Technologies for Assessment of Motor Disorders in Parkinson's Disease: A Review

The Effects of Mindfulness-Based Interventions in People with Parkinson's Disease: A Systematic Review and Meta-Analysis

OBJECTIVES

To examine the effectiveness of mindfulness-based interventions (MBIs) on psychological symptoms, motor symptoms, and quality of life in patients with Parkinson's disease (PD).

METHODS

Published studies in Chinese and English languages, conducted from inception to March 2023, were identified by searching PubMed, Web of Science, Cochrane Library, CINAHL, PsycINFO, and two Chinese electronic databases. The systematic review was performed according to the Preferred Reporting Items for Systematic Reviews and Meta Analyses guidelines.

RESULTS

Twelve studies were selected for quantitative syntheses. The impact of MBIs on reducing depression and anxiety, and improving mindfulness and quality of life in PD patients was statistically significant compared to the control group. However, no statistically significant effect on motor symptoms was observed. Subgroup analysis indicated that participants from Asia, those who received face-to-face sessions, and those whose sessions lasted 1.5 hours showed a more positive effect than other subgroups.

CONCLUSIONS

Patients with PD may benefit from MBIs to improve psychological symptoms and quality of life. MBIs represent a pivotal non-pharmacological therapeutic approach in clinical practice.

CLINICAL IMPLICATIONS

MBIs confer positive improvements in psychological well-being and quality of life in PD patients. However, it remains challenging to conclusively determine their efficacy in addressing motor symptoms.

Parkinson's disease and levodopa-induced dyskinesias: a quantitative analysis through 99mTc-TRODAT-1 SPECT imaging of the brain

Objective

To compare the dopamine transporter (DAT) density with other risk factors for L-DOPA-induced dyskinesia (LID) in patients with Parkinson's disease (PD), with and without LID.

Materials and Methods

We evaluated 67 subjects: 44 patients with idiopathic PD of varying degrees of severity (PD group), and 23 healthy age-matched volunteers (control group). Among the 44 patients in the PD group, 29 were male and the following means were recorded at baseline: age, 59 ± 7 years; disease duration, 10 ± 6 years; Hoehn and Yahr (H&Y) stage, 2.16 ± 0.65; and Unified Parkinson's Disease Rating Scale part III (UPDRS III) score, 29.74 ± 17.79. All subjects underwent 99mTc-TRODAT-1 SPECT. We also calculated specific uptake ratios or binding potentials in the striatum.

Results

The DAT density in the ipsilateral and contralateral striata was lower in the PD group. The variables disease duration, L-DOPA dosage, doses per day, L-DOPA effect duration time, H&Y stage, and UPDRS III score explained the occurrence of LID. The DAT density in the ipsilateral striatum, contralateral striatum, and caudate nucleus was lower in the patients with LID than in those without.

Conclusion

Our findings suggest that presynaptic dopaminergic denervation is associated with LID in individuals with PD.

Intraoperative Quantification of MDS-UPDRS Tremor Measurements Using 3D Accelerometry: A Pilot Study

The most frequently used method for evaluating tremor in Parkinson’s disease (PD) is currently the internationally standardized Movement Disorder Society—Unified PD Rating Scale (MDS-UPDRS). However, the MDS-UPDRS is associated with limitations, such as its inherent subjectivity and reliance on experienced raters. Objective motor measurements using accelerometry may overcome the shortcomings of visually scored scales. Therefore, the current study focuses on translating the MDS-UPDRS tremor tests into an objective scoring method using 3D accelerometry. An algorithm to measure and classify tremor according to MDS-UPDRS criteria is proposed. For this study, 28 PD patients undergoing neurosurgical treatment and 26 healthy control subjects were included. Both groups underwent MDS-UPDRS tests to rate tremor severity, while accelerometric measurements were performed at the index fingers. All measurements were performed in an off-medication state. Quantitative measures were calculated from the 3D acceleration data, such as tremor amplitude and area-under-the-curve of power in the 4−6 Hz range. Agreement between MDS-UPDRS tremor scores and objective accelerometric scores was investigated. The trends were consistent with the logarithmic relationship between tremor amplitude and MDS-UPDRS score reported in previous studies. The accelerometric scores showed a substantial concordance (>69.6%) with the MDS-UPDRS ratings. However, accelerometric kinetic tremor measures poorly associated with the given MDS-UPDRS scores (R2 < 0.3), mainly due to the noise between 4 and 6 Hz found in the healthy controls. This study shows that MDS-UDPRS tremor tests can be translated to objective accelerometric measurements. However, discrepancies were found between accelerometric kinetic tremor measures and MDS-UDPRS ratings. This technology has the potential to reduce rater dependency of MDS-UPDRS measurements and allow more objective intraoperative monitoring of tremor.

