Characteristics Of Motor Research Articles

APPLICATION OF THE MAPLE PACKAGE FOR ANALYTICAL TRANSFORMATIONS FOR CALCULATION AND PLOTING OF STATIONARY MODES OF INDUCTION MOTORS AND ELECTRIC DRIVES

A program was developed in the package of symbolic transformations Maple. It provides automated formulas and graphing of basic characteristics of IM motors in a convenient form for the electrical engineer and student: M(s), ω(M), ω(I), I(s); cosφ=f(s) and φ(s) for stator currents, rotor currents, magnetizing circuit, machine efficiency η (s) and a number of other characteristics. The calculation is based on the substitution scheme of IM motors in its different variants: with one circuit in the rotor, with two or more circuits in the rotor, taking into account the skin effect in the rotor rods and without it. The user can build up the substitution scheme to the desired configuration. The algorithm of further transformations is based on analytical obtaining of amplitude-frequency and phase-frequency characteristics in the nodes of the substitution schemes with further calculation by power and slip. The output of the graphs is provided by the user after setting numerical values and calculations according to the obtained ana- lytical dependences, without numerical errors and mistakes. The article contains screenshots of important parts of the programs and illustrates the complete set of graphs obtained in one run of the program.

Association of Dizziness-Related Handicap or Disability with Clinical Features in Patients with Early Parkinson's Disease.

Complaining of dizziness is common in patients with Parkinson's disease (PD) even at the early phase of the disease. Therefore, regarding motor or non-motor symptoms, clinical implication of subjective dizziness in early Parkinsonian patients is needed to be explored. Eighty patients diagnosed with early PD (defined by disease duration of five years or less) were retrospectively enrolled for the study. Dizziness handicap inventory (DHI), the American Academy of Otolaryngology-Head and Neck Surgery (AAO-HNS) Functional Level Scale (FLS), and clinical features of parkinsonian motor and non-motor symptoms using representative measurements. Through simple and multiple linear regression analyses, we found that both DHI and FLS were significantly and positively correlated with postural instability/gait disorder (PIGD) score but negatively with the Montreal cognitive assessment (MoCA) score. We found that subjective dizziness in patients with early PD was related to not only axial symptoms of PIGD, but also global cognitive function of MoCA. Further research is required to confirm our results.

Classifying handwriting samples according to their type using discriminant analysis

Classifying handwriting samples according to their type (i.e. natural, disguised, traced, simulated or unintentionally unnatural) is an important task in handwriting analysis. It may facilitate the collection of writing standards and also help experts to assess the differences between questioned material and comparative samples or to choose the best writing features and the most relevant examination protocol for the case. Current research aimed to create a method for classifying the type of a handwriting sample using discriminant analysis. Five basic types (i.e. natural, disguised, traced, simulated and unintentionally unnatural) and some subtypes were included in this study. Participants (N = 139) wrote their full signatures, fictional signatures or a short sentence. Motor and dimensional features were assessed. The methods proved to be more than twice as accurate in classifying samples according to their type than a random choice probability (e.g. 44% as opposed to 17% for the 6-types classifier). This proof-of-a-concept study demonstrates that handwriting samples may be classified according to their type with satisfying accuracy based on their writing features and statistical tools of discriminant analysis. However, further studies are necessary to improve the accuracy of the method.

Research on Electromagnetic Vibration Characteristics of a Permanent Magnet Synchronous Motor Based on Multi-Physical Field Coupling

Background and Purpose: The stator vibration characteristics are comprehensively mastered by considering the influence of winding and the housing structure on the stator modes. This effect is neglected in the research field of electromagnetic vibration of permanent magnet synchronous motors (PMSMs). Methods: The radial air-gap flux density equations and PMSM’s electromagnetic force density are derived, and then the harmonic characteristics of electromagnetic force density are studied. An equivalent finite element model of the stator is proposed that investigates the impacts of the winding and stator housing rules on the stator modal frequency. Finally, the harmonic response and acoustic analyses of electromagnetic vibration are carried out based on multi-physics field coupling. Results: The equivalent radiated power distribution laws and acoustic field of motor electromagnetic vibration under transient operating conditions are obtained. The theoretical analysis results are consistent with the experimental results. Conclusion: The obtained results show that the spatial order of the radial electromagnetic force is not equal to the order of the radial mode of the motor stator. The reason for this is that structural resonance is induced when the frequency components of the spatial radial electromagnetic force are coupled with the intrinsic frequency of the stator.

