametern zu prädizieren. Vorhersagegenauigkeiten wurden retrospektiv innerhalb des Trainingsdatensatzes, sowie in einer unabhängigen Testkohorte von 19 Patient*innen quantifiziert. Die validierten Vorhersagemodelle wurden dann in einen Optimierungsalgorithmus integriert, um Stimulationseinstellungen mit maximalem (prädizierten) therapeutischen Benefit zu ermitteln. Der Algorithmus wurde in eine grafische Benutzeroberfläche eingebettet und öffentlich zugänglich gemacht. In einer doppelblinden cross-over Studie wurde StimFit dann prospektiv an 35 Patient*innen mit STN-THS angewandt. Hierbei wurden sowohl die von StimFit vorgeschlagenen, als auch die durch traditionelle Optimierungsverfahren ermittelten („Standard of Care“, SoC) Stimulationseinstellungen in randomisierter Reihenfolge eingestellt. Die therapeutischen Effekte der StimFit-Einstellungen wurden mittels des MDS-UPDRS-III quantifiziert und diesbezüglich auf Nicht-Unterlegenheit gegenüber dem SoC untersucht. Ergebnisse: Die durch StimFit prädizierten motorischen Effekte korrelierten mit den empirischen Effekten innerhalb der Trainingskohorte (R = 0,57; p < 0,001) sowie in der retrospektiven Testkohorte (R = 0,53; p < 0,001). In der prospektiven Studie verbesserten sich die motorischen Symptome sowohl unter StimFit- als auch unter SoC-Stimulation (43 und 48 %). Der Summenscore des MDS-UPDRS-III unterschied sich statistisch nicht signifikant um -1,6 ± 7,1 (95% CI: [-4,0; 0,9]) zwischen beiden Stimulationskonditionen. Die Nicht-Unterlegenheit von StimFit konnte bei einer vordefinierten Grenze von -5 Punkten gezeigt werden (p = 0,004). Schlussfolgerungen: Effektive Stimulationseinstellungen können anhand der Elektrodenpositionen durch automatisierte datengetriebene Algorithmen abgeleitet werden und somit die Optimierung der THS-Parameter erleichtern. Weitere prospektive Studien sind notwendig, um Langzeiteffekte und den Einfluss datengetriebener THS-Programmierungsmethoden auf nicht-motorische Domänen und die Lebensqualität der Patient*innen zu ermitteln

Levodopa-induced gamma oscillations in patients with Parkinson’s disease using chronic invasive brain recording: detection, entrainment, and functional relevance

In Parkinson’s disease, imbalances between “antikinetic” and “prokinetic” patterns of neuronal oscillatory activity are related to motor dysfunction. Invasive brain recordings from the motor network have suggested that medical or surgical therapy can promote a prokinetic state by inducing narrowband gamma rhythms (60-90 Hz), which are associated with dyskinesia. Here, I investigate the behavioral and statistical properties of levodopa-induced and deep brain stimulation-entrained narrowband gamma rhythms. Collaborating with Oxford researchers, we also model the deep brain stimulation induced entrainment of gamma oscillations using interacting neuronal populations and patient-specific features of the levodopa-induced gamma oscillations. Using a sensing-enabled deep brain stimulation system, attached to both motor cortex and subthalamic or pallidal leads, the Starr Lab recorded over 900 hours of multisite field potentials prior to initiating deep brain stimulation, and over 600 hours with deep brain stimulation. I find that levodopa-induced gamma oscillations are more strongly associated with dyskinesia than deep brain stimulation-entrained gamma oscillations. Statistical comparisons revealed that levodopa-induced gamma oscillations exhibit increased variance in peak frequency, decreased spectral power, and higher variance in spectral power. Furthermore, I also work with collaborators at Oxford to show that entrainment can be predicted using neural circuit models fitted to patient data at various stimulation parameters. Put together, this work suggests that gamma oscillations as programming biomarkers should be leveraged distinctly across medical and surgical interventions to mitigate dyskinesia – while excessive levodopa-induced gamma oscillations are a marker for dyskinesia, increased entrained gamma oscillations may be a marker of a non-pathological prokinetic movement state. Our modeling work can subsequently be leveraged to predict the amount of entrained gamma across stimulation parameters

Gait characterization using wearable inertial sensors in healthy and pathological populations

Gait analysis is emerging as an effective tool to detect an incipient neurodegenerative disease or to monitor its progression. It has been shown that gait disturbances are an early indicator for cognitive impairments and can predict progression to neurodegenerative diseases. Furthermore, gait performance is a predictor of fall status, morbidity and mortality. Instrumented gait analysis provides quantitative measures to support the investigation of gait pathologies and the definition of targeted rehabilitation programs. In this framework, technologies such as inertial sensors are well accepted, and increasingly employed, as tools to characterize locomotion patterns and their variability in research settings. The general aim of this thesis is the evaluation, comparison and refinement of methods for gait characterization using magneto-inertial measurement units (MIMUs), in order to contribute to the migration of instrumented gait analysis from state of the art to state of the science (i.e.: from research towards its application in standard clinical practice). At first, methods for the estimation of spatio-temporal parameters during straight gait were investigated. Such parameters are in fact generally recognized as key metrics for an objective evaluation of gait and a quantitative assessment of clinical outcomes. Although several methods for their estimate have been proposed, few provided a thorough validation. Therefore an error analysis across different pathologies, multiple clinical centers and large sample size was conducted to further validate a previously presented method (TEADRIP). Results confirmed the applicability and robustness of the TEADRIP method. The combination of good performance, reliability and range of usage indicate that the TEADRIP method can be effectively adopted for gait spatio-temporal parameter estimation in the routine clinical practice. However, while traditionally gait analysis is applied to straight walking, several clinical motor tests include turns between straight gait segments. Furthermore, turning is used to evaluate subjects’ motor ability in more challenging circumstances. The second part of the research therefore headed towards the application of gait analysis on turning, both to segment it (i.e.: distinguish turns and straight walking bouts) and to specifically characterize it. Methods for turn identification based on a single MIMU attached to the trunk were implemented and their performance across pathological populations was evaluated. Focusing on Parkinson’s Disease (PD) subjects, turn characterization was also addressed in terms of onset and duration, using MIMUs positioned both on the trunk and on the ankles. Results showed that in PD population turn characterization with the sensors at the ankles lacks of precision, but that a single MIMU positioned on the low back is functional for turn identification. The development and validation of the methods considered in these works allowed for their application to clinical studies, in particular supporting the spatio-temporal parameters analysis in a PD treatment assessment and the investigation of turning characteristic in PD subjects with Freezing of Gait. In the first application, comparing the pre and post parameters it was possible to objectively determine the effectiveness of a rehabilitation treatment. In the second application, quantitative measures confirmed that in PD subjects with Freezing of Gait turning 360° in place is further compromised (and requires additional cognitive effort) compared to turning 180° while walking