Medication adherence status among patients with neurological conditions and its association with quality of life

Background/Aim

Medication non-adherence may cause significant morbidity and mortality in patients with chronic diseases and may increase the economic burden on the healthcare system. The prevalence of neurological disorders is increasing in Malaysia; however, comprehensive data on medication adherence among Malaysian patients with these disorders is limited. This study was conducted to determine the association of medication non-adherence with quality of life in patients with neurological problems.

Methods

A cross-sectional survey was performed in 370 patients diagnosed with epilepsy, Parkinson's disease, stroke and Alzheimer's disease at Neurology clinic. Patients aged 18 years or older, without documented physical or psychiatric illness such as schizophrenia and major depression, were included. Patient-administered questionnaires, such as the Malaysian Medication Adherence Scale and Medication Possession Ratio were used to determine medication adherence. An established EQ-5D-3L questionnaire was used to determine quality of life. Data were analysed using descriptive and inferential analysis.

Results

The overall prevalence of medication non-adherence among patients with neurological disorders was 59.2%. Among these neuromedical diseases, 69.2% (n = 9/13) of Alzheimer's disease, 66.7% (n = 98/147) of epilepsy, 62.1% (n = 36/58) of Parkinson's disease and 48.7% (n = 74/152) of stroke patients were found non-adherent. There was a significant difference in EQ-5D index scores (p = 0.041) between adherent and non-adherent patients.

Conclusion

A high prevalence of medication non-adherence was found among patients with neurological disorders. The rate of non-adherence varied among different neurological conditions. There was a significant difference in quality of life between adherent and non-adherent patients.

Wearable Health Technology to Quantify the Functional Impact of Peripheral Neuropathy on Mobility in Parkinson's Disease: A Systematic Review

The occurrence of peripheral neuropathy (PNP) is often observed in Parkinson’s disease (PD) patients with a prevalence up to 55%, leading to more prominent functional deficits. Motor assessment with mobile health technologies allows high sensitivity and accuracy and is widely adopted in PD, but scarcely used for PNP assessments. This review provides a comprehensive overview of the methodologies and the most relevant features to investigate PNP and PD motor deficits with wearables. Because of the lack of studies investigating motor impairments in this specific subset of PNP-PD patients, Pubmed, Scopus, and Web of Science electronic databases were used to summarize the state of the art on PNP motor assessment with wearable technology and compare it with the existing evidence on PD. A total of 24 papers on PNP and 13 on PD were selected for data extraction: The main characteristics were described, highlighting major findings, clinical applications, and the most relevant features. The information from both groups (PNP and PD) was merged for defining future directions for the assessment of PNP-PD patients with wearable technology. We established suggestions on the assessment protocol aiming at accurate patient monitoring, targeting personalized treatments and strategies to prevent falls and to investigate PD and PNP motor characteristics.

Nanocytometer for smart analysis of peripheral blood and acute myeloid leukemia: a pilot study

We realize an ultracompact nanocytometer for real-time impedimetric detection and classification of subpopulations of living cells. Nanoscopic nanowires in a microfluidic channel act as nanocapacitors and measure in real time the change of the amplitude and phase of the output voltage and, thus, the electrical properties of living cells. We perform the cell classification in the human peripheral blood (PBMC) and demonstrate for the first time the possibility to discriminate monocytes and subpopulations of lymphocytes in a label-free format. Further, we demonstrate that the PBMC of acute myeloid leukemia and healthy samples grant the label free identification of the disease. Using the algorithm based on machine learning, we generated specific data patterns to discriminate healthy donors and leukemia patients. Such a solution has the potential to improve the traditional diagnostics approaches with respect to the overall cost and time effort, in a label-free format, and restrictions of the complex data analysis.

