Sensor Approach for Brain Pathophysiology of Freezing of Gait in Parkinson\u27s Disease Patients

Parkinson\u27s Disease (PD) affects over 1% of the population over 60 years of age and is expected to reach 1 million in the USA by the year 2020, growing by 60 thousand each year. It is well understood that PD is characterized by dopaminergic loss, leading to decreased executive function causing motor symptoms such as tremors, bradykinesia, dyskinesia, and freezing of gait (FoG) as well as non-motor symptoms such as loss of smell, depression, and sleep abnormalities. A PD diagnosis is difficult to make since there is no worldwide approved test and difficult to manage since its manifestations are widely heterogeneous among subjects. Thus, understanding the patient subsets and the neural biomarkers that set them apart will lead to improved personalized care. To explore the physiological alternations caused by PD on neurological pathways and their effect on motor control, it is necessary to detect the neural activity and its dissociation with healthy physiological function. To this effect, this study presents a custom ultra-wearable sensor solution, consisting of electroencephalograph, electromyograph, ground reaction force, and symptom measurement sensors for the exploration of neural biomarkers during active gait paradigms. Additionally, this study employed novel de-noising techniques for dealing with the motion artifacts associated with active gait EEG recordings and compared time-frequency features between a group of PD with FoG and a group of age-matched controls and found significant differences between several EEG frequency bands during start and end of normal walking (with a p\u3c0.05)

Detection of gait initiation Failure in Parkinson's disease based on wavelet transform and Support Vector Machine

© 2017 IEEE. Gait initiation Failure (GIF) is the situation in which patients with Parkinson's disease (PD) feel as if their feet get 'stuck' to the floor when initiating their first steps. GIF is a subtype of Freezing of Gait (FOG) and often leads to falls and related injuries. Understanding of neurobiological mechanisms underlying GIF has been limited by difficulties in eliciting and objectively characterizing such gait phenomena in the clinical setting. Studies investigating the effects of GIF on brain activity using EEG offer the potential to study such behavior. In this preliminary study, we present a novel methodology where wavelet transform was used for feature extraction and Support Vector Machine for classifying GIF events in five patients with PD and FOG. To deal with the large amount of EEG data, a Principal Component Analysis (PCA) was applied to reduce the data dimension from 15 EEG channels into 6 principal components (PCs), retaining 93% of the information. Independent Component Analysis using Entropy Bound Minimization (ICA-EBM) was applied to 6 PCs for source separation with the aim of improving detection ability of GIF events as compared to the normal initiation of gait (Good Starts). The results of this analysis demonstrated the correct identification of GIF episodes with an 83.1% sensitivity, 89.5% specificity and 86.3% accuracy. These results suggest that our proposed methodology is a promising non-invasive approach to improve GIF detection in PD and FOG