Experiment research on instantaneous characteristics of variable-thrust hybrid rocket motor by reconstruction techniques

The firing test of variable-thrust Hybrid Rocket Motor (HRM), operated with Hydrogen Peroxide (HP) and Polyethylene (PE), based on closed-loop thrust control, has been carried out in this paper. The experiment research on instantaneous characteristics of HRM is conducted based on reconstruction techniques. A post-processing software has been developed to reconstruct the instantaneous performance parameters during the burning stage of HRM. Meanwhile, the formula of regression rate is fitted by reconstruction results through a single firing test, which is a significant advantage compared with traditional method. The averages of instantaneous parameters calculated by reconstruction techniques accord well with experimental data summarized by traditional consumption weight loss method, and the accuracy of reconstruction calculation is better than 1%. This paper is designed to achieve the reconstruction for instantaneous parameters and research the instantaneous characteristics during the variable-thrust firing test of HRM. The reconstruction techniques have the better accuracy, and also provide a critical method for the research on instantaneous characteristics of hybrid rocket motor.

MATHEMATICAL MODEL OF THE DEPENDENCE OF THE STARTING TORQUE OF AN ASYNCHRONOUS ELECTRICAL MOTOR ON THE INITIAL ANGULAR POSITION OF THE ROTOR

The current trend of recent decades is the growing role of the automated electric drive built on the basis of asynchronous electric motors. This is connected both with the development of theoretical approaches that allow to model the behavior of the latter with high accuracy, and with a significant increase in reliability and a decrease in the cost of power semiconductor technology, which in turn made it possible to improve operational characteristics and reduce the cost of such systems as a whole. The consequence of this trend is that asynchronous motors are increasingly used not only in classic electric drive systems with easy starting conditions, but also increasingly used in traction electric drives and electric drives with large starting moments, which were traditionally implemented on the basis of direct current motors. This circumstance leads to the appearance of additional technical difficulties in the design and operation of asynchronous electric motors, which is associated with the peculiarities of their natural mechanical characteristics, which have a slightly underestimated value of the starting torque. And although this shortcoming can be eliminated by increasing the power reserve of the engine when designing the electric drive, such a compromise solution will have significant disadvantages associated with a decrease in efficiency and an increase in the cost of the electric drive as a whole. It is obvious that this circumstance determines the need for further theoretical research aimed at improving the starting characteristics of asynchronous motors. Considering what has been said, today the induction motor remains the most common electric machine used in various fields of activity. Moreover, the share of asynchronous motors used in industry is growing rapidly, and the scope of their use is constantly expanding. A. therefore, the development of a mathematical model of the dependence of the starting torque on the angular position of the rotor, which in the future would allow improving the starting characteristics of asynchronous motors, is an urgent scientific and applied problem that has significant practical value. In the article, the equation of the dependence of the starting torque of an asynchronous motor on the initial position of its rotor is obtained, which makes it possible, taking into account the design features of the AD, to uniquely calculate the value of the mechanical torque that occurs on the motor shaft when it is started with the nominal supply voltage. The mathematical dependence of the deviation of the AD starting moment on the initial rotation angle of the rotor was obtained, which can be used as a corrective mathematical model of the AD control system. The simulation of the dependence of the torque of an asynchronous motor on the initial position of its rotor was carried out, which made it possible to confirm the adequacy of the obtained mathematical dependence. It is shown that the change of the starting torque during rotation of the AD rotor occurs according to the sinusoidal law. It is theoretically proven that deviations of the starting torque at different initial angular positions of the rotor can reach ± 6.5% of the nominal value.

