Machine learning-based classification of simple drawing movements in Parkinson's disease

Multi-Scale and Multi-Level Feature Assessment Framework for Classification of Parkinson's Disease State From Short-Term Motor Tasks

OBJECTIVE

Recent quantification research on Parkinson's disease (PD) integrates wearable technology with machine learning methods, indicating a strong potential for practical applications. However, the effectiveness of these techniques is influenced by environmental settings and is hardly applied in real-world situations. This paper aims to propose an effective feature assessment framework to automatically rate the severity of PD motor symptoms from short-term motor tasks, and then classify different PD severity levels in the real world.

METHODS

This paper identified specific PD motor symptoms using a novel feature-assessment framework at both segment-level and sample-level. Features were selected after calculating SHapley Additive exPlanation(SHAP) value, and verified by different machine learning methods with appropriate parameters. This framework has been verified on real-world data from 100 PD patients performing Unified Parkinson's Disease Rating Scale(UPDRS)-recommended short motor tasks, each task lasting 20-50 seconds.

RESULTS

The sensitivity for recognizing motor fluctuations reached 88% in tremor recognition. Additionally, LightGBM achieved the highest accuracy for early detection(92.59%) and achieved 71.58% in fine-grained severity classification using 31 selected features.

CONCLUSION

This paper reports the first effort to assess multi-level and multi-scale features for automatic quantification of motor symptoms and PD severity levels. The proposed framework has been proven effective in assessing key PD information for recognition during short-term tasks.

SIGNIFICANCE

The explanatory analysis of digital features in this study provides more prior knowledge for PD self-assessment in a free-living environment.

Towards Parkinson's Disease Detection Through Analysis of Everyday Handwriting

Background: Parkinson's disease (PD) is the second most prevalent neurodegenerative disorder worldwide. People suffering from PD exhibit motor symptoms that affect the control of upper and lower limb movement. Among daily activities that depend on proper upper limb control is the handwriting process, which has been studied in state-of-the-art research, mainly considering non-semantic drawings like spirals, geometric figures, cursive lines, and others. Objectives: This paper analyzes the suitability of modeling the handwriting process of digits from 0 to 9 to automatically discriminate between PD patients and healthy control subjects. The main hypothesis is that modeling these numbers allows a more natural evaluation of upper limb control. Methods: Two approaches are considered: modeling of the images resulting from the strokes collected by the digital tablet and modeling of the time series yielded by the digital tablet while performing the strokes, i.e., time-dependent signals. The first approach is implemented by fine-tuning a CNN-based architecture, while the second approach is based on hand-crafted features measured upon the time series, namely pressure and kinematic measurements. Features extracted from time-dependent signals are represented following two strategies, one based on statistical functionals and the other one based on creating Gaussian Mixture Models (GMMs). Results: The experiments indicate that pressure-based features modeled with functionals are the ones that yield the highest accuracy, indicating that PD-related symptoms are better modeled with dynamic approaches than those based on images. Conclusions: The dynamic approach outperformed the image-based model, indicating that the writing process, modeled with signals collected over time, reveals motor symptoms more clearly than images resulting from handwriting. This finding is in line with previous results in the state-of-the-art research and constitutes a step forward to create more accurate and informative methods to detect and monitor PD symptoms.

Predicting Parkinson's Disease Using a Deep-Learning Algorithm to Analyze Prodromal Medical and Prescription Data

BACKGROUND AND PURPOSE

Parkinson's disease (PD) is characterized by various prodromal symptoms, and these symptoms are mostly investigated retrospectively. While some symptoms such as rapid eye movement sleep behavior disorder are highly specific, others are common. This makes it challenging to predict those at risk of PD based solely on less-specific prodromal symptoms. The prediction accuracy when using only less-specific symptoms can be improved by analyzing the vast amount of information available using sophisticated deep-learning techniques. This study aimed to improve the performance of deep-learning-based screening in detecting prodromal PD using medical-claims data, including prescription information.

METHODS

We sampled 820 PD patients and 8,200 age- and sex-matched non-PD controls from Korean National Health Insurance cohort data. A deep-learning algorithm was developed using various combinations of diagnostic codes, medication codes, and prodromal periods.

RESULTS

During the prodromal period from year -3 to year 0, predicting PD using only diagnostic codes yielded a high accuracy of 0.937. Adding medication codes for the same period did not increase the accuracy (0.931-0.935). For the earlier prodromal period (year -6 to year -3), the accuracy of PD prediction decreased to 0.890 when using only diagnostic codes. The inclusion of all medication-codes data increased that accuracy markedly to 0.922.

CONCLUSIONS

A deep-learning algorithm using both prodromal diagnostic and medication codes was effective in screening PD. Developing a surveillance system with automatically collected medical-claims data for those at risk of developing PD could be cost-effective. This approach could streamline the process of developing disease-modifying drugs by focusing on the most-appropriate candidates for inclusion in accurate diagnostic tests.

Cognitive activity analysis of Parkinson's patients using artificial intelligence techniques

PURPOSE

The development of modern Artificial Intelligence (AI) based models for the early diagnosis of Parkinson's disease (PD) has been gaining deep attention by researchers recently. In particular, the use of different types of datasets (voice, hand movements, gait, etc.) increases the variety of up-to-date models. Movement disorders and tremors are also among the most prominent symptoms of PD. The usage of drawings in the detection of PD can be a crucial decision-support approach that doctors can benefit from.

METHODS

A dataset was created by asking 40 PD and 40 Healthy Controls (HC) to draw spirals with and without templates using a special tablet. The patient-healthy distinction was achieved by classifying drawings of individuals using Support Vector Machine (SVM), Random Forest (RF), and Naive Bayes (NB) algorithms. Prior to classification, the data were normalized by applying the min-max normalization method. Moreover, Leave-One-Subject-Out (LOSO) Cross-Validation (CV) approach was utilized to eliminate possible overfitting scenarios. To further improve the performances of classifiers, Principal Component Analysis (PCA) dimension reduction technique were also applied to the raw data and the results were compared accordingly.

RESULTS

The highest accuracy among machine learning based classifiers was obtained as 90% with SVM classifier using non-template drawings with PCA application.