A wearable biofeedback device to improve motor symptoms in Parkinson’s disease

Dissertação de mestrado em Engenharia BiomédicaThis dissertation presents the work done during the fifth year of the course Integrated Master’s in Biomedical Engineering, in Medical Electronics. This work was carried out in the Biomedical & Bioinspired Robotic Devices Lab (BiRD Lab) at the MicroElectroMechanics Center (CMEMS) established at the University of Minho. For validation purposes and data acquisition, it was developed a collaboration with the Clinical Academic Center (2CA), located at Braga Hospital. The knowledge acquired in the development of this master thesis is linked to the motor rehabilitation and assistance of abnormal gait caused by a neurological disease. Indeed, this dissertation has two main goals: (1) validate a wearable biofeedback system (WBS) used for Parkinson's disease patients (PD); and (2) develop a digital biomarker of PD based on kinematic-driven data acquired with the WBS. The first goal aims to study the effects of vibrotactile biofeedback to play an augmentative role to help PD patients mitigate gait-associated impairments, while the second goal seeks to bring a step advance in the use of front-end algorithms to develop a biomarker of PD based on inertial data acquired with wearable devices. Indeed, a WBS is intended to provide motor rehabilitation & assistance, but also to be used as a clinical decision support tool for the classification of the motor disability level. This system provides vibrotactile feedback to PD patients, so that they can integrate it into their normal physiological gait system, allowing them to overcome their gait difficulties related to the level/degree of the disease. The system is based on a user- centered design, considering the end-user driven, multitasking and less cognitive effort concepts. This manuscript presents all steps taken along this dissertation regarding: the literature review and respective critical analysis; implemented tech-based procedures; validation outcomes complemented with results discussion; and main conclusions and future challenges.Esta dissertação apresenta o trabalho realizado durante o quinto ano do curso Mestrado Integrado em Engenharia Biomédica, em Eletrónica Médica. Este trabalho foi realizado no Biomedical & Bioinspired Robotic Devices Lab (BiRD Lab) no MicroElectroMechanics Center (CMEMS) estabelecido na Universidade do Minho. Para efeitos de validação e aquisição de dados, foi desenvolvida uma colaboração com Clinical Academic Center (2CA), localizado no Hospital de Braga. Os conhecimentos adquiridos no desenvolvimento desta tese de mestrado estão ligados à reabilitação motora e assistência de marcha anormal causada por uma doença neurológica. De facto, esta dissertação tem dois objetivos principais: (1) validar um sistema de biofeedback vestível (WBS) utilizado por doentes com doença de Parkinson (DP); e (2) desenvolver um biomarcador digital de PD baseado em dados cinemáticos adquiridos com o WBS. O primeiro objetivo visa o estudo dos efeitos do biofeedback vibrotáctil para desempenhar um papel de reforço para ajudar os pacientes com PD a mitigar as deficiências associadas à marcha, enquanto o segundo objetivo procura trazer um avanço na utilização de algoritmos front-end para biomarcar PD baseado em dados inerciais adquiridos com o dispositivos vestível. De facto, a partir de um WBS pretende-se fornecer reabilitação motora e assistência, mas também utilizá-lo como ferramenta de apoio à decisão clínica para a classificação do nível de deficiência motora. Este sistema fornece feedback vibrotáctil aos pacientes com PD, para que possam integrá-lo no seu sistema de marcha fisiológica normal, permitindo-lhes ultrapassar as suas dificuldades de marcha relacionadas com o nível/grau da doença. O sistema baseia-se numa conceção centrada no utilizador, considerando o utilizador final, multitarefas e conceitos de esforço menos cognitivo. Portanto, este manuscrito apresenta todos os passos dados ao longo desta dissertação relativamente a: revisão da literatura e respetiva análise crítica; procedimentos de base tecnológica implementados; resultados de validação complementados com discussão de resultados; e principais conclusões e desafios futuros

Gait analysis in neurological populations: Progression in the use of wearables

Gait assessment is an essential tool for clinical applications not only to diagnose different neurological conditions but also to monitor disease progression as it contributes to the understanding of underlying deficits. There are established methods and models for data collection and interpretation of gait assessment within different pathologies. This narrative review aims to depict the evolution of gait assessment from observation and rating scales to wearable sensors and laboratory technologies, and provide possible future directions. In this context, we first present an extensive review of current clinical outcomes and gait models. Then, we demonstrate commercially available wearable technologies with their technical capabilities along with their use in gait assessment studies for various neurological conditions. In the next sections, a descriptive knowledge for existing inertial based algorithms and a sign based guide that shows the outcomes of previous neurological gait assessment studies are presented. Finally, we state a discussion for the use of wearables in gait assessment and speculate the possible research directions by revealing the limitations and knowledge gaps in the literature

Cross-Frequency Coupling and Intelligent Neuromodulation.

Cross-frequency coupling (CFC) reflects (nonlinear) interactions between signals of different frequencies. Evidence from both patient and healthy participant studies suggests that CFC plays an essential role in neuronal computation, interregional interaction, and disease pathophysiology. The present review discusses methodological advances and challenges in the computation of CFC with particular emphasis on potential solutions to spurious coupling, inferring intrinsic rhythms in a targeted frequency band, and causal interferences. We specifically focus on the literature exploring CFC in the context of cognition/memory tasks, sleep, and neurological disorders, such as Alzheimer's disease, epilepsy, and Parkinson's disease. Furthermore, we highlight the implication of CFC in the context and for the optimization of invasive and noninvasive neuromodulation and rehabilitation. Mainly, CFC could support advancing the understanding of the neurophysiology of cognition and motor control, serve as a biomarker for disease symptoms, and leverage the optimization of therapeutic interventions, e.g., closed-loop brain stimulation. Despite the evident advantages of CFC as an investigative and translational tool in neuroscience, further methodological improvements are required to facilitate practical and correct use in cyborg and bionic systems in the field