A Mobile Cloud based IoMT Framework for Automated Health Assessment and Management

In recent years, there has been growing interest in the use of mobile cloud and Internet of Medical Things (IoMT) in automated diagnosis and health monitoring. These applications play a significant role in providing smart medical services in modern healthcare systems. In this paper, we deploy a mobile cloud-based IoMT scheme to monitor the progression of a neurological disorder using a test of motor coordination. The computing and storage capabilities of cloud server is employed to facilitate the estimation of the severity levels given by an established quantitative assessment. An Android application is used for data acquisition and communication with the cloud. Further, we integrate the proposed system with a data sharing framework in a blockchain network as an innovative solution that allows reliable data exchange among healthcare users. The experimental results show the feasibility of implementing the proposed system in a wide range of healthcare applications.

A multiple motion sensors index for motor state quantification in Parkinson's disease

AIM

To construct a Treatment Response Index from Multiple Sensors (TRIMS) for quantification of motor state in patients with Parkinson's disease (PD) during a single levodopa dose. Another aim was to compare TRIMS to sensor indexes derived from individual motor tasks.

METHOD

Nineteen PD patients performed three motor tests including leg agility, pronation-supination movement of hands, and walking in a clinic while wearing inertial measurement unit sensors on their wrists and ankles. They performed the tests repeatedly before and after taking 150% of their individual oral levodopa-carbidopa equivalent morning dose.Three neurologists blinded to treatment status, viewed patients' videos and rated their motor symptoms, dyskinesia, overall motor state based on selected items of Unified PD Rating Scale (UPDRS) part III, Dyskinesia scale, and Treatment Response Scale (TRS). To build TRIMS, out of initially 178 extracted features from upper- and lower-limbs data, 39 features were selected by stepwise regression method and were used as input to support vector machines to be mapped to mean reference TRS scores using 10-fold cross-validation method. Test-retest reliability, responsiveness to medication, and correlation to TRS as well as other UPDRS items were evaluated for TRIMS.

RESULTS

The correlation of TRIMS with TRS was 0.93. TRIMS had good test-retest reliability (ICC = 0.83). Responsiveness of the TRIMS to medication was good compared to TRS indicating its power in capturing the treatment effects. TRIMS was highly correlated to dyskinesia (R = 0.85), bradykinesia (R = 0.84) and gait (R = 0.79) UPDRS items. Correlation of sensor index from the upper-limb to TRS was 0.89.

CONCLUSION

Using the fusion of upper- and lower-limbs sensor data to construct TRIMS provided accurate PD motor states estimation and responsive to treatment. In addition, quantification of upper-limb sensor data during walking test provided strong results.

Parkinson's Disease: Available Clinical and Promising Omics Tests for Diagnostics, Disease Risk Assessment, and Pharmacotherapy Personalization

Parkinson's disease is the second most frequent neurodegenerative disease, representing a significant medical and socio-economic problem. Modern medicine still has no answer to the question of why Parkinson's disease develops and whether it is possible to develop an effective system of prevention. Therefore, active work is currently underway to find ways to assess the risks of the disease, as well as a means to extend the life of patients and improve its quality. Modern studies aim to create a method of assessing the risk of occurrence of Parkinson's disease (PD), to search for the specific ways of correction of biochemical disorders occurring in the prodromal stage of Parkinson's disease, and to personalize approaches to antiparkinsonian pharmacotherapy. In this review, we summarized all available clinically approved tests and techniques for PD diagnostics. Then, we reviewed major improvements and recent advancements in genomics, transcriptomics, and proteomics studies and application of metabolomics in PD research, and discussed the major metabolomics findings for diagnostics and therapy of the disease.