CONCLUSION

The model can be used as a pre-evaluation system in the clinic as a non-invasive method that also minimizes environmental and educational level differences by using simple hand gestures such as hand drawing, writing numbers, words, and syllables. As a result of our study, preliminary preparation has been made so that hand drawing analysis can be used as an auxiliary system that can save time for health professionals. We plan to work on more comprehensive data in the future.

Investigating the efficacy and importance of mobile-based assessments for Parkinson's disease: uncovering the potential of novel digital tests

This study introduces PDMotion, a mobile application comprising 11 digital tests, including those adapted from the MDS-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) Part III and novel assessments, for remote Parkinson's Disease (PD) motor symptoms evaluation. Employing machine learning techniques on data from 50 PD patients and 29 healthy controls, PDMotion achieves accuracies of 0.878 for PD status prediction and 0.715 for severity assessment. A post-hoc explanation model is employed to assess the importance of features and tasks in diagnosis and severity evaluation. Notably, novel tasks that are not adapted from MDS-UPDRS Part III like the circle drawing, coordination test, and alternative tapping test are found to be highly important, suggesting digital assessments for PD can go beyond digitizing existing tests. The alternative tapping test emerges as the most significant task. Using its features alone achieves prediction accuracies comparable to the full task set, underscoring its potential as an independent screening tool. This study addresses a notable research gap by digitalizing a wide array of tests, including novel ones, and conducting a comparative analysis of their feature and task importance. These insights provide guidance for task selection and future development in PD mobile assessments, a field previously lacking such comparative studies.

A review of machine learning and deep learning algorithms for Parkinson's disease detection using handwriting and voice datasets

Parkinson's Disease (PD) is a prevalent neurodegenerative disorder with significant clinical implications. Early and accurate diagnosis of PD is crucial for timely intervention and personalized treatment. In recent years, Machine Learning (ML) and Deep Learning (DL) techniques have emerged as promis-ing tools for improving PD diagnosis. This review paper presents a detailed analysis of the current state of ML and DL-based PD diagnosis, focusing on voice, handwriting, and wave spiral datasets. The study also evaluates the effectiveness of various ML and DL algorithms, including classifiers, on these datasets and highlights their potential in enhancing diagnostic accuracy and aiding clinical decision-making. Additionally, the paper explores the identifi-cation of biomarkers using these techniques, offering insights into improving the diagnostic process. The discussion encompasses different data formats and commonly employed ML and DL methods in PD diagnosis, providing a comprehensive overview of the field. This review serves as a roadmap for future research, guiding the development of ML and DL-based tools for PD detection. It is expected to benefit both the scientific community and medical practitioners by advancing our understanding of PD diagnosis and ultimately improving patient outcomes.

A Deep Learning Approach for Automatic and Objective Grading of the Motor Impairment Severity in Parkinson's Disease for Use in Tele-Assessments

Wearable sensors provide a tool for at-home monitoring of motor impairment progression in neurological conditions such as Parkinson's disease (PD). This study examined the ability of deep learning approaches to grade the motor impairment severity in a modified version of the Movement Disorders Society-sponsored revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) using low-cost wearable sensors. We hypothesized that expanding training datasets with motion data from healthy older adults (HOAs) and initializing classifiers with weights learned from unsupervised pre-training would lead to an improvement in performance when classifying lower vs. higher motor impairment relative to a baseline deep learning model (XceptionTime). This study evaluated the change in classification performance after using expanded training datasets with HOAs and transferring weights from unsupervised pre-training compared to a baseline deep learning model (XceptionTime) using both upper extremity (finger tapping, hand movements, and pronation-supination movements of the hands) and lower extremity (toe tapping and leg agility) tasks consistent with the MDS-UPDRS. Overall, we found a 12.2% improvement in accuracy after expanding the training dataset and pre-training using max-vote inference on hand movement tasks. Moreover, we found that the classification performance improves for every task except toe tapping after the addition of HOA training data. These findings suggest that learning from HOA motion data can implicitly improve the representations of PD motion data for the purposes of motor impairment classification. Further, our results suggest that unsupervised pre-training can improve the performance of motor impairment classifiers without any additional annotated PD data, which may provide a viable solution for a widely deployable telemedicine solution.

A review of emergent intelligent systems for the detection of Parkinson's disease

Parkinson's disease (PD) is a neurodegenerative disorder affecting people worldwide. The PD symptoms are divided into motor and non-motor symptoms. Detection of PD is very crucial and essential. Such challenges can be overcome by applying artificial intelligence to diagnose PD. Many studies have also proposed the implementation of computer-aided diagnosis for the detection of PD. This systematic review comprehensively analyzed all appropriate algorithms for detecting and assessing PD based on the literature from 2012 to 2023 which are conducted as per PRISMA model. This review focused on motor symptoms, namely handwriting dynamics, voice impairments and gait, multimodal features, and brain observation using single photon emission computed tomography, magnetic resonance and electroencephalogram signals. The significant challenges are critically analyzed, and appropriate recommendations are provided. The critical discussion of this review article can be helpful in today's PD community in such a way that it allows clinicians to provide proper treatment and timely medication.

A Deep Learning Approach for Grading of Motor Impairment Severity in Parkinson's Disease

Objective and quantitative monitoring of movement impairments is crucial for detecting progression in neurological conditions such as Parkinson's disease (PD). This study examined the ability of deep learning approaches to grade motor impairment severity in a modified version of the Movement Disorders Society-sponsored revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) using low-cost wearable sensors. A convolutional neural network architecture, XceptionTime, was used to classify lower and higher levels of motor impairment in persons with PD, across five distinct rhythmic tasks: finger tapping, hand movements, pronation-supination movements of the hands, toe tapping, and leg agility. In addition, an aggregate model was trained on data from all tasks together for evaluating bradykinesia symptom severity in PD. The model performance was highest in the hand movement tasks with an accuracy of 82.6% in the hold-out test dataset; the accuracy for the aggregate model was 79.7%, however, it demonstrated the lowest variability. Overall, these findings suggest the feasibility of integrating low-cost wearable technology and deep learning approaches to automatically and objectively quantify motor impairment in persons with PD. This approach may provide a viable solution for a widely deployable telemedicine solution.