Automated Intelligent Cueing Device to Improve Ambient Gait Behaviors for Patients with Parkinson\u27s Disease

Freezing of gait (FoG) is a common motor dysfunction in individuals with Parkinson’s disease (PD). FoG impairs walking and is associated with increased fall risk. Although pharmacological treatments have shown promise during ON-medication periods, FoG remains difficult to treat during medication OFF state and in advanced stages of the disease. External cueing therapy in the forms of visual, auditory, and vibrotactile, has been effective in treating gait deviations. Intelligent (or on-demand) cueing devices are novel systems that analyze gait patterns in real-time and activate cues only at moments when specific gait alterations are detected. In this study we developed methods to analyze gait signals collected through wearable sensors and accurately identify FoG episodes. We also investigated the potential of predicting the symptoms before their actual occurrence. We collected data from seven participants with PD using two Inertial Measurement Units (IMUs) on ankles. In our first study, we extracted engineered features from the signals and used machine learning (ML) methods to identify FoG episodes. We tested the performance of models using patient-dependent and patient-independent paradigms. The former models achieved 92.5% and 89.0% for average sensitivity and specificity, respectively. However, the conventional binary classification methods fail to accurately classify data if only data from normal gait periods are available. In order to identify FoG episodes in participants who did not freeze during data collection sessions, we developed a Deep Gait Anomaly Detector (DGAD) to identify anomalies (i.e., FoG) in the signals. DGAD was formed of convolutional layers and trained to automatically learn features from signals. The convolutional layers are followed by fully connected layers to reduce the dimensions of the features. A k-nearest neighbors (kNN) classifier is then used to classify the data as normal or FoG. The models identified 87.4% of FoG onsets, with 21.9% being predicted on average for each participant. This study demonstrates our algorithm\u27s potential for delivery of preventive cues. The DGAD algorithm was then implemented in an Android application to monitor gait patterns of PD patients in ambient environments. The phone triggered vibrotactile and auditory cues on a connected smartwatch if an FoG episode was identified. A 6-week in-home study showed the potentials for effective treatment of FoG severity in ambient environments using intelligent cueing devices

Clinical Decision Support Systems with Game-based Environments, Monitoring Symptoms of Parkinson’s Disease with Exergames

Parkinson’s Disease (PD) is a malady caused by progressive neuronal degeneration, deriving in several physical and cognitive symptoms that worsen with time. Like many other chronic diseases, it requires constant monitoring to perform medication and therapeutic adjustments. This is due to the significant variability in PD symptomatology and progress between patients. At the moment, this monitoring requires substantial participation from caregivers and numerous clinic visits. Personal diaries and questionnaires are used as data sources for medication and therapeutic adjustments. The subjectivity in these data sources leads to suboptimal clinical decisions. Therefore, more objective data sources are required to better monitor the progress of individual PD patients. A potential contribution towards more objective monitoring of PD is clinical decision support systems. These systems employ sensors and classification techniques to provide caregivers with objective information for their decision-making. This leads to more objective assessments of patient improvement or deterioration, resulting in better adjusted medication and therapeutic plans. Hereby, the need to encourage patients to actively and regularly provide data for remote monitoring remains a significant challenge. To address this challenge, the goal of this thesis is to combine clinical decision support systems with game-based environments. More specifically, serious games in the form of exergames, active video games that involve physical exercise, shall be used to deliver objective data for PD monitoring and therapy. Exergames increase engagement while combining physical and cognitive tasks. This combination, known as dual-tasking, has been proven to improve rehabilitation outcomes in PD: recent randomized clinical trials on exergame-based rehabilitation in PD show improvements in clinical outcomes that are equal or superior to those of traditional rehabilitation. In this thesis, we present an exergame-based clinical decision support system model to monitor symptoms of PD. This model provides both objective information on PD symptoms and an engaging environment for the patients. The model is elaborated, prototypically implemented and validated in the context of two of the most prominent symptoms of PD: (1) balance and gait, as well as (2) hand tremor and slowness of movement (bradykinesia). While balance and gait affections increase the risk of falling, hand tremors and bradykinesia affect hand dexterity. We employ Wii Balance Boards and Leap Motion sensors, and digitalize aspects of current clinical standards used to assess PD symptoms. In addition, we present two dual-tasking exergames: PDDanceCity for balance and gait, and PDPuzzleTable for tremor and bradykinesia. We evaluate the capability of our system for assessing the risk of falling and the severity of tremor in comparison with clinical standards. We also explore the statistical significance and effect size of the data we collect from PD patients and healthy controls. We demonstrate that the presented approach can predict an increased risk of falling and estimate tremor severity. Also, the target population shows a good acceptance of PDDanceCity and PDPuzzleTable. In summary, our results indicate a clear feasibility to implement this system for PD. Nevertheless, long-term randomized clinical trials are required to evaluate the potential of PDDanceCity and PDPuzzleTable for physical and cognitive rehabilitation effects