An Optimized Brain-Based Algorithm for Classifying Parkinson’s Disease

During the last years, highly-recognized computational intelligence techniques have been proposed to treat classification problems. These automatic learning approaches lead to the most recent researches because they exhibit outstanding results. Nevertheless, to achieve this performance, artificial learning methods firstly require fine tuning of their parameters and then they need to work with the best-generated model. This process usually needs an expert user for supervising the algorithm’s performance. In this paper, we propose an optimized Extreme Learning Machine by using the Bat Algorithm, which boosts the training phase of the machine learning method to increase the accuracy, and decreasing or keeping the loss in the learning phase. To evaluate our proposal, we use the Parkinson’s Disease audio dataset taken from UCI Machine Learning Repository. Parkinson’s disease is a neurodegenerative disorder that affects over 10 million people. Although its diagnosis is through motor symptoms, it is possible to evidence the disorder through variations in the speech using machine learning techniques. Results suggest that using the bio-inspired optimization algorithm for adjusting the parameters of the Extreme Learning Machine is a real alternative for improving its performance. During the validation phase, the classification process for Parkinson’s Disease achieves a maximum accuracy of 96.74% and a minimum loss of 3.27%.

Designing a mHealth clinical decision support system for Parkinson's disease: a theoretically grounded user needs approach

Background

Despite the established evidence and theoretical advances explaining human judgments under uncertainty, developments of mobile health (mHealth) Clinical Decision Support Systems (CDSS) have not explicitly applied the psychology of decision making to the study of user needs. We report on a user needs approach to develop a prototype of a mHealth CDSS for Parkinson’s disease (PD), which is theoretically grounded in the psychological literature about expert decision making and judgement under uncertainty.

Methods

A suite of user needs studies was conducted in 4 European countries (Greece, Italy, Slovenia, the UK) prior to the development of PD_Manager, a mHealth-based CDSS designed for Parkinson’s disease, using wireless technology. Study 1 undertook Hierarchical Task Analysis (HTA) including elicitation of user needs, cognitive demands and perceived risks/benefits (ethical considerations) associated with the proposed CDSS, through structured interviews of prescribing clinicians (N = 47). Study 2 carried out computational modelling of prescribing clinicians’ (N = 12) decision strategies based on social judgment theory. Study 3 was a vignette study of prescribing clinicians’ (N = 18) willingness to change treatment based on either self-reported symptoms data, devices-generated symptoms data or combinations of both.

Results

Study 1 indicated that system development should move away from the traditional silos of ‘motor’ and ‘non-motor’ symptom evaluations and suggest that presenting data on symptoms according to goal-based domains would be the most beneficial approach, the most important being patients’ overall Quality of Life (QoL). The computational modelling in Study 2 extrapolated different factor combinations when making judgements about different questions. Study 3 indicated that the clinicians were equally likely to change the care plan based on information about the change in the patient’s condition from the patient’s self-report and the wearable devices.

Conclusions

Based on our approach, we could formulate the following principles of mHealth design: 1) enabling shared decision making between the clinician, patient and the carer; 2) flexibility that accounts for diagnostic and treatment variation among clinicians; 3) monitoring of information integration from multiple sources. Our approach highlighted the central importance of the patient-clinician relationship in clinical decision making and the relevance of theoretical as opposed to algorithm (technology)-based modelling of human judgment.

Contrast and Homogeneity Feature Analysis for Classifying Tremor Levels in Parkinson's Disease Patients

Early detection of different levels of tremors helps to obtain a more accurate diagnosis of Parkinson’s disease and to increase the therapy options for a better quality of life for patients. This work proposes a non-invasive strategy to measure the severity of tremors with the aim of diagnosing one of the first three levels of Parkinson’s disease by the Unified Parkinson’s Disease Rating Scale (UPDRS). A tremor being an involuntary motion that mainly appears in the hands; the dataset is acquired using a leap motion controller that measures 3D coordinates of each finger and the palmar region. Texture features are computed using sum and difference of histograms (SDH) to characterize the dataset, varying the window size; however, only the most fundamental elements are used in the classification stage. A machine learning classifier provides the final classification results of the tremor level. The effectiveness of our approach is obtained by a set of performance metrics, which are also used to show a comparison between different proposed designs.