Utilizing a tablet-based artificial intelligence system to assess movement disorders in a prospective study

Spiral drawings on paper are used as routine measures in hospitals to assess Parkinson's Disease motor deficiencies. In the age of emerging mobile health tools and Artificial Intelligence a comprehensive digital setup enables granular biomarker analyses and improved differential diagnoses in movement disorders. This study aims to evaluate on discriminatory features among Parkison's Disease patients, healthy subjects and diverse movement disorders. Overall, 24 Parkinson's Disease patients, 27 healthy controls and 26 patients with similar differential diagnoses were assessed with a novel tablet-based system. It utilizes an integrative assessment by combining a structured symptoms questionnaire-the Parkinson's Disease Non-Motor Scale-and 2-handed spiral drawing captured on a tablet device. Three different classification tasks were evaluated: Parkinson's Disease patients versus healthy control group (Task 1), all Movement disorders versus healthy control group (Task 2) and Parkinson's Disease patients versus diverse other movement disorder patients (Task 3). To systematically study feature importances of digital biomarkers a Machine Learning classifier is cross-validated and interpreted with SHapley Additive exPlanations (SHAP) values. The number of non-motor symptoms differed significantly for Tasks 1 and 2 but not for Task 3. The proposed drawing features partially differed significantly for all three tasks. The diagnostic accuracy was on average 94.0% in Task 1, 89.4% in Task 2, and 72% in Task 3. While the accuracy in Task 3 only using the symptom questionnaire was close to the baseline, it greatly improved when including the tablet-based features from 60 to 72%. The accuracies for all three tasks were significantly improved by integrating the two modalities. These results show that tablet-based drawing features can not only be captured by consumer grade devices, but also capture specific features to Parkinson's Disease that significantly improve the diagnostic accuracy compared to the symptom questionnaire. Therefore, the proposed system provides an objective type of disease characterization of movement disorders, which could be utilized for home-based assessments as well.Clinicaltrials.gov Study-ID: NCT03638479.

A screening method for cervical myelopathy using machine learning to analyze a drawing behavior

Early detection of cervical myelopathy (CM) is important for a favorable outcome, as its prognosis is poor when left untreated. We developed a screening method for CM using machine learning-based analysis of the drawing behavior of 38 patients with CM and 66 healthy volunteers. Using a stylus pen, the participants traced three different shapes displayed on a tablet device. During the tasks, writing behaviors, such as the coordinates, velocity, and pressure of the stylus tip, along with the drawing time, were recorded. From these data, features related to the drawing pressure, and time to trace each shape and combination of shapes were used as training data for the support vector machine, a machine learning algorithm. To evaluate the accuracy, a receiver operating characteristic curve was generated, and the area under the curve (AUC) was calculated. Models with triangular waveforms tended to be the most accurate. The best triangular wave model identified patients with and without CM with 76% sensitivity and 76% specificity, yielding an AUC of 0.80. Our model was able to classify CM with high accuracy and could be applied to the development of disease screening systems useful outside the hospital setting.

Explainable machine learning models based on multimodal time-series data for the early detection of Parkinson's disease

BACKGROUND AND OBJECTIVES

Parkinson's Disease (PD) is a devastating chronic neurological condition. Machine learning (ML) techniques have been used in the early prediction of PD progression. Fusion of heterogeneous data modalities proved its capability to improve the performance of ML models. Time series data fusion supports the tracking of the disease over time. In addition, the trustworthiness of the resulting models is improved by adding model explainability features. The literature on PD has not sufficiently explored these three points.

METHODS

In this work, we proposed an ML pipeline for predicting the progression of PD that is both accurate and explainable. We explore the fusion of different combinations of five time series modalities from the Parkinson's Progression Markers Initiative (PPMI) real-world dataset, including patient characteristics, biosamples, medication history, motor, and non-motor function data. Each patient has six visits. The problem has been formulated in two ways: ❶ a three-class based progression prediction with 953 patients in each time series modality, and ❷ a four-class based progression prediction with 1,060 patients in each time series modality. The statistical features of these six visits were calculated from each modality and diverse feature selection methods were applied to select the most informative feature sets. The extracted features were used to train a set of well-known ML models including Support vector machines (SVM), random forests (RF), extra tree classifier (ETC), light gradient boosting machines (LGBM), and stochastic gradient descent (SGD). We examined a number of data-balancing strategies in the pipeline with different combinations of modalities. ML models have been optimized using the Bayesian optimizer. A comprehensive evaluation of various ML methods has been conducted, and the best models have been extended to provide different explainability features.

RESULTS

We compare the performance of ML models before and after optimization and using and without using feature selection. In the three-class experiment and with various modality fusions, the LGBM model produced the most accurate results with a 10-fold cross-validation (10-CV) accuracy of 90.73% using non-motor function modality. RF produced the best results in the four-class experiment with various modality fusions with a 10-CV accuracy of 94.57% using non-motor modality. With the fused dataset of non-motor and motor function modalities, the LGBM model outperformed the other ML models in both the 3-class and 4-class experiments (i.e., 10-CV accuracy of 94.89% and 93.73%, respectively). Using the Shapely Additive Explanations (SHAP) framework, we employed global and instance-based explanations to explain the behavior of each ML classifier. Moreover, we extended the explainability by implementing the LIME and SHAPASH local explainers. The consistency of these explainers has been explored. The resultant classifiers were accurate, explainable, and thus medically more relevant and applicable.

CONCLUSIONS

The select modalities and feature sets were confirmed by the literature and medical experts. The various explainers suggest that the bradykinesia (NP3BRADY) feature was the most dominant and consistent. By providing thorough insights into the influence of multiple modalities on the disease risk, the suggested approach is expected to help improve the clinical knowledge of PD progression processes.

Convergence of Artificial Intelligence and Neuroscience towards the Diagnosis of Neurological Disorders-A Scoping Review

Artificial intelligence (AI) is a field of computer science that deals with the simulation of human intelligence using machines so that such machines gain problem-solving and decision-making capabilities similar to that of the human brain. Neuroscience is the scientific study of the struczture and cognitive functions of the brain. Neuroscience and AI are mutually interrelated. These two fields help each other in their advancements. The theory of neuroscience has brought many distinct improvisations into the AI field. The biological neural network has led to the realization of complex deep neural network architectures that are used to develop versatile applications, such as text processing, speech recognition, object detection, etc. Additionally, neuroscience helps to validate the existing AI-based models. Reinforcement learning in humans and animals has inspired computer scientists to develop algorithms for reinforcement learning in artificial systems, which enables those systems to learn complex strategies without explicit instruction. Such learning helps in building complex applications, like robot-based surgery, autonomous vehicles, gaming applications, etc. In turn, with its ability to intelligently analyze complex data and extract hidden patterns, AI fits as a perfect choice for analyzing neuroscience data that are very complex. Large-scale AI-based simulations help neuroscientists test their hypotheses. Through an interface with the brain, an AI-based system can extract the brain signals and commands that are generated according to the signals. These commands are fed into devices, such as a robotic arm, which helps in the movement of paralyzed muscles or other human parts. AI has several use cases in analyzing neuroimaging data and reducing the workload of radiologists. The study of neuroscience helps in the early detection and diagnosis of neurological disorders. In the same way, AI can effectively be applied to the prediction and detection of neurological disorders. Thus, in this paper, a scoping review has been carried out on the mutual relationship between AI and neuroscience, emphasizing the convergence between AI and neuroscience in order to detect and predict various neurological disorders.