Revelations of delirium subtype research: A bibliometric analysis of publications in the past twenty years in the field

Delirium is an acute confusion state that is common and costly. According to different clinical manifestations, delirium can be divided into three subtypes: hyperactive, hypoactive and mixed. Subtype research has become a necessary branch. However, it is difficult to record all the changes in subtype research. Publications on delirium subtypes in the Web of Science Core Collection (WOSCC) were identified and analyzed by visualization software VOSviewer and CiteSpace. A total of 247 articles published from 1999 to 2022 were identified in the WOSCC, and the largest number of articles was published in 2021 (n = 33). The top three countries that contributed publications were the USA (n = 75), Ireland (n = 26), and the United Kingdom (n = 25), which communicated more often and focused on delirium subtypes earlier. Critical Care Medicine published the most articles regarding delirium subtypes, with 11 publications. Three instrument validation studies were cited most frequently. Six clusters were summarized, including descriptions of delirium among elderly people, delirium research, postoperative delirium, delirium motor subtype validation, critical delirium, and motor characteristics. The "postoperative delirium", "intensive care unit", and "cardiac surgery" keywords were seen in recent years. Based on this bibliometric analysis of the publications in the last twenty years, a comprehensive analysis of the literature clarified the contributions, changes, and evolution regarding delirium subtypes. This research can provide medical staff and researchers with revelations into future directions of delirium subtype advancements.

Fast Topology Optimization for PM Motors Using Variational Autoencoder and Neural Networks With Dropout

This study proposes a novel topology optimization (TO) method for permanent magnet (PM) motors based on a variational autoencoder (VAE) and a neural network (NN). The VAE is trained to embed various shapes generated from the TO into the latent space. The NN is trained to predict the characteristics of the PM motor from its latent representation derived using the VAE. After training, TO is performed in the latent space based on the prediction using the NN. We adopt the Monte Carlo dropout to maintain prediction reliability using the NN during optimization, where prediction deviation is evaluated and used to eliminate unreliable solutions. The proposed method yields Pareto solutions within 80 s in a single-thread CPU machine, which is considerably faster than numerical analysis-based optimization, such as finite element analysis.

Considering REM Sleep Behavior Disorder in the Management of Parkinson's Disease.

Rapid eye movement (REM) sleep behavior disorder (RBD) is the result of the loss of physiological inhibition of muscle tone during REM sleep, characterized by dream-enacting behavior and widely recognized as a prodromal manifestation of alpha-synucleinopathies. Indeed, patients with isolated RBD (iRBD) have an extremely high estimated risk to develop a neurodegenerative disease after a long follow up. Nevertheless, in comparison with PD patients without RBD (PDnoRBD), the occurrence of RBD in the context of PD (PDRBD) seems to identify a unique, more malignant phenotype, characterized by a more severe burden of disease in terms of both motor and non-motor symptoms and increased risk for cognitive decline. However, while some medications (eg, melatonin, clonazepam, etc.) and non-pharmacological options have been found to have some therapeutic benefits on RBD there is no available treatment able to modify the disease course or, at least, slow down the neurodegenerative process underlying phenoconversion. In this scenario, the long prodromal phase may allow an early therapeutic window and, therefore, the identification of multimodal biomarkers of disease onset and progression is becoming increasingly crucial. To date, several clinical (motor, cognitive, olfactory, visual, and autonomic features) neurophysiological, neuroimaging, biological (biofluids or tissue biopsy), and genetic biomarkers have been identified and proposed, also in combination, as possible diagnostic or prognostic markers, along with a potential role for some of them as outcome measures and index of treatment response. In this review, we provide an insight into the present knowledge on both existing and future biomarkers of iRBD and highlight the difference with PDRBD and PDnoRBD, including currently available treatment options.

Catatonia and Neuroleptic Malignant Syndrome in Patients With Cerebral Palsy: Two Case Reports and a Systematic Review of the Literature.

