Search

The aim of this work is to address the problem of White Matter Lesion (WML) segmentation employing Magnetic Resonance Imaging (MRI) images from Multiple Sclerosis (MS) patients through the application of deep learning. A U-net based architecture containing a contrastive path and an expanding path prior to the final pixel-wise classification is implemented. The data are provided by the Ippokratio Radiology Center of Ioannina and include Fluid-Attenuated Inversion Recovery (FLAIR) MRI images from 30 patients in three phases, baseline and two follow ups. The prediction results are quite significant in terms of pixel-wise classification. The implemented deep learning model demonstrates Dice coefficient 0.7292, Precision 75.92% and Recall 70.16% in 2D slices of FLAIR MRI non-skull stripped images.

Analysis of cases with spirochetal uveitis related to spirochetes in a tertiary referral academic center. Retrospective study of patients diagnosed with uveitis attributed to Treponema pallidum, Leptospira spp. and Borrelia burgdorferi from June 1991 until December 2019. A total of 57 cases of spirochetal uveitis (22 patients with T. pallidum, 26 with Leptospira spp., and 9 with B. burgdorferi) that consisted 1% of the overall number of uveitics were recorded. All these cases presented with a wide spectrum of clinical presentations (anterior uveitis, posterior uveitis, panuveitis, vasculitis, papillitis, and in some rare cases concomitant posterior scleritis). The treatment included mainly penicillin or doxycycline, while corticosteroids were administered systematically in some cases with Borrelia or Leptospira infection. The final visual outcome was favorable (> 6/10 in Snellen visual acuity) in approximately 76% of our patients. Despite being rare, spirochetal uveitis can be detrimental for the vision and must always be included in the differential diagnosis.

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.

Quality of life of patients with Parkinson's disease degrades significantly with disease progression. This paper presents a step towards personalized management of Parkinson's disease patients, based on discovering groups of similar patients. Similarity is based on patients’ medical conditions and changes in the prescribed therapy when the medical conditions change. We present two novel approaches. The first algorithm discovers symptoms’ impact on Parkinson's disease progression. Experiments on the Parkinson Progression Markers Initiative (PPMI) data reveal a subset of symptoms influencing disease progression which are already established in Parkinson's disease literature, as well as symptoms that are considered only recently as possible indicators of disease progression by clinicians. The second novelty is a methodology for detecting patterns of medications dosage changes based on the patient status. The methodology combines multitask learning using predictive clustering trees and short time series analysis to better understand when a change in medications is required. The experiments on PPMI data demonstrate that, using the proposed methodology, we can identify some clinically confirmed patients’ symptoms suggesting medications change. In terms of predictive performance, our multitask predictive clustering tree approach is mostly comparable to the random forest multitask model, but has the advantage of model interpretability.

Summarization: The electroencephalogram (EEG) is the most prominent means to study epilepsy and capture changes in electrical brain activity that could declare an imminent seizure. In this work, Long Short-Term Memory (LSTM) networks are introduced in epileptic seizure prediction using EEG signals, expanding the use of deep learning algorithms with convolutional neural networks (CNN). A pre-analysis is initially performed to find the optimal architecture of the LSTM network by testing several modules and layers of memory units. Based on these results, a two-layer LSTM network is selected to evaluate seizure prediction performance using four different lengths of preictal windows, ranging from 15 min to 2 h. The LSTM model exploits a wide range of features extracted prior to classification, including time and frequency domain features, between EEG channels cross-correlation and graph theoretic features. The evaluation is performed using long-term EEG recordings from the open CHB-MIT Scalp EEG database, suggest that the proposed methodology is able to predict all 185 seizures, providing high rates of seizure prediction sensitivity and low false prediction rates (FPR) of 0.11–0.02 false alarms per hour, depending on the duration of the preictal window. The proposed LSTM-based methodology delivers a significant increase in seizure prediction performance compared to both traditional machine learning techniques and convolutional neural networks that have been previously evaluated in the literature. Presented on: Computers in Biology and Medicine

Parkinson’s disease (PD) is a degenerative disorder of the central nervous system, which requires a long-term, interdisciplinary disease management. The EU Horizon 2020 project PD_manager (http://w...