Neural Preparation For Step Initiation In Unpredictable Conditions With Age And Parkinson\u27s Disease

Mobility is essential for the independent lifestyle. However, as the US population ages, challenges to mobility start to arise, among them just the aging itself which leads to decreased postural stability, falls and the second most common neurodegenerative disease, that is Parkinson’s disease (PD). We decided to investigate step initiation as it is crucial to mobility: walking is not possible without the first step. Step initiation is impaired in PD. However, the impact of PD on the neural mechanisms of step initiation when some of the step parameters are unpredictable remains unexplored. Cortical preparation for step initiation can be assessed by beta event-related desynchronization (ERD) derived from electroencephalography (EEG) recordings. We hypothesized that subjects with PD would exhibit less cortical modulation between conditions of forward step initiation with and without prior knowledge of limb choice. Further, we hypothesized that decreased cortical modulation in PD would associate with a higher impairment of motor performance. Results identified that the group with PD exhibited decreased beta ERD amplitudes that were similar regardless of condition, whereas control subjects modulated beta ERD amplitudes between conditions, particularly in early stages of pre-movement processing in areas overlying sensory cortex. Subjects with PD presented with delayed and reduced postural preparation with increased step target error across both conditions and exhibited a greater incidence of multiple anticipatory postural adjustments (APAs) in the predictable relative to the unpredictable condition. Delayed postural preparation significantly correlated with lower amplitudes of beta ERD. We concluded that diminished early pre-movement processing over sensory cortex was concomitant with poor pre-selection of the stepping limb in predictable conditions and that a generally diminished amplitude of cortical pre-movement processing relates to delayed step initiation in people with PD. Furthermore, impaired mobility accompanies healthy aging, but there is a need for deeper understanding of how aging changes central control of motor behavior. Using previous study’s method, we compared cortical preparation for step initiation using beta ERD in young and older healthy subjects performing forward steps with and without prior knowledge of limb choice. Our results show that older subjects exhibited increased beta ERD amplitudes before the step regardless of whether they were informed of limb choice or not. Moreover, older subjects exhibited early increases in beta ERD in the “sensory” cluster of electrodes, but only when full limb-choice information was available. Behaviorally, the older subjects also exhibited shortened and increased anticipatory postural adjustments which led to earlier step initiation and similar swing-foot velocities but was also accompanied by greater target step placement errors and decreased postural stability. For the older group, condition-related increases in beta ERD amplitudes and stability correlated with condition-related prolongation of APA durations. We conclude that older subjects exhibited a spectrum across two strategies: (1) a “fast” strategy associated with decreased neural preparation that trades shortened step preparation and higher swing-foot velocity for target step errors and lowered postural stability; and (2) an “accurate” strategy associated with greater neural preparation, longer step-preparation time, and higher stability during step execution. In conclusion, this thesis provides more support for beta ERD as a useful tool for studying cortical preparation non-invasively. We have also established the importance of the signals recorded by “sensory” clusters: in subjects with PD the absence of beta ERD similar to the control group was associated with impaired motor behavior even when conditions were predictable. Similarly, a part of the older group seemed to pre-potentiate its cortex lying beneath the cluster of “sensory” electrodes which was associated with more safe and accurate steps. Further investigations should focus on the importance of sensorimotor integration and its’ changes due to PD or healthy aging and beta ERD may be an excellent tool for this task

Detection and Prediction of Freezing of Gait in Parkinson’s Disease using Wearable Sensors and Machine Learning