Patients with cerebral palsy, a group of movement disorders with motor, and possibly communication and behavioral features that mimic catatonic signs, may benefit from efforts to improve the detection and treatment of comorbid catatonia. Given that cerebral palsy frequently co-occurs with conditions associated with catatonia, such as autism spectrum disorder, epilepsy, intellectual disability, and mood and psychotic disorders, lifetime prevalence of catatonia in this population may be high. This study aimed to systematically review the literature on catatonia and the related condition of neuroleptic malignant syndrome (NMS) in patients with cerebral palsy while presenting 2 additional cases of catatonia. We used the terms "cerebral palsy" in combination with "catatoni∗," related terms for catatonia, and "neuroleptic malignant syndrome" to query Ovid MEDLINE (1948 to November 28, 2022), PsycINFO, Cumulative Index to Nursing, and Allied Health Literature, and Embase for applicable case reports. The Neuroleptic Malignant Syndrome Information Service database was also manually searched. In addition to our 2 catatonia reports, we identified 10 reports of catatonia in patients with cerebral palsy, as well as 8 reports of NMS. Patients with both conditions responded well, and sometimes rapidly, to treatment. Notably, of the 5 patients with catatonia and cerebral palsy who received electroconvulsive therapy, 2 developed recurrent self-limited hyperthermia posttreatment. We also identified several cases of baclofen withdrawal, which can be life threatening because of seizure risk, presenting with NMS-like features in patients with cerebral palsy who had malfunctioning intrathecal baclofen pumps for spasticity management. Given frequent comorbidity of conditions associated with catatonia in patients with cerebral palsy, as well as routine treatment with medications that can induce NMS, such as metoclopramide and anticholinergics, catatonia and NMS may be underreported in the cerebral palsy patient population, despite being highly treatable. Possible underdiagnosis of catatonia in patients with cerebral palsy may be because of misattribution of overlapping features between the 2 conditions to cerebral palsy. Clinicians should be aware of possible recurrent self-limited fever when using electroconvulsive therapy to treat patients with catatonia and cerebral palsy while also being vigilant for intrathecal baclofen withdrawal when encountering NMS-like features in patients with cerebral palsy.

An ensemble nearest neighbor boosting technique for prediction of Parkinson’s disease

Parkinson’s disease (PD) is a complex condition that affects an individual’s motor features and intensifies over time. Symptoms vary between individuals and can go undetected at an early stage. Voice data has shown potential in predicting PD. In recent years, Machine Learning (ML) has made significant progress, with ensemble ML approaches proving to be effective in various applications. This research aims to detect PD at an early stage using ensemble techniques on the Parkinson’s speech dataset (PSV), which contains several sound recordings with 27 input features and one target feature. Feature importance was computed using Mean Decrease in Impurity (F-MDI), Feature Permutation (F-PER), and Pearson’s Correlation (F-CORR) techniques. Several supervised ML baseline models were compared with the proposed Nearest Neighbor Boosting (NNB) technique, which combines k-Nearest Neighbor (k-NN) and Gradient Boosting (GB). The NNB technique outperformed the baseline models for multiple performance metrics, including accuracy, recall, precision, F-score, the area under the curve (AUC), receiver operating characteristic (ROC), and confusion matrix (CM). The findings suggest that the designed ensemble method can be a promising method for PD detection.

Comparative Study of Magnet Temperature Estimation at Low Speeds Based on High-Frequency Resistance and Inductance

Interior permanent magnet synchronous motors have been widely used in electric vehicles. These motors employ Nd-Fe-B as the permanent magnet, which is vulnerable to temperature variations. Moreover, some features of Nd-Fe-B magnets are related to temperature, which can affect motor characteristics. Therefore, magnet temperature is an important parameter and methods for estimating it have been developed. In particular, a signal injection method has been developed for low-speed regions. In this method, two parameters are employed: high-frequency resistance and high-frequency inductance. In this paper, these two methods are compared to determine which parameter is more appropriate for estimations at low speeds and to reveal whether signal injection methods can be applied to the standstill condition. The comparison indicated that the high-frequency inductance-based method has a stronger correlation with the magnet temperature at low speed and standstill conditions than the high-frequency resistance-based method.