Technology-aided assessments of sensorimotor function: current use, barriers and future directions in the view of different stakeholders

BACKGROUND

There is growing interest in the use of technology in neurorehabilitation, from robotic to sensor-based devices. These technologies are believed to be excellent tools for quantitative assessment of sensorimotor ability, addressing the shortcomings of traditional clinical assessments. However, clinical adoption of technology-based assessments is very limited. To understand this apparent contradiction, we sought to gather the points-of-view of different stakeholders in the development and use of technology-aided sensorimotor assessments.

METHODS

A questionnaire regarding motivators, barriers, and the future of technology-aided assessments was prepared and disseminated online. To promote discussion, we present an initial analysis of the dataset; raw responses are provided to the community as Supplementary Material. Average responses within stakeholder groups were compared across groups. Additional questions about respondent's demographics and professional practice were used to obtain a view of the current landscape of sensorimotor assessments and interactions between different stakeholders.

RESULTS

One hundred forty respondents from 23 countries completed the survey. Respondents were a mix of Clinicians (27%), Research Engineers (34%), Basic Scientists (15%), Medical Industry professionals (16%), Patients (2%) and Others (6%). Most respondents were experienced in rehabilitation within their professions (67% with > 5 years of experience), and had exposure to technology-aided assessments (97% of respondents). In general, stakeholders agreed on reasons for performing assessments, level of details required, current bottlenecks, and future directions. However, there were disagreements between and within stakeholders in aspects such as frequency of assessments, and important factors hindering adoption of technology-aided assessments, e.g., Clinicians' top factor was cost, while Research Engineers indicated device-dependent factors and lack of standardization. Overall, lack of time, cost, lack of standardization and poor understanding/lack of interpretability were the major factors hindering the adoption of technology-aided assessments in clinical practice. Reimbursement and standardization of technology-aided assessments were rated as the top two activities to pursue in the coming years to promote the field of technology-aided sensorimotor assessments.

CONCLUSIONS

There is an urgent need for standardization in technology-aided assessments. These efforts should be accompanied by quality cross-disciplinary activities, education and alignment of scientific language, to more effectively promote the clinical use of assessment technologies.

TRIAL REGISTRATION

NA; see Declarations section.

Can the Latest Computerized Technologies Revolutionize Conventional Assessment Tools and Therapies for a Neurological Disease? The Example of Parkinson's Disease

Dramatic breakthroughs in the treatment and assessment of neurological diseases are lacking. We believe that conventional methods have several limitations. Computerized technologies, including virtual reality, augmented reality, and robot assistant systems, are advancing at a rapid pace. In this study, we used Parkinson's disease (PD) as an example to elucidate how the latest computerized technologies can improve the diagnosis and treatment of neurological diseases. Dopaminergic medication and deep brain stimulation remain the most effective interventions for treating PD. Subjective scales, such as the Unified Parkinson's Disease Rating Scale and the Hoehn and Yahr stage, are still the most widely used assessments. Wearable sensors, virtual reality, augmented reality, and robot assistant systems are increasingly being used for evaluation of patients with PD. The use of such computerized technologies can result in safe, objective, real-time behavioral assessments. Our experiences and understanding of PD have led us to believe that such technologies can provide real-time assessment, which will revolutionize the traditional assessment and treatment of PD. New technologies are desired that can revolutionize PD treatment and facilitate real-time adjustment of treatment based on motor fluctuations, such as telediagnosis systems and "smart treatment systems." The use of these technologies will substantially improve both the assessment and the treatment of neurological diseases before next-generation treatments, such as stem cell and genetic therapy, and next-generation assessments, can be clinically practiced, although the current level of artificial intelligence cannot replace the role of clinicians.