Using Features Extracted From Upper Limb Reaching Tasks to Detect Parkinson's Disease by Means of Machine Learning Models

While in the literature there is much interest in investigating lower limbs gait of patients affected by neurological diseases, such as Parkinson's Disease (PD), fewer publications involving upper limbs movements are available. In previous studies, 24 motion signals (the so-called reaching tasks) of the upper limbs of PD patients and Healthy Controls (HCs) were used to extract several kinematic features through a custom-made software; conversely, the aim of our paper is to investigate the possibility to build models-using these features-for distinguishing PD patients from HCs. First, a binary logistic regression and, then, a Machine Learning (ML) analysis was performed by implementing five algorithms through the Knime Analytics Platform. The ML analysis was performed twice: first, a leave-one out-cross validation was applied; then, a wrapper feature selection method was implemented to identify the best subset of features that could maximize the accuracy. The binary logistic regression achieved an accuracy of 90.5%, demonstrating the importance of the maximum jerk during subjects upper limb motion; the Hosmer-Lemeshow test supported the validity of this model (p-value=0.408). The first ML analysis achieved high evaluation metrics by overcoming 95% of accuracy; the second ML analysis achieved a perfect classification with 100% of both accuracy and area under the curve receiver operating characteristics. The top-five features in terms of importance were the maximum acceleration, smoothness, duration, maximum jerk and kurtosis. The investigation carried out in our work has proved the predictive power of the features, extracted from the reaching tasks involving the upper limbs, to distinguish HCs and PD patients.

Long Short-Term Memory-Based Deep Learning Models for Screening Parkinson's Disease Using Sequential Diagnostic Codes

BACKGROUND AND PURPOSE

It is challenging to detect Parkinson's disease (PD) in its early stages, which has prompted researchers to develop techniques based on machine learning methods for detecting PD. However, previous studies did not fully incorporate the slow progression of PD over a long period of time nor consider that its symptoms occur in a time-sequential manner. Contributing to the literature on PD, which has relied heavily on cross-sectional data, this study aimed to develop a method for detecting PD early that can process time-series information using the long short-term memory (LSTM) algorithm.

METHODS

We sampled 926 patients with PD and 9,260 subjects without PD using medical-claims data. The LSTM algorithm was tested using diagnostic histories, which contained the diagnostic codes and their respective time information. We compared the prediction power of the 12-month diagnostic codes under two different settings over the 4 years prior to the first PD diagnosis.

RESULTS

The model that was trained using the most-recent 12-month diagnostic codes had the best performance, with an accuracy of 94.25%, a sensitivity of 82.91%, and a specificity of 95.26%. The other three models (12-month codes from 2, 3, and 4 years prior) were found to have comparable performances, with accuracies of 92.27%, 91.86%, and 91.81%, respectively. The areas under the curve from our data settings ranged from 0.839 to 0.923.

CONCLUSIONS

We explored the possibility that PD specialists could benefit from our proposed machine learning method as an early detection method for PD.

A dual-task-embedded virtual reality system for intelligent quantitative assessment of cognitive processing speed

Introduction

Processing speed is defined as the ability to quickly process information, which is generally considered as one of the affected cognitive functions of multiple sclerosis and schizophrenia. Paper-pencil type tests are traditionally used in the assessment of processing speed. However, these tests generally need to be conducted under the guidance of clinicians in a specific environment, which limits their application in cognitive assessment or training in daily life. Therefore, this paper proposed an intelligent evaluation method of processing speed to assist clinicians in diagnosis.

Methods

We created an immersive virtual street embedded with Stroop task (VR-Street). The behavior and performance information was obtained by performing the dual-task of street-crossing and Stroop, and a 50-participant dataset was established with the label of standard scale. Utilizing Pearson correlation coefficient to find the relationship between the dual-task features and the cognitive test results, and an intelligent evaluation model was developed using machine learning.

Results

Statistical analysis showed that all Stroop task features were correlated with cognitive test results, and some behavior features also showed correlation. The estimated results showed that the proposed method can estimate the processing speed score with an adequate accuracy (mean absolute error of 0.800, relative accuracy of 0.916 and correlation coefficient of 0.804). The combination of Stroop features and behavior features showed better performance than single task features.

Discussion

The results of this work indicates that the dual-task design in this study better mobilizes participants' attention and cognitive resources, and more fully reflects participants' cognitive processing speed. The proposed method provides a new opportunity for accurate quantitative evaluation of cognitive function through virtual reality.

A Systematic Survey of Research Trends in Technology Usage for Parkinson's Disease

Parkinson's disease (PD) is a neurological disorder with complicated and disabling motor and non-motor symptoms. The complexity of PD pathology is amplified due to its dependency on patient diaries and the neurologist's subjective assessment of clinical scales. A significant amount of recent research has explored new cost-effective and subjective assessment methods pertaining to PD symptoms to address this challenge. This article analyzes the application areas and use of mobile and wearable technology in PD research using the PRISMA methodology. Based on the published papers, we identify four significant fields of research: diagnosis, prognosis and monitoring, predicting response to treatment, and rehabilitation. Between January 2008 and December 2021, 31,718 articles were published in four databases: PubMed Central, Science Direct, IEEE Xplore, and MDPI. After removing unrelated articles, duplicate entries, non-English publications, and other articles that did not fulfill the selection criteria, we manually investigated 1559 articles in this review. Most of the articles (45%) were published during a recent four-year stretch (2018-2021), and 19% of the articles were published in 2021 alone. This trend reflects the research community's growing interest in assessing PD with wearable devices, particularly in the last four years of the period under study. We conclude that there is a substantial and steady growth in the use of mobile technology in the PD contexts. We share our automated script and the detailed results with the public, making the review reproducible for future publications.