Subtypes of Relapsing-Remitting Multiple Sclerosis Identified by Network Analysis (P3-3.004)

<h3>Objective:</h3> To use network analysis to identify subtypes of relapsing-remitting multiple sclerosis patients based on their cumulative signs and symptoms. <h3>Background:</h3> Network analysis can elucidate the complex relationships between the phenotypes, genes, proteins, and metabolic pathways that underlie human diseases, without a priori knowledge of how these elements will interact. It is of interest to determine whether subtypes of multiple sclerosis based on predominant clinical presentation (sensory, cerebellar, motor, visual, cognitive, fatigue, etc.) can be identified. Clinical subtypes could differ regarding prognosis, course, and response to treatment. <h3>Design/Methods:</h3> We reviewed the electronic medical records of 120 patients with relapsing-remitting multiple sclerosis and recorded signs and symptoms. Signs and symptoms were mapped to a neuro-ontology and then collapsed into 16 super classes by subsumption. Bipartite (subject-feature) and unipartite (subject-subject) network graphs were created using Gephi for both a normalized and unnormalized feature set. Degree and weighted degree were calculated for each node. Graphs were partitioned into communities using the modularity score. Feature maps were used to visualize differences in features by community. <h3>Results:</h3> Network analysis of unipartite graphs yielded higher modularity scores (unnormalized 0.377, normalized 0.49) than the bipartite graphs (unnormalized 0.258, normalized 0.247). Across all graphs, we found large communities of subjects with predominantly sensory features and communities with predominantly motor features (weakness combined with hypertonia, hyperreflexia, or incoordination). When the feature dataset was normalized, network analysis showed motor and sensory communities and additional communities of subjects with a predominance of fatigue, pain, and cognitive features. <h3>Conclusions:</h3> Network analysis can partition multiple sclerosis subjects into communities based on signs and symptoms. Communities of subjects with predominant motor, sensory, pain, fatigue, cognitive, and behavior features were found. Larger datasets and additional partitioning algorithms are needed to confirm these results and elucidate their clinical significance. <b>Disclosure:</b> Mr. Howlett-Prieto has nothing to disclose. Ms. Oommen has nothing to disclose. The institution of Dr. Carrithers has received research support from Biogen. Dr. Hier has nothing to disclose.

A novel ANO3 variant in two siblings with different phenotypes

IntroductionDystonia type 24 is due to the mutation of the ANO3 gene. It generally consists of craniocervical dystonia associated with tremor; however, other neurological manifestations may also occur. Scientific literature has been expanding on its phenotype over the past few years. CaseHere we present two siblings affected by dystonia 24 associated to a novel missense mutation of the ANO3 gene. Description of their phenotype, with regard to motor and non-motor features, may improve the knowledge on DYT 24. Consistent with previous reports, our patients presented with cranio-cervical involvement, and they also exhibited different severity and phenotypes. However non-motor symptoms were present too. ConclusionDystonia 24 spectrum is continuously expanding. This case suggests that the ANO3 missense mutation should be sought in all cases of dystonia and isolated tremor and that non-motor symptoms are an integral part of dystonic syndromes. It also shows that clinical and treatment features may vary from patient to patient, even if they may present the same mutation.

Status Epilepticus in Children: Experience in a Portuguese Tertiary Hospital

Purpose: Status epilepticus (SE) is a life-threatening neurological emergency, frequently diagnosed in pediatric patients. We aimed to characterize our pediatric cases of SE in an 11-year period according to the 2015 International League Against Epilepsy report. Methods: Clinical electronic records were retrospectively reviewed. All pediatric SE cases admitted from January 2010 to December 2020 were included, excluding neonates. SE was considered refractory if it persisted despite the administration of two appropriate antiseizure medications at acceptable doses.Results: We included 102 episodes, 55 (53.9%) in boys. The median age was 2.5 years (interquartile range, 1.3 to 5.0). Most episodes were classified as SE with prominent motor features (92.2%), and the most frequent etiological classification was acute symptomatic cause (84.3%). A benzodiazepine was used as the first-line antiseizure medication in 99 (97%) cases, of which diazepam was preferred (93%). The preferred second-line medication was phenytoin (65.7%). Midazolam was the most frequently responsible for termination of SE when given in a continuous infusion (47%). Episodes of SE were classified as refractory in 81 (79.4%) cases. Episodes of &gt;60 minutes were more frequent in patients diagnosed with epilepsy (&lt;i&gt;P&lt;/i&gt;=0.036), focal motor SE (&lt;i&gt;P&lt;/i&gt;&lt;0.001), and non-convulsive SE (&lt;i&gt;P&lt;/i&gt;=0.037). The in-hospital mortality rate was 2.9%. Conclusion: Most of our findings are in accord with the current literature. Epilepsy, non-convulsive SE, and focal motor SE were associated with prolonged duration (&gt;60 minutes), which reinforces the significance of the underlying neurological disease and semiological standardization in pediatric SE.

