Objective Assessment of the Finger Tapping Task in Parkinson's Disease and Control Subjects using Azure Kinect and Machine Learning

Parkinson's disease (PD) is characterised by a progressive worsening of motor functionalities. In particular, limited hand dexterity strongly correlates with PD diagnosis and staging. Objective detection of alterations in hand motor skills would allow, for example, prompt identification of the disease, its symptoms and the definition of adequate medical treatments. Among the clinical assessment tasks to diagnose and stage PD from hand impairment, the Finger Tapping (FT) task is a well-established tool. This preliminary study exploits a single RGB-Depth camera (Azure Kinect) and Google MediaPipe Hands to track and assess the Finger Tapping task. The system includes several stages. First, hand movements are tracked from FT video recordings and used to extract a series of clinically-relevant features. Then, the most significant features are selected and used to train and test several Machine Learning (ML) models, to distinguish subjects with PD from healthy controls. To test the proposed system, 35 PD subjects and 60 healthy volunteers were recruited. The best-performing ML model achieved a 94.4% Accuracy and 98.4% Fl score in a Leave-One-Subject-Out validation. Moreover, different clusters with respect to spatial and temporal variability in the FT trials among PD subjects were identified. This result suggests the possibility of exploiting the proposed system to perform an even finer identification of subgroups among the PD population

Measuring Brain Activation Patterns from Raw Single-Channel EEG during Exergaming: A Pilot Study

Physical and cognitive rehabilitation is deemed crucial to attenuate symptoms and to improve the quality of life in people with neurodegenerative disorders, such as Parkinson's Disease. Among rehabilitation strategies, a novel and popular approach relies on exergaming: the patient performs a motor or cognitive task within an interactive videogame in a virtual environment. These strategies may widely benefit from being tailored to the patient's needs and engagement patterns. In this pilot study, we investigated the ability of a low-cost BCI based on single-channel EEG to measure the user's engagement during an exergame. As a first step, healthy subjects were recruited to assess the system's capability to distinguish between (1) rest and gaming conditions and (2) gaming at different complexity levels, through Machine Learning supervised models. Both EEG and eye-blink features were employed. The results indicate the ability of the exergame to stimulate engagement and the capability of the supervised classification models to distinguish resting stage from game-play(accuracy > 95%). Finally, different clusters of subject responses throughout the game were identified, which could help define models of engagement trends. This result is a starting point in developing an effectively subject-tailored exergaming system

Assessing REM sleep behaviour disorder: from machine learning classification to the definition of a continuous dissociation index

Objectives: Rapid Eye Movement Sleep Behaviour Disorder (RBD) is regarded as a pro-drome of neurodegeneration, with a high conversion rate to α–synucleinopathies such as Parkinson’s Disease (PD). The clinical diagnosis of RBD co–exists with evidence of REM Sleep Without Atonia (RSWA), a parasomnia that features loss of physiological muscular atonia during REM sleep. The objectives of this study are to implement an automatic detection of RSWA from polysomnographic traces, and to propose a continuous index (the Dissociation Index) to assess the level of dissociation between REM sleep stage and atonia. This is performed using Euclidean distance in proper vector spaces. Each subject is assigned a dissociation degree based on their distance from a reference, encompassing healthy subjects and clinically diagnosed RBD patients at the two extremes. Methods: Machine Learning models were employed to perform automatic identification of patients with RSWA through clinical polysomnographic scores, together with variables derived from electromyography. Proper distance metrics are proposed and tested to achieve a dissociation measure. Results: The method proved efficient in classifying RSWA vs. not-RSWA subjects, achieving an overall accuracy, sensitivity and precision of 87%, 93% and 87.5%, respectively. On its part, the Dissociation Index proved to be promising in measuring the impairment level of patients. Conclusions: The proposed method moves a step forward in the direction of automatically identifying REM sleep disorders and evaluating the impairment degree. We believe that this index may be correlated with the patients’ neurodegeneration process; this assumption will undergo a robust clinical validation process involving healthy, RSWA, RBD and PD subjects