A survey of visual and procedural handwriting analysis for neuropsychological assessment

To date, Artificial Intelligence systems for handwriting and drawing analysis have primarily targeted domains such as writer identification and sketch recognition. Conversely, the automatic characterization of graphomotor patterns as biomarkers of brain health is a relatively less explored research area. Despite its importance, the work done in this direction is limited and sporadic. This paper aims to provide a survey of related work to provide guidance to novice researchers and highlight relevant study contributions. The literature has been grouped into “visual analysis techniques” and “procedural analysis techniques”. Visual analysis techniques evaluate offline samples of a graphomotor response after completion. On the other hand, procedural analysis techniques focus on the dynamic processes involved in producing a graphomotor reaction. Since the primary goal of both families of strategies is to represent domain knowledge effectively, the paper also outlines the commonly employed handwriting representation and estimation methods presented in the literature and discusses their strengths and weaknesses. It also highlights the limitations of existing processes and the challenges commonly faced when designing such systems. High-level directions for further research conclude the paper.

Leveraging the Potential of Digital Technology for Better Individualized Treatment of Parkinson's Disease

Recent years have witnessed a strongly increasing interest in digital technology within medicine (sensor devices, specific smartphone apps) and specifically also neurology. Quantitative measures derived from digital technology could provide Digital Biomarkers (DMs) enabling a quantitative and continuous monitoring of disease symptoms, also outside clinics. This includes the possibility to continuously and sensitively monitor the response to treatment, hence opening the opportunity to adapt medication pathways quickly. In addition, DMs may in the future allow early diagnosis, stratification of patient subgroups and prediction of clinical outcomes. Thus, DMs could complement or in certain cases even replace classical examiner-based outcome measures and molecular biomarkers measured in cerebral spinal fluid, blood, urine, saliva, or other body liquids. Altogether, DMs could play a prominent role in the emerging field of precision medicine. However, realizing this vision requires dedicated research. First, advanced data analytical methods need to be developed and applied, which extract candidate DMs from raw signals. Second, these candidate DMs need to be validated by (a) showing their correlation to established clinical outcome measures, and (b) demonstrating their diagnostic and/or prognostic value compared to established biomarkers. These points again require the use of advanced data analytical methods, including machine learning. In addition, the arising ethical, legal and social questions associated with the collection and processing of sensitive patient data and the use of machine learning methods to analyze these data for better individualized treatment of the disease, must be considered thoroughly. Using Parkinson's Disease (PD) as a prime example of a complex multifactorial disorder, the purpose of this article is to critically review the current state of research regarding the use of DMs, discuss open challenges and highlight emerging new directions.

Predicting Parkinson's disease using gradient boosting decision tree models with electroencephalography signals

INTRODUCTION

Parkinson's disease (PD) is a neurodegenerative disorder with only symptomatic treatments currently available. Although correct, early diagnoses of PD are important, the existing diagnostic method based on pathologic examinations only has an accuracy of approximately 80.6%. Although electroencephalography (EEG)-based assistive technology has been introduced, it has been difficult to implement in practice due to the high computational complexity and low accuracy of the analysis methods. This study proposed a fast, accurate PD prediction method using the Hjorth parameter and the gradient boosting decision tree (GBDT) algorithm.

METHOD

We used an open EEG dataset with 41 PD patients and 41 healthy controls (HCs); EEG signals were recorded from participants at the University of New Mexico (PD: 27 vs. HC: 27) and University of Iowa (PD: 14 vs. HC: 14). We explored the analytic time segment and frequency range in which the Hjorth parameter best represents the EEG characteristics of PD patients.

RESULTS

Our best model (CatBoost-based) distinguished PD patients from controls with an accuracy of 89.3%, an area under the receiver operating characteristics curve (AUC) of 0.912, an F-score of 0.903, and an odds ratio of 115.5. These results showed that our models outperformed those of all other previous works and were even superior to previously known pathologic examination-based diagnoses with long-term follow-up (accuracy = 83.9%).

CONCLUSION

The proposed methods are expected to be utilized as an effective method for improving the diagnosis of PD.

SPECT Image Features for Early Detection of Parkinson's Disease using Machine Learning Methods

Millions of people around the world suffer from Parkinson's disease, a neurodegenerative disorder with no remedy. Currently, the best response to interventions is achieved when the disease is diagnosed at an early stage. Supervised machine learning models are a common approach to assist early diagnosis from clinical data, but their performance is highly dependent on available example data and selected input features. In this study, we explore 23 single photon emission computed tomography (SPECT) image features for the early diagnosis of Parkinson's disease on 646 subjects. We achieve 94 % balanced classification accuracy in independent test data using the full feature space and show that matching accuracy can be achieved with only eight features, including original features introduced in this study. All the presented features can be generated using a routinely available clinical software and are therefore straightforward to extract and apply.

A Machine-Learning-Based Assessment Method for Early-Stage Neurocognitive Impairment by an Immersive Virtual Supermarket

Alzheimer's disease (AD) is a neurodegenerative disorder. Though it is not yet curable or reversible, research has shown that clinical intervention or intensive cognitive training at an early stage may effectively delay the progress of the disease. As a result, screening populations with mild cognitive impairment (MCI) or early AD via efficient, effective and low-cost cognitive assessments is important. Currently, a cognitive assessment relies mostly on cognitive tests, such as the Mini-Mental State Examination (MMSE) or the Montreal Cognitive Assessment (MoCA), which must be performed by therapists. Also, cognitive functions can be divided into a variety of dimensions, such as memory, attention, executive function, visual spatial and so on. Executive functions (EF), also known as executive control or cognitive control, refer to a set of skills necessary to perform higher-order cognitive processes, including working memory, planning, attention, cognitive flexibility, and inhibitory control. Along with the fast progress of virtual reality (VR) and artificial intelligence (AI), this study proposes an intelligent assessment method aimed at assessing executive functions. Utilizing machine learning to develop an automatic evidence-based assessment model, behavioral information is acquired through performing executive-function tasks in a VR supermarket. Clinical trials were performed individuals with MCI or early AD and six healthy participants. Statistical analysis showed that 45 out of 46 indices derived from behavioral information were found to differ significantly between individuals with neurocognitive disorder and healthy participants. This analysis indicates these indices may be potential bio-markers. Further, machine-learning methods were applied to build classifiers that differentiate between individuals with MCI or early AD and healthy participants. The accuracy of the classifier is up to 100%, demonstrating the derived features from the VR system were highly related to diagnosis of individuals with MCI or early AD.