An improved global fast terminal high-order sliding mode control strategy based on novel reaching law for improving PMLSM dynamic performance

Due to its better power density and efficiency, permanent magnet linear synchronous motor (PMLSM) is ideal for generating electricity with wave power device (WPD). Nevertheless, when the PMLSM for the WPD control system is impacted by changes in motor characteristics and interference from the outside environment, it typically is unable to deliver high-quality dynamic performance on schedule. Although sliding mode control (SMC) has been introduced to improve the robustness of the system greatly in recent decades, traditional methods still have problems such as slow dynamic response, poor anti-disturbance performance, large chattering amplitude, and phase delay. To solve the above defects in SMC, an improved global fast terminal high-order SMC strategy based on novel reaching law for improving PMLSM dynamic performance is proposed in this paper, as well as the sliding mode surface and control law of the speed controller is designed based on this control strategy. The proposed strategy would not only shorten the dynamic response time but also weaken the chattering magnitude in the sliding mode stage, improving the dynamic performance of both speed and current. The simulation results demonstrate that in comparison to the traditional PI and SMC control methods, the improved strategy proposed in this paper can further enhance the control performance of the linear motor, demonstrating the method’s enormous potential in the double closed-loop vector control system of PMLSM for WPD.

Direct Torque Control (DTC) Design With Fuzzy Sugeno-Proportional Derivative for 3-Phase Induction Motor Speed Control

In general, in the industrial world, induction motors are more widely used than direct current motors, due to the characteristics of induction motors that are sturdy, reliable, easy to maintain, and relatively inexpensive. The phenomenon of changing rotational speed when the load changes results in regulation of the speed of the induction motor, which risks slow response time, there is overshoot which should still be minimized, and there are disturbances caused by external environmental factors, so that a controller is needed that is able to work effectively to optimize performance. 3 phase induction motor. The purpose of this study is to design a sugeno-PD fuzzy DTC controller, where the DTC provides a fast and strong response mounted on an AC motor. Fuzzy Sugeno provides a short calculation time and its reasoning includes wide enough data and PD to speed up the response time results. So that the proposed method produces an induction motor rotating speed according to the given setpoint of 100 rad/s with a settling time of 0.45 seconds, a rise time of 0.2 seconds and no steady state error. From the state of the plan output response before being given the controller there is a steady state error of 5 rad/s, a maximum overshoot of 5.4111%, a settling time of 0.1554 seconds and a rise time of 0.1554 seconds

Regional spreading pattern is associated with clinical phenotype in amyotrophic lateral sclerosis.