Electrodermal Activity in the Evaluation of Engagement for Telemedicine Applications

Electrodermal Activity (EDA) is a broadly-investigated physiological signal, whose behaviour is connected to nervous system arousal. Such system, indeed, influences the properties of the skin, producing a measurable electrical signal. Among the possible applications of such measurements, several studies have correlated the signal behaviour to engagement during mental and physical tasks, and the subjects' response to specific multimodal stimuli. Also due to the possibility of performing remote assessment and rehabilitation, telemedicine applications are gaining ground in the healthcare system. However, acceptance and engagement, hence continuity of usage, still remain significant obstacles. Therefore, it would be highly beneficial to verify, through objective measures, if these solutions are actually providing a sufficient stimulation to properly engage subjects while playing. This study investigates the possibility of employing EDA in the automatic recognition of different levels of user engagement, while playing a motor-cognitive exergame specifically designed for this purpose. Preliminary results, obtained on a cohort of 25 healthy subjects, seem to confirm that features extracted from EDA analysis are significant and able to train supervised classifiers, achieving high accuracy and precision in the engagement recognition problem

Deep learning and wearable sensors for the diagnosis and monitoring of Parkinson’s disease: A systematic review

Parkinson’s disease (PD) is a neurodegenerative disorder that produces both motor and non-motor complications, degrading the quality of life of PD patients. Over the past two decades, the use of wearable devices in combination with machine learning algorithms has provided promising methods for more objective and continuous monitoring of PD. Recent advances in artificial intelligence have provided new methods and algorithms for data analysis, such as deep learning (DL). The aim of this article is to provide a comprehensive review of current applications where DL algorithms are employed for the assessment of motor and nonmotor manifestations (NMM) using data collected via wearable sensors. This paper provides the reader with a summary of the current applications of DL and wearable devices for the diagnosis, prognosis, and monitoring of PD, in the hope of improving the adoption, applicability, and impact of both technologies as support tools. Following PRISMA (Systematic Reviews and Meta-Analyses) guidelines, sixty-nine studies were selected and analyzed. For each study, information on sample size, sensor configuration, DL approaches, validation methods, and results according to the specific symptom under study were extracted and summarized. Furthermore, quality assessment was conducted according to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) method. The majority of studies (74%) were published within the last three years, demonstrating the increasing focus on wearable technology and DL approaches for PD assessment. However, most papers focused on monitoring (59%) and computer-assisted diagnosis (37%), while few papers attempted to predict treatment response. Motor symptoms (86%) were treated much more frequently than NMM (14%). Inertial sensors were the most commonly used technology, followed by force sensors and microphones. Finally, convolutional neural networks (52%) were preferred to other DL approaches, while extracted features (38%) and raw data (37%) were similarly used as input for DL models. The results of this review highlight several challenges related to the use of wearable technology and DL methods in the assessment of PD, despite the advantages this technology could bring in the development and implementation of automated systems for PD assessment

Measuring Brain Activation Patterns from Raw Single-Channel EEG during Exergaming: A Pilot Study

Physical and cognitive rehabilitation is deemed crucial to attenuate symptoms and to improve the quality of life in people with neurodegenerative disorders, such as Parkinson’s Disease. Among rehabilitation strategies, a novel and popular approach relies on exergaming: the patient performs a motor or cognitive task within an interactive videogame in a virtual environment. These strategies may widely benefit from being tailored to the patient’s needs and engagement patterns. In this pilot study, we investigated the ability of a low-cost BCI based on single-channel EEG to measure the user’s engagement during an exergame. As a first step, healthy subjects were recruited to assess the system’s capability to distinguish between (1) rest and gaming conditions and (2) gaming at different complexity levels, through Machine Learning supervised models. Both EEG and eye-blink features were employed. The results indicate the ability of the exergame to stimulate engagement and the capability of the supervised classification models to distinguish resting stage from game-play (accuracy > 95%). Finally, different clusters of subject responses throughout the game were identified, which could help define models of engagement trends. This result is a starting point in developing an effectively subject-tailored exergaming system