Screening of Parkinson's Disease Using Geometric Features Extracted from Spiral Drawings

Conventional means of Parkinson’s Disease (PD) screening rely on qualitative tests typically administered by trained neurologists. Tablet technologies that enable data collection during handwriting and drawing tasks may provide low-cost, portable, and instantaneous quantitative methods for high-throughput PD screening. However, past efforts to use data from tablet-based drawing processes to distinguish between PD and control populations have demonstrated only moderate classification ability. Focusing on digitized drawings of Archimedean spirals, the present study utilized data from the open-access ParkinsonHW dataset to improve existing PD drawing diagnostic pipelines. Random forest classifiers were constructed using previously documented features and highly-predictive, newly-proposed features that leverage the many unique mathematical characteristics of the Archimedean spiral. This approach yielded an AUC of 0.999 on the particular dataset we tested on, and more importantly identified interpretable features with good promise for generalization across diverse patient cohorts. It demonstrated the potency of mathematical relationships inherent to the drawing shape and the usefulness of sparse feature sets and simple models, which further enhance interpretability, in the face of limited sample size. The results of this study also inform suggestions for future drawing task design and data analytics (feature extraction, shape selection, task diversity, drawing templates, and data sharing).

A deep explainable artificial intelligent framework for neurological disorders discrimination

Pathological hand tremor (PHT) is a common symptom of Parkinson's disease (PD) and essential tremor (ET), which affects manual targeting, motor coordination, and movement kinetics. Effective treatment and management of the symptoms relies on the correct and in-time diagnosis of the affected individuals, where the characteristics of PHT serve as an imperative metric for this purpose. Due to the overlapping features of the corresponding symptoms, however, a high level of expertise and specialized diagnostic methodologies are required to correctly distinguish PD from ET. In this work, we propose the data-driven [Formula: see text] model, which processes the kinematics of the hand in the affected individuals and classifies the patients into PD or ET. [Formula: see text] is trained over 90 hours of hand motion signals consisting of 250 tremor assessments from 81 patients, recorded at the London Movement Disorders Centre, ON, Canada. The [Formula: see text] outperforms its state-of-the-art counterparts achieving exceptional differential diagnosis accuracy of [Formula: see text]. In addition, using the explainability and interpretability measures for machine learning models, clinically viable and statistically significant insights on how the data-driven model discriminates between the two groups of patients are achieved.

Artificial intelligence in neurodegenerative diseases: A review of available tools with a focus on machine learning techniques

Neurodegenerative diseases have shown an increasing incidence in the older population in recent years. A significant amount of research has been conducted to characterize these diseases. Computational methods, and particularly machine learning techniques, are now very useful tools in helping and improving the diagnosis as well as the disease monitoring process. In this paper, we provide an in-depth review on existing computational approaches used in the whole neurodegenerative spectrum, namely for Alzheimer's, Parkinson's, and Huntington's Diseases, Amyotrophic Lateral Sclerosis, and Multiple System Atrophy. We propose a taxonomy of the specific clinical features, and of the existing computational methods. We provide a detailed analysis of the various modalities and decision systems employed for each disease. We identify and present the sleep disorders which are present in various diseases and which represent an important asset for onset detection. We overview the existing data set resources and evaluation metrics. Finally, we identify current remaining open challenges and discuss future perspectives.

CLASSIFICATION OF HEALTHY PEOPLE AND PD PATIENTS USING TAKAGI–SUGENO FUZZY MODEL-BASED INSTANCE SELECTION AND WAVELET TRANSFORMS

In this study, a new instance selection method that combines the neural network with weighted fuzzy memberships (NEWFM) and Takagi–Sugeno (T–S) fuzzy model was proposed to improve the classification accuracy of healthy people and Parkinson’s disease (PD) patients. In order to evaluate the proposed instance selection for the classification accuracy of healthy people and PD patients, foot pressure data were collected from healthy people and PD patients as experimental data. This study uses wavelet transforms (WTs) to remove the noise from the foot pressure data in preprocessing step. The proposed instance selection method is an algorithm that selects instances using both weighted mean defuzzification (WMD) in the T–S fuzzy model and the confidence interval of a normal distribution used in statistics. The classification accuracy was compared before and after instance selection was applied to prove the superiority of instance selection. Classification accuracy before and after instance selection was 77.33% and 78.19%, respectively. The classification accuracy after instance selection exhibited a higher classification accuracy than that before instance selection by 0.86%. Further, McNemar’s test, which is used in statistics, was employed to show the difference in classification accuracy before and after instance selection was applied. The results of the McNemar’s test revealed that the probability of significance was smaller than 0.05, which reaffirmed that the classification accuracy was better when instance selection was applied than when instance selection was not applied. NEWFM includes the bounded sum of weighted fuzzy memberships (BSWFMs) that can easily show the differences in the graphically distinct characteristics between healthy people and PD patients. This study proposes new technique that NEWFM can detect PD patients from foot pressure data by the BSWFMs embedded in devices or systems.

Telemonitoring Parkinson's disease using machine learning by combining tremor and voice analysis

BACKGROUND

With the growing number of the aged population, the number of Parkinson's disease (PD) affected people is also mounting. Unfortunately, due to insufficient resources and awareness in underdeveloped countries, proper and timely PD detection is highly challenged. Besides, all PD patients' symptoms are neither the same nor they all become pronounced at the same stage of the illness. Therefore, this work aims to combine more than one symptom (rest tremor and voice degradation) by collecting data remotely using smartphones and detect PD with the help of a cloud-based machine learning system for telemonitoring the PD patients in the developing countries.

METHOD

This proposed system receives rest tremor and vowel phonation data acquired by smartphones with built-in accelerometer and voice recorder sensors. The data are primarily collected from diagnosed PD patients and healthy people for building and optimizing machine learning models that exhibit higher performance. After that, data from newly suspected PD patients are collected, and the trained algorithms are evaluated to detect PD. Based on the majority-vote from those algorithms, PD-detected patients are connected with a nearby neurologist for consultation. Upon receiving patients' feedback after being diagnosed by the neurologist, the system may update the model by retraining using the latest data. Also, the system requests the detected patients periodically to upload new data to track their disease progress.