Increasing evidence shows that disease spreading in amyotrophic lateral sclerosis (ALS) follows a preferential pattern with more frequent involvement of contiguous regions from the site of symptom onset. The aim of our study was to assess if: (i) the burden of upper (UMN) and lower motor neuron (LMN) involvement influences directionality of disease spreading; (ii) specific patterns of disease progression are associated with motor and neuropsychological features of different ALS subtypes (classic, bulbar, primary lateral sclerosis, UMN-predominant, progressive muscular atrophy, flail arm, flail leg); and (iii) specific clinical features may help identify ALS subtypes, which remain localized to the site of onset for a prolonged time (regionally entrenching ALS). A single-centre, retrospective cohort of 913 Italian ALS patients was evaluated to assess correlations between directionality of the disease process after symptom onset and motor/neuropsychological phenotype. All patients underwent an extensive evaluation including the following clinical scales: Penn Upper Motor Neuron Score (PUMNS), MRC Scale for Muscle Strength and the Edinburgh Cognitive and Behavioural ALS Screen (ECAS). The most frequent initial spreading pattern was that towards adjacent horizontal regions (77.3%), which occurred preferentially in patients with lower MRC scores (P = 0.038), while vertical diffusion (21.1%) was associated with higher PUMNS (P < 0.001) and with reduced survival (P < 0.001). Non-contiguous disease spreading was associated with more severe UMN impairment (P = 0.003), while contiguous disease pattern with lower MRC scores. Furthermore, non-contiguous disease spreading was associated with more severe cognitive impairment in both executive and visuospatial ECAS domains. Individuals with regionally entrenching ALS were more frequently female (45.6% versus 36.9%; P = 0.028) and had higher frequencies of symmetric disease onset (40.3% versus 19.7%; P < 0.001) and bulbar phenotype (38.5% versus 16.4%; P < 0.001). Our study suggests that motor phenotypes characterized by a predominant UMN involvement are associated with a vertical pattern of disease progression reflecting ipsilateral spreading within the motor cortex, while those with predominant LMN involvement display more frequently a horizontal spreading from one side of the spinal cord to the other. These observations raise the hypothesis that one of the mechanisms underlying disease spreading in ALS pathology is represented by diffusion of toxic factors in the neuron microenvironment. Finally, it is possible that in our cohort, regionally entrenching ALS forms are mainly observed in patients with atypical bulbar phenotypes, characterized by a slowly progressive course and relatively benign prognosis.

Automatic video analysis and classification of sleep-related hypermotor seizures and disorders of arousal.

Sleep-related hypermotor epilepsy (SHE) is a focal epilepsy with seizures occurring mostly during sleep. SHE seizures present different motor characteristics ranging from dystonic posturing to hyperkinetic motor patterns, sometimes associated with affective symptoms and complex behaviors. Disorders of arousal (DOA) are sleep disorders with paroxysmal episodes that may present analogies with SHE seizures. Accurate interpretation of the different SHE patterns and their differentiation from DOA manifestations can be difficult and expensive, and can require highly skilled personnel not always available. Furthermore, it is operator dependent. Common techniques for human motion analysis, such as wearable sensors (e.g., accelerometers) and motion capture systems, have been considered to overcome these problems. Unfortunately, these systems are cumbersome and they require trained personnel for marker and sensor positioning, limiting their use in the epilepsy domain. To overcome these problems, recently significant effort has been spent in studying automatic methods based on video analysis for the characterization of human motion. Systems based on computer vision and deep learning have been exploited in many fields, but epilepsy has received limited attention. In this paper, we present a pipeline composed of a set of three-dimensional convolutional neural networks that, starting from video recordings, reached an overall accuracy of 80% in the classification of different SHE semiology patterns and DOA. The preliminary results obtained in this study highlight that our deep learning pipeline could be used by physicians as a tool to support them in the differential diagnosis of the different patterns of SHE and DOA, and encourage further investigation.

The strength, hope and resourcefulness program for people with Parkinson’s disease: a qualitative investigation of group members’ experiences

ABSTRACT Parkinson’s Disease (PD) is a neurodegenerative disease characterized by motor and non-motor features. Many people with PD struggle with mental health issues such as depression. Evidence shows that people with PD may have trouble adopting a hopeful or positive orientation given their diagnosis and symptoms. Psychological treatments for PD are scarce and even more scarce are treatments that specifically focus on promoting hope and well-being. The Strengths, Hope, and Resourcefulness Program for People with Parkinson’s Disease was developed to begin to address this treatment gap. This qualitative study identifies seven processes group members found helpful during treatment: Accessing Hope, Comparing, Drawing Together, Reaching Out Beyond Self, Releasing Emotion, Sharing Skills and Picking Up Resources, and Self-Reflecting Leading to Personal Insight. The unique contributions of an intentional and explicit hope, strengths, and resources intervention are discussed with specific attention to therapeutic process and practice.