Freezing of gait (FOG), is a brief episodic absence of forward body progression despite the intention to walk. Appearing mostly in mid-late stage Parkinson’s disease (PD), freezing manifests as a sudden loss of lower-limb function, and is closely linked to falling, decreased functional mobility, and loss of independence. Wearable-sensor based devices can detect freezes already in progress, and intervene by delivering auditory, visual, or tactile stimuli called cues. Cueing has been shown to reduce FOG duration and allow walking to continue. However, FOG detection and cueing systems require data from the freeze episode itself and are thus unable to prevent freezing. Anticipating the FOG episode before onset and supplying a timely cue could prevent the freeze from occurring altogether. FOG has been predicted in offline analyses by training machine learning models to identify wearable-sensor signal patterns known to precede FOG. The most commonly used sensors for FOG detection and prediction are inertial measurement units (IMU) that include an accelerometer, gyroscope and sometimes magnetometer. Currently, the best FOG prediction systems use data collected from multiple sensors on various body locations to develop person-specific models. Multi-sensor systems are more complex and may be challenging to integrate into real-life assistive devices. The ultimate goal of FOG prediction systems is a user-friendly assistive device that can be used by anyone experiencing FOG. To achieve this goal, person-independent models with high FOG prediction performance and a minimal number of conveniently located sensors are needed. The objectives of this thesis were: to develop and evaluate FOG detection and prediction models using IMU and plantar pressure data; determine if event-based or period of gait disruption FOG definitions have better classification performance for FOG detection and prediction; and evaluate FOG prediction models that use a single unilateral plantar pressure insole sensor or bilateral sensors. In this thesis, IMU (accelerometer and gyroscope) and plantar pressure insole sensors were used to collect data from 11 people with FOG while they walked a freeze provoking path. A custom-made synchronization and labeling program was used synchronize the IMU and plantar pressure data and annotate FOG episodes. Data were divided into overlapping 1 s windows with 0.2 s shift between consecutive windows. Time domain, Fourier transform based, and wavelet transform based features were extracted from the data. A total of 861 features were extracted from each of the 71,000 data windows. To evaluate the effectiveness of FOG detection and prediction models using plantar pressure and IMU data features, three feature sets were compared: plantar pressure, IMU, and both plantar pressure and IMU features. Minimum-redundancy maximum-relevance (mRMR) and Relief-F feature selection were performed prior to training boosted ensembles of decision trees. The binary classification models identified Total-FOG or Non-FOG states, wherein the Total-FOG class included windows with data from 2 s before the FOG onset until the end of the FOG episode. The plantar-pressure-only model had the greatest sensitivity, and the IMU-only model had the greatest specificity. The best overall model used the combination of plantar pressure and IMU features, achieving 76.4% sensitivity and 86.2% specificity. Next, the Total-FOG class components were evaluated individually (i.e., Pre-FOG windows, freeze windows, and transition windows between Pre-FOG and FOG). The best model, which used plantar pressure and IMU features, detected windows that contained both Pre-FOG and FOG data with 85.2% sensitivity, which is equivalent to detecting FOG less than 1 s after the freeze began. Models using both plantar pressure and IMU features performed better than models that used either sensor type alone. Datasets used to train machine learning models often generate ground truth FOG labels based on visual observation of specific lower limb movements (event-based definition) or an overall inability to walk effectively (period of gait disruption based definition). FOG definition ambiguity may affect FOG detection and prediction model performance, especially with respect to multiple FOG in rapid succession. This research examined the effects of defining FOG either as a period of gait disruption (merging successive FOG), or based on an event (no merging), on FOG detection and prediction. Plantar pressure and lower limb acceleration data were used to extract a set of features and train decision tree ensembles. FOG was labeled using an event-based definition. Additional datasets were then produced by merging FOG that occurred in rapid succession. A merging threshold was introduced where FOG that were separated by less than the merging threshold were merged into one episode. FOG detection and prediction models were trained for merging thresholds of 0, 1, 2, and 3 s. Merging had little effect on FOG detection model performance; however, for the prediction model, merging resulted in slightly later FOG identification and lower precision. FOG prediction models may benefit from using event-based FOG definitions and avoiding merging multiple FOG in rapid succession. Despite the known asymmetry of PD motor symptom manifestation, the difference between the more severely affected side (MSS) and less severely affected side (LSS) is rarely considered in FOG detection and prediction studies. The additional information provided by the MSS or LSS, if any, may be beneficial to FOG prediction models, especially if using a single sensor. To examine the effect of using data from the MSS, LSS, or both limbs, multiple FOG prediction models were trained and compared. Three datasets were created using plantar pressure data from the MSS, LSS, and both sides together. Feature selection was performed, and FOG prediction models were trained using the top 5, 10, 15, 20, 25 or 30 features for each dataset. The best models were the MSS model with 15 features, and the LSS and bilateral features with 5 features. The LSS model reached the highest sensitivity (79.5%) and identified the highest percentage of FOG episodes (94.9%). The MSS model achieved the highest specificity (84.9%) and the lowest false positive (FP) rate (2 FP/walking trial). Overall, the bilateral model was best. The bilateral model had 77.3% sensitivity, 82.9% specificity, and identified 94.3% of FOG episodes an average of 1.1 s before FOG onset. Compared to the bilateral model, the LSS model had a higher false positive rate; however, the bilateral and LSS models were similar in all other evaluation metrics. Therefore, using the LSS model instead of the bilateral model would produce similar FOG prediction performance at the cost of slightly more false positives. Given the advantages of single sensor systems, the increased FP rate may be acceptable. Therefore, a single plantar pressure sensor placed on the LSS could be used to develop a FOG prediction system and produce performance similar to a bilateral system