RESULT

The highest accuracy in PD detection using offline data was [Formula: see text] from voice data and [Formula: see text] from tremor data when used separately. In both cases, k-nearest neighbors (kNN) gave the highest accuracy over support vector machine (SVM) and naive Bayes (NB). The application of maximum relevance minimum redundancy (MRMR) feature selection method showed that by selecting different feature sets based on the patient's gender, we could improve the detection accuracy. This study's novelty is the application of ensemble averaging on the combined decisions generated from the analysis of voice and tremor data. The average accuracy of PD detection becomes [Formula: see text] when ensemble averaging was performed on majority-vote from kNN, SVM, and NB.

CONCLUSION

The proposed system can detect PD using a cloud-based system for computation, data preserving, and regular monitoring of voice and tremor samples captured by smartphones. Thus, this system can be a solution for healthcare authorities to ensure the older population's accessibility to a better medical diagnosis system in the developing countries, especially in the pandemic situation like COVID-19, when in-person monitoring is minimal.

Intelligent Sensory Pen for Aiding in the Diagnosis of Parkinson's Disease from Dynamic Handwriting Analysis

In this paper, we propose a pen device capable of detecting specific features from dynamic handwriting tests for aiding on automatic Parkinson’s disease identification. The method used in this work uses machine learning to compare the raw signals from different sensors in the device coupled to a pen and extract relevant information such as tremors and hand acceleration to diagnose the patient clinically. Additionally, the datasets composed of raw signals from healthy and Parkinson’s disease patients acquired here are made available to further contribute to research related to this topic.

Data-Driven Models for Objective Grading Improvement of Parkinson's Disease

Parkinson's disease (PD) is a progressive disorder of the central nervous system that causes motor dysfunctions in affected patients. Objective assessment of symptoms can support neurologists in fine evaluations, improving patients' quality of care. Herein, this study aimed to develop data-driven models based on regression algorithms to investigate the potential of kinematic features to predict PD severity levels. Sixty-four patients with PD (PwPD) and 50 healthy subjects of control (HC) were asked to perform 13 motor tasks from the MDS-UPDRS III while wearing wearable inertial sensors. Simultaneously, the clinician provided the evaluation of the tasks based on the MDS-UPDRS scores. One hundred-ninety kinematic features were extracted from the inertial motor data. Data processing and statistical analysis identified a set of parameters able to distinguish between HC and PwPD. Then, multiple feature selection methods allowed selecting the best subset of parameters for obtaining the greatest accuracy when used as input for several predicting regression algorithms. The maximum correlation coefficient, equal to 0.814, was obtained with the adaptive neuro-fuzzy inference system (ANFIS). Therefore, this predictive model could be useful as a decision support system for a reliable objective assessment of PD severity levels based on motion performance, improving patients monitoring over time.

NEURO-FUZZY SYSTEM FOR DETECTING PD PATIENTS BASED ON EUCLID DISTANCE, FFT, AND PCA

This study proposes a method to distinguish between healthy people and Parkinson’s disease patients using sole pressure sensor data, neural network with weighted fuzzy membership (NEWFM), and preprocessing techniques. The preprocessing techniques include fast Fourier transform (FFT), Euclidean distance, and principal component analysis (PCA), to remove noise in the data for performance enhancement. To make the features usable as inputs for NEWFM, the Euclidean distances between the left and right sole pressure sensor data were used at the first step. In the second step, the frequency scales of the Euclidean distances extracted in the first step were divided into individual scales by the FFT using the Hamming method. In the final step, 1–15 dimensions were extracted as the features of NEWFM from the individual scales by the FFT extracted in the second step by the PCA. An accuracy of 75.90% was acquired from the eight dimensions as the inputs of NEWFM.

A Heterogeneous Sensing Suite for Multisymptom Quantification of Parkinson's Disease

Parkinson's disease (PD) is the second most common neurodegenerative disease affecting millions worldwide. Bespoke subject-specific treatment (medication or deep brain stimulation (DBS)) is critical for management, yet depends on precise assessment cardinal PD symptoms - bradykinesia, rigidity and tremor. Clinician diagnosis is the basis of treatment, yet it allows only a cross-sectional assessment of symptoms which can vary on an hourly basis and is liable to inter- and intra-rater subjectivity across human examiners. Automated symptomatic assessment has attracted significant interest to optimise treatment regimens between clinician visits, however, no wearable has the capacity to simultaneously assess all three cardinal symptoms. Challenges in the measurement of rigidity, mapping muscle activity out-of-clinic and sensor fusion have inhibited translation. In this study, we address all through a novel wearable sensor system and machine learning algorithms. The sensor system is composed of a force-sensor, three inertial measurement units (IMUs) and four custom mechanomyography (MMG) sensors. The system was tested in its capacity to predict Unified Parkinson's Disease Rating Scale (UPDRS) scores based on quantitative assessment of bradykinesia, rigidity and tremor in PD patients. 23 PD patients were tested with the sensor system in parallel with exams conducted by treating clinicians and 10 healthy subjects were recruited as a comparison control group. Results prove the system accurately predicts UPDRS scores for all symptoms (85.4% match on average with physician assessment) and discriminates between healthy subjects and PD patients (96.6% on average). MMG features can also be used for remote monitoring of severity and fluctuations in PD symptoms out-of-clinic. This closed-loop feedback system enables individually tailored and regularly updated treatment, facilitating better outcomes for a very large patient population.

A novel approach combining temporal and spectral features of Arabic online handwriting for Parkinson's disease prediction

BACKGROUND

Parkinson's disease (PD) affects millions of people worldwide, and it is predicted that this pathology will gravely increase in the next few years. Unfortunately, there's currently no cure for this disease, indeed an early diagnosis of Parkinson's disease can help to better manage its symptoms and its evolution. One of the most frequent abilities and usually also the first manifestation of Parkinson's disease is alteration of handwriting.