Parameters of Surface Electromyogram Suggest That Dry Immersion Relieves Motor Symptoms in Patients With Parkinsonism

Dry immersion (DI) is acknowledged as a reliable space flight analog condition. At DI, subject is immersed in water being wrapped in a waterproof film to imitate microgravity (μG). Microgravity is known to decrease muscle tone due to deprivation of the sensory stimuli that activate the reflexes that keep up the muscle tone. In contrary, parkinsonian patients are characterized by elevated muscle tone, or rigidity, along with rest tremor and akinesia. We hypothesized that DI can diminish the elevated muscle tone and/or the tremor in parkinsonian patients. Fourteen patients with Parkinson's disease (PD, 10 males, 4 females, 47-73 years) and 5 patients with vascular parkinsonism (VP, 1 male, 4 females, 65-72 years) participated in the study. To evaluate the effect of DI on muscles' functioning, we compared parameters of surface electromyogram (sEMG) measured before and after a single 45-min long immersion session. The sEMG recordings were made from the biceps brachii muscle, bilaterally. Each recording was repeated with the following loading conditions: with arms hanging freely down, and with 0, 1, and 2 kg loading on each hand with elbows flexed to 90°. The sEMG parameters comprised of amplitude, median frequency, time of decay of mutual information, sample entropy, correlation dimension, recurrence rate, and determinism of sEMG. These parameters have earlier been proved to be sensitive to PD severity. We used the Wilcoxon test to decide which parameters were statistically significantly different before and after the dry immersion. Accepting the p < 0.05 significance level, amplitude, time of decay of mutual information, recurrence rate, and determinism tended to decrease, while median frequency and sample entropy of sEMG tended to increase after the DI. The most statistically significant change was for the determinism of sEMG from the left biceps with 1 kg loading, which decreased for 84% of the patients. The results suggest that DI can promptly relieve motor symptoms of parkinsonism. We conclude that DI has strong potential as a rehabilitation method for parkinsonian patients.

Automated Assessment of Movement Impairment in Huntington's Disease

Quantitative assessment of movement impairment in Huntington's disease (HD) is essential to monitoring of disease progression. This paper aimed to develop and validate a novel low cost, objective automated system for the evaluation of upper limb movement impairment in HD in order to eliminate the inconsistency of the assessor and offer a more sensitive, continuous assessment scale. Patients with genetically confirmed HD and healthy controls were recruited to this observational study. Demographic data, including age (years), gender, and unified HD rating scale total motor score (UHDRS-TMS), were recorded. For the purposes of this paper, a modified upper limb motor impairment score (mULMS) was generated from the UHDRS-TMS. All participants completed a brief, standardized clinical assessment of upper limb dexterity while wearing a tri-axial accelerometer on each wrist and on the sternum. The captured acceleration data were used to develop an automatic classification system for discriminating between healthy and HD participants and to automatically generate a continuous movement impairment score (MIS) that reflected the degree of the movement impairment. Data from 48 healthy and 44 HD participants was used to validate the developed system, which achieved 98.78% accuracy in discriminating between healthy and HD participants. The Pearson correlation coefficient between the automatic MIS and the clinician rated mULMS was 0.77 with a p-value < 0.01. The approach presented in this paper demonstrates the possibility of an automated objective, consistent, and sensitive assessment of the HD movement impairment.

Smartphone motor testing to distinguish idiopathic REM sleep behavior disorder, controls, and PD

Objective

We sought to identify motor features that would allow the delineation of individuals with sleep study-confirmed idiopathic REM sleep behavior disorder (iRBD) from controls and Parkinson disease (PD) using a customized smartphone application.

Methods

A total of 334 PD, 104 iRBD, and 84 control participants performed 7 tasks to evaluate voice, balance, gait, finger tapping, reaction time, rest tremor, and postural tremor. Smartphone recordings were collected both in clinic and at home under noncontrolled conditions over several days. All participants underwent detailed parallel in-clinic assessments. Using only the smartphone sensor recordings, we sought to (1) discriminate whether the participant had iRBD or PD and (2) identify which of the above 7 motor tasks were most salient in distinguishing groups.