Single-Channel EEG Detection of REM Sleep Behaviour Disorder: The Influence of REM and Slow Wave Sleep

Sleep Disorders have received much attention in recent years, as they are related to the risk and pathogenesis of neurodegenerative diseases. Notably, REM Sleep Behaviour Disorder (RBD) is considered an early symptom of -synucleinopathies, with a conversion rate to Parkinson’s Disease (PD) up to 90%. Recent studies also highlighted the role of disturbed Non-REM Slow Wave Sleep (SWS) in neurodegenerative diseases pathogenesis and its link to cognitive outcomes in PD and Dementia. However, the diagnosis of sleep disorders is a long and cumbersome process. This study proposes a method for automatically detecting RBD from single-channel EEG data, by analysing segments recorded during both REM sleep and SWS. This paper inspects the underlying microstructure of the two stages and includes a comparison of their performance to discuss their potential as markers for RBD. Machine Learning models were employed in the binary classification between healthy and RBD subjects, with an 86% averaged accuracy on a 5-fold cross-validation when considering both stages. Besides, SWS features alone proved promising in detecting RBD, scoring a 91% sensitivity (RBD class). These findings suggest the applicability of an EEG-based, low-cost, automatic detection of RBD, leading to potential use in the early diagnosis of neurodegeneration, thus allowing for disease-modifying interventions

Single‐channel EEG classification of sleep stages based on REM microstructure

Abstract Rapid‐eye movement (REM) sleep, or paradoxical sleep, accounts for 20–25% of total night‐time sleep in healthy adults and may be related, in pathological cases, to parasomnias. A large percentage of Parkinson's disease patients suffer from sleep disorders, including REM sleep behaviour disorder and hypokinesia; monitoring their sleep cycle and related activities would help to improve their quality of life. There is a need to accurately classify REM and the other stages of sleep in order to properly identify and monitor parasomnias. This study proposes a method for the identification of REM sleep from raw single‐channel electroencephalogram data, employing novel features based on REM microstructures. Sleep stage classification was performed by means of random forest (RF) classifier, K‐nearest neighbour (K‐NN) classifier and random Under sampling boosted trees (RUSBoost); the classifiers were trained using a set of published and novel features. REM detection accuracy ranges from 89% to 92.7%, and the classifiers achieved a F‐1 score (REM class) of about 0.83 (RF), 0.80 (K‐NN), and 0.70 (RUSBoost). These methods provide encouraging outcomes in automatic sleep scoring and REM detection based on raw single‐channel electroencephalogram, assessing the feasibility of a home sleep monitoring device with fewer channels

Towards fully automatic quantification of REM sleep without atonia according to the Sleep Innsbruck Barcelona (SINBAR) scoring method

Rapid eye movement (REM) sleep without atonia (RWA) is the polysomnographic hallmark of REM Sleep Behavior Disorder (RBD). The state-of-the-art methods to score RWA are visual-based. Recent international guidelines recommended the Sleep Innsbruck Barcelona (SINBAR) method for scoring RWA in 3-s mini-epochs. This method calls for scoring phasic EMG activity in the flexor digitorum superficialis (FDS) and “any” (i.e., tonic and/or phasic) EMG activity in the mentalis muscle. A semi-automatic algorithm scoring RWA according to this method is currently available in a commercial polysomnographic system (BrainRT, OSG, Belgium), however it still requires manual removal of EMG artifacts from expert scorers. This work proposes a novel method that, based on morphological aspects of EMG activity and machine learning (ML), discriminates activity from artifacts in the evaluation of RWA, thus allowing automatization for artifact correctio