NEW METHOD

We propose a novel method to detect Parkinson's disease, based on the segmentation of the online handwritten text into lines. Indeed, we propose to compare Parkinson's disease patients and healthy controls, based on the full dynamics of new temporal and spectral features. Three classifiers were used, K-Nearest Neighbors, Support Vector Machine and Decision Trees. The performances of these three classifiers were estimated using a stratified nested 10 cross-validation. All the models in this study have been evaluated using classification accuracy, balanced accuracy, sensitivity, specificity, F-Score and Matthews Correlation Coefficient.

RESULTS

An accuracy of 92.86 % was obtained with Decision Trees classifier in the last line. The new categories of spectral and temporal features gave the best classification performances in comparison to the basic statistical features.

COMPARISON WITH EXISTING METHODS

Previous studies have only focused on words or sentences. This is the first study to deal with the analysis of a text composed of several lines.

CONCLUSION

The last line discriminates at best between Parkinson's disease patients and healthy controls. This obtained result has further strengthened our hypothesis concerning the fatigue occurring while writing in PD patients.

PHTNet: Characterization and Deep Mining of Involuntary Pathological Hand Tremor using Recurrent Neural Network Models

The global aging phenomenon has increased the number of individuals with age-related neurological movement disorders including Parkinson's Disease (PD) and Essential Tremor (ET). Pathological Hand Tremor (PHT), which is considered among the most common motor symptoms of such disorders, can severely affect patients' independence and quality of life. To develop advanced rehabilitation and assistive technologies, accurate estimation/prediction of nonstationary PHT is critical, however, the required level of accuracy has not yet been achieved. The lack of sizable datasets and generalizable modeling techniques that can fully represent the spectrotemporal characteristics of PHT have been a critical bottleneck in attaining this goal. This paper addresses this unmet need through establishing a deep recurrent model to predict and eliminate the PHT component of hand motion. More specifically, we propose a machine learning-based, assumption-free, and real-time PHT elimination framework, the PHTNet, by incorporating deep bidirectional recurrent neural networks. The PHTNet is developed over a hand motion dataset of 81 ET and PD patients collected systematically in a movement disorders clinic over 3 years. The PHTNet is the first intelligent systems model developed on this scale for PHT elimination that maximizes the resolution of estimation and allows for prediction of future and upcoming sub-movements.

Early diagnosis of Parkinson's disease using machine learning algorithms

Parkinson's disease is caused by the disruption of the brain cells that produce substance to allow brain cells to communicate with each other, called dopamine. The cells that produce dopamine in the brain are responsible for the control, adaptation and fluency of movements. When 60-80%of these cells are lost, then enough dopamine is not produced and Parkinson's motor symptoms appear. It is thought that the disease begins many years before the motor (movement related) symptoms and therefore, researchers are looking for ways to recognize the non-motor symptoms that appear early in the disease as early as possible, thereby halting the progression of the disease. In this paper, machine learning based diagnosis of Parkinson's disease is presented. The proposed diagnosis method consists of feature selection and classification processes. Feature Importance and Recursive Feature Elimination methods were considered for feature selection task. Classification and Regression Trees, Artificial Neural Networks, and Support Vector Machines were used for the classification of Parkinson's patients in the experiments. Support Vector Machines with Recursive Feature Elimination was shown to perform better than the other methods. 93.84% accuracy was achieved with the least number of voice features for Parkinson's diagnosis.

A new semi-supervised approach for characterizing the Arabic on-line handwriting of Parkinson's disease patients

Parkinson's disease (PD) is the second most common neurodegenerative disease affecting significant portion of elderly population. One of the most frequent hallmarks and the first manifestation of PD is deterioration of handwriting. Since the diagnosis of Parkinson's disease is difficult, researchers have worked to develop a support tool based on algorithms to separate healthy controls from PD patients. On-line handwriting analysis is one of the methods that can be used to diagnose PD. In this study, we aimed to analyze the Arabic Handwriting of 28 Parkinson's disease patients and 28 healthy controls (HCs) who were the same age and have the same intellectual level. We focused on copying an Arabic text task. For each participant we have calculated 1482 features. Based on the most relevant features selected by the Pearson's coefficient correlation, the Hierarchical Ascendant Classification (HAC) was applied and generated 3 clusters of writers. The characterization of these clusters was carried out by using the quantitative and qualitative parameters. The obtained results show that the combination of these two aspects can discriminate at best PD patients from HCs.

Dynamic Handwriting Analysis for Neurodegenerative Disease Assessment: A Literary Review

Studying the effects of neurodegeneration on handwriting has emerged as an interdisciplinary research topic and has attracted considerable interest from psychologists to neuroscientists and from physicians to computer scientists. The complexity of handwriting, in fact, appears to be sensitive to age-related impairments in cognitive functioning; thus, analyzing handwriting in elderly people may facilitate the diagnosis and monitoring of these impairments. A large body of knowledge has been collected in the last thirty years thanks to the advent of new technologies which allow researchers to investigate not only the static characteristics of handwriting but also especially the dynamic aspects of the handwriting process. The present paper aims at providing an overview of the most relevant literature investigating the application of dynamic handwriting analysis in neurodegenerative disease assessment. The focus, in particular, is on Parkinon’s disease (PD) and Alzheimer’s disease (AD), as the two most widespread neurodegenerative disorders. More specifically, the studies taken into account are grouped in accordance with three main research questions: disease insight, disease monitoring, and disease diagnosis. The net result is that dynamic handwriting analysis is a powerful, noninvasive, and low-cost tool for real-time diagnosis and follow-up of PD and AD. In conclusion of the paper, open issues still demanding further research are highlighted.

Parkinson’s Disease Detection from Drawing Movements Using Convolutional Neural Networks

Nowadays, an important research effort in healthcare biometrics is finding accurate biomarkers that allow developing medical-decision support tools. These tools help to detect and supervise illnesses like Parkinson’s disease (PD). This paper contributes to this effort by analyzing a convolutional neural network (CNN) for PD detection from drawing movements. This CNN includes two parts: feature extraction (convolutional layers) and classification (fully connected layers). The inputs to the CNN are the module of the Fast Fourier’s transform in the range of frequencies between 0 Hz and 25 Hz. We analyzed the discrimination capability of different directions during drawing movements obtaining the best results for X and Y directions. This analysis was performed using a public dataset: Parkinson Disease Spiral Drawings Using Digitized Graphics Tablet dataset. The best results obtained in this work showed an accuracy of 96.5%, a F1-score of 97.7%, and an area under the curve of 99.2%.