Neuromagnetic investigations of mechanisms and effects of STN-DBS and medication in Parkinson's disease

Parkinson’s disease (PD) is a neurodegenerative disorder cardinally marked by motor symptoms, but also sensory symptoms and several other non-motor symptoms. PD patients are typically treated with dopaminergic medication for several years. Many patients eventually experience bouts of periods where medication might not be able to effectively control symptoms as well as experience side-effects of long-term dopaminergic treatments. Deep brain stimulation (DBS) is an option as the next therapeutic recourse for such patients. DBS treatment essentially involves placement of stimulating electrodes in the subthalamic nucleus (STN) or the globus pallidus internum (GPi) along with an implanted pulse generator (IPG) in the sub-clavicular space. STN-DBS alleviates motor symptoms and leads to substantial improvements in quality of life for PD patients. Although DBS is known to improve several classes of symptoms, the effect mechanism of DBS is still not clear. While there is a lack of electrophysiological investigation of sensory processing and the effects of treatments in PD altogether, the electrophysiological studies of the cortical dynamics during motor tasks and at rest lack consensus.We recorded magnetoencephalography (MEG) and electromyography (EMG) from PD patients in three studies: (i) at rest, (ii) during median nerve stimulation, and (iii) while performing phasic contractions (hand gripping). The three studies focused on cortical oscillatory dynamics at rest, during somatosensory processing and during movement, respectively. The measurements were conducted in DBS-treated, untreated (DBS washout) and dopaminergic-medicated states. While both treatments (DBS and dopaminergic medication) ameliorated motor symptoms similarly in all studies, they showed differentiated effects on: (i) increased sensorimotor cortical low-gamma spectral power (31-45 Hz) (but no changes in beta power (13-30 Hz)) at rest only during DBS, (ii) somatosensory processing with higher gamma augmentation (31-45 Hz, 20-60 ms) in the dopaminergic-medicated state compared to DBS-treated and untreated states, and (iii) hand gripping with increased motor-related beta corticomuscular coherence (CMC, 13-30 Hz) during dopaminergic medication in contrast to increased gamma power (31-45 Hz) during DBS.Firstly, we infer from the three studies that DBS and dopaminergic medication employ partially different anatomo-functional pathways and functional strategies when improving PD symptoms. Secondly, we suggest that treatments act on pathological oscillatory dynamics differently at cortical and sub-cortical levels and may do so through more sophisticated mechanisms than mere suppression of the pathological spectral power in a particular band. And thirdly, we urge exploring effect mechanisms of PD treatments beyond the motor system. The effects of dopaminergic medication on early somatosensory processing has opened the door for exploring the effects of treatments and studying their mechanisms using electrophysiology, especially in higher order sensory deficits. Integration of such research findings into a holistic view on mechanisms of treatments could pave way for better disease management paradigms.