Results

Statistically significant differences based on these 7 tasks were observed between the 3 groups. For the 3 pairwise discriminatory comparisons, (1) controls vs iRBD, (2) controls vs PD, and (3) iRBD vs PD, the mean sensitivity and specificity values ranged from 84.6% to 91.9%. Postural tremor, rest tremor, and voice were the most discriminatory tasks overall, whereas the reaction time was least discriminatory.

Conclusions

Prodromal forms of PD include the sleep disorder iRBD, where subtle motor impairment can be detected using clinician-based rating scales (e.g., Unified Parkinson's Disease Rating Scale), which may lack the sensitivity to detect and track granular change. Consumer grade smartphones can be used to accurately separate not only iRBD from controls but also iRBD from PD participants, providing a growing consensus for the utility of digital biomarkers in early and prodromal PD.

Automatic Outcome in Manual Dexterity Assessment Using Colour Segmentation and Nearest Neighbour Classifier

Objective assessment of motor function is an important component to evaluating the effectiveness of a rehabilitation process. Such assessments are carried out by clinicians using traditional tests and scales. The Box and Blocks Test (BBT) is one such scale, focusing on manual dexterity evaluation. The score is the maximum number of cubes that a person is able to displace during a time window. In a previous paper, an automated version of the Box and Blocks Test using a Microsoft Kinect sensor was presented, and referred to as the Automated Box and Blocks Test (ABBT). In this paper, the feasibility of ABBT as an automated tool for manual dexterity assessment is discussed. An algorithm, based on image segmentation in CIELab colour space and the Nearest Neighbour (NN) rule, was developed to improve the reliability of automatic cube counting. A pilot study was conducted to assess the hand motor function in people with Parkinson’s disease (PD). Three functional assessments were carried out. The success rate in automatic cube counting was studied by comparing the manual (BBT) and the automatic (ABBT) methods. The additional information provided by the ABBT was analysed to discuss its clinical significance. The results show a high correlation between manual (BBT) and automatic (ABBT) scoring. The lowest average success rate in cube counting for ABBT was 92%. Additionally, the ABBT acquires extra information from the cubes’ displacement, such as the average velocity and the time instants in which the cube was detected. The analysis of this information can be related to indicators of health status (coordination and dexterity). The results showed that the ABBT is a useful tool for automating the assessment of unilateral gross manual dexterity, and provides additional information about the user’s performance.

Reengineering deep brain stimulation for movement disorders: Emerging technologies

Deep brain stimulation (DBS) is a neurosurgical technique, which consists of continuous delivery of an electrical pulse through chronically implanted electrodes connected to a neurostimulator, programmable in amplitude, pulse width, frequency, and stimulation channel. DBS is a promising treatment option for addressing severe and drug-resistant movement disorders. The success of DBS therapy is a combination of surgical implantation techniques, device technology, and clinical programming strategies. Changes in device settings require highly trained and experienced clinicians to achieve maximal therapeutic benefit for each targeted symptom, and optimization of stimulation parameters can take many visits. Thus, the development of innovative DBS technologies that can optimize the clinical implementation of DBS will lead to wider scale utilization. This review aims to present engineering approaches that have the potential to improve clinical outcomes of DBS, focusing on the development novel temporal patterns, innovative electrode designs, computational models to guide stimulation, closed-loop DBS, and remote programming.

Feature selection and extraction for class prediction in dysphonia measures analysis:A case study on Parkinson's disease speech rehabilitation

BACKGROUND

Speech disorders such as dysphonia and dysarthria represent an early and common manifestation of Parkinson's disease. Class prediction is an essential task in automatic speech treatment, particularly in the Parkinson's disease case. Many classification experiments have been performed which focus on the automatic detection of Parkinson's disease patients from healthy speakers but results are still not optimistic. A major problem in accomplishing this task is high dimensionality of speech data.

OBJECTIVE

In this work, the potential of Principal Component Analysis (PCA) based modeling in dimensionality reduction is taken into consideration as the data smoothening tool with multiclass target expression data.

METHODS

On the basis of suggested PCA-based modeling, the power of class prediction using logistic regression (LR) and C5.0 in numeric data is investigated in publicly available Parkinson's disease dataset Silverman voice treatment (LSVT) to develop an advanced classification model.

RESULTS

The main advantage of our model is the effective reduction of the number of factors from p= 309 to k= 32 for LSVT Voice Rehabilitation dataset, with a fine classification accuracy of 100% and 99.92% for PCA-LR and PCA-C5.0 respectively. In addition, using only 9 dysphonia features, classification accuracy was (99.20%) and (99.11%) for PCA-LR, and PCA-C5.0 respectively.

CONCLUSIONS

Our combined dimension reduction and data smoothening approaches have significant potential to minimize the number of features and increase the classification accuracy and then automatically classify subjects into Parkinson's disease patients or healthy speakers.

Outcome of subthalamic nucleus deep brain stimulation on long-term motor function of patients with advanced Parkinson disease

Background: The objective of our study was to assess Unified Parkinson Disease Rating Scale (UPDRS) score in Parkinson disease (PD) patients who underwent subthalamic nucleus (STN) deep brain stimulation (DBS) 6 years after their surgery and to compare their UPDRS score 6 years after DBS with their score before surgery and 6 months after their operation. Methods: In this cross sectional study which was carried out at Neurology Department of Rasool-e Akram Hospital, Tehran, Iran, affiliated to Iran University of Medical Sciences between 2008 and 2014, 37 patients with advanced PD were enrolled using non-randomized sampling method. All of the patients underwent STN DBS surgery and one patient died before being discharged, therefore; we started our study with 36 patients. The UPDRS III total score at preoperative state, 6-month follow-up and 6-year follow-up state were compared using repeated-measure analysis of variance. Results: Thirty-seven patients (26 men and 10 women) with mean age of 50 ± 3 ranging from 32 to 72 years underwent STN DBS surgery. All patients were suffering from advanced PD with mean period of 11.3 ± 1.9 years. All patients except one were followed up for six months. And 14 patients (8 men and 6 women) were included in a six-year follow-up. The UPDRS score measurements before surgery, at 6-month follow-up and 6-year follow-up were 18.22 ± 2.88, 12.80 ± 3.14, 25.0 ± 11.8, respectively. Significant increase in UPDRS score was observed between the preoperative and six-year follow-up period (P < 0.001). Conclusion: In conclusion, this study suggests that total UPDRS score will increase at 5 years following STN DBS and also showed that resting tremor, one of UPDRS sub-scores, will improve over time and the benefit of DBS will be persistent even after 6 years.

New methods for the assessment of Parkinson's disease (2005 to 2015): A systematic review

BACKGROUND

The past decade has witnessed a highly dynamic and growing expansion of novel methods aimed at improving the assessment of Parkinson's disease with technology (NAM-PD) in laboratory, clinical, and home environments. However, the current state of NAM-PD regarding their maturity, feasibility, and usefulness in assessing the main PD features has not been systematically evaluated.

METHODS

A systematic review of articles published in the field from 2005 to 2015 was performed. Of 9,503 publications identified in PubMed and the Web of Science, 848 full papers were evaluated, and 588 original articles were assessed to evaluate the technological, demographic, clinimetric, and technology transfer readiness parameters of NAM-PD.

RESULTS

Of the studies, 65% included fewer than 30 patients, < 50% employed a standard methodology to validate diagnostic tests, 8% confirmed their results in a different dataset, and 87% occurred in a clinic or lab. The axial features domain was the most frequently studied, followed by bradykinesia. Rigidity and nonmotor domains were rarely investigated. Only 6% of the systems reached a technology level that justified the hope of being included in clinical assessments in a useful time period.

CONCLUSIONS

This systematic evaluation provides an overview of the current options for quantitative assessment of PD and what can be expected in the near future. There is a particular need for standardized and collaborative studies to confirm the results of preliminary initiatives, assess domains that are currently underinvestigated, and better validate the existing and upcoming NAM-PD. © 2016 International Parkinson and Movement Disorder Society.