In this work an unsupervised methodology for the detection of epileptic seizures in long-term EEG recordings is presented. The design of the methodology exploits the available medical knowledge to tackle the lack of training data using a simple rule-based seizure detection logic, avoiding complex decision making systems, training and empirical thresholds. The Short-Time Fourier Transform is initially applied to extract the EEG signal energy distribution over the delta (

In this work an unsupervised epileptic seizure detection methodology is proposed to automatically isolate candidate EEG seizure segments without requiring any apriori information or human intervention. Seizures are detected when a concentration of rhythmical activity is observed in the lower end of the EEG spectrum within the delta (≤3 Hz), theta (4–7 Hz) and alpha (8–13 Hz) frequency bands, along with an increase in EEG signal variation and a high frequency of spikes/sharp waves in the same segment. When a candidate seizure segment is detected, channel localization is also evaluated to assess its activation pattern providing feedback to the clinician regarding potential seizure onset area. The proposed methodology is evaluated using EEG signals from two open EEG datasets: the CHB-MIT Scalp EEG Database with long-term recordings and the TUH Seizure Corpus consisting of selected EEG epochs with high ictal to interictal duration ratio with an average sensitivity of 81.15-91.35% and a false detection rate between 5.33-12.15 FD/h.

Tracking symptoms progression in the early stages of Parkinson's disease (PD) is a laborious endeavor as the disease can be expressed with vastly different phenotypes, forcing clinicians to follow a multi-parametric approach in patient evaluation, looking for not only motor symptomatology but also non-motor complications, including cognitive decline, sleep problems and mood disturbances. Being neurodegenerative in nature, PD is expected to inflict a continuous degradation in patients' condition over time. The rate of symptoms progression, however, is found to be even more chaotic than the vastly different phenotypes that can be expressed in the initial stages of PD. In this work, an analysis of baseline PD characteristics is performed using machine learning techniques, to identify prognostic factors for early rapid progression of PD symptoms. Using open data from the Parkinson's Progression Markers Initiative (PPMI) study, an extensive set of baseline patient evaluation outcomes is examined to isolate determinants of rapid progression within the first two and four years of PD. The rate of symptoms progression is estimated by tracking the change of the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) total score over the corresponding follow-up period. Patients are ranked according to their progression rates and those who expressed the highest rates of MDS-UPDRS total score increase per year of follow-up period are assigned into the rapid progression class, using 5- and 10-quantiles partition. Classification performance against the rapid progression class was evaluated in a per quantile partition analysis scheme and in quantile-independent approach, respectively. The results shown a more accurate patient discrimination with quantile partitioning, however, a much more compact subset of baseline factors is extracted in the latter, making a more suitable for actual interventions in practice. Classification accuracy improved in all cases when using the longer 4-year follow-up period to estimate PD progression, suggesting that a prolonged patient evaluation can provide better outcomes in identifying rapid progression phenotype. Non-motor symptoms are found to be the main determinants of rapid symptoms progression in both follow-up periods, with autonomic dysfunction, mood impairment, anxiety, REM sleep behavior disorders, cognitive decline and memory impairment being alarming signs at baseline evaluation, along with rigidity symptoms, certain laboratory blood test results and genetic mutations.

Although the electroencephalogram (EEG) is the most commonly used means to monitor epileptic patients, public EEG datasets are very scarce making it difficult to develop and validate seizure detection algorithms. In this work an unsupervised seizure detection methodology is used to isolate ictal EEG segments without requiring any apriori information or human intervention. Seizures are detected using four simple seizure detection conditions that are activated when rhythmical activity from different brain areas is simultaneously concentrated in the alpha (8-13 Hz), theta (4-7 Hz) or delta (1-3 Hz) frequency range. Then, only a small proportion of the EEG segments that are most likely to contain ictal activity is selected and presented to the physician for the final evaluation. In this way, large volumes of EEG signals can be annotated in a fraction of the time and effort that would be otherwise required. Using EEG data from 33 sessions from the Temple University Hospital (TUH) EEG Corpus, our unsupervised methodology reached, on average, 84.92% seizure detection sensitivity with 3.46 false detections per hour of EEG signals.

Background Imaging and histopathologic studies in patients with primary Sjogren’s Syndrome (pSS) have demonstrated white matter lesions which are indistinguishable from those observed in multiple sclerosis (MS). Objectives The purpose of this study was to evaluate the frequency of MS in a cohort of patients with pSS. Methods One hundred and twenty-one patients with pSS had been evaluated and followed up in the outpatient Rheumatology Clinic at predefined times since 1994. All patients were classified according to the 2016 ACR-EULAR criteria for SS. During follow-up the clinical, laboratory and imaging findings were all recorded. In addition, Magnetic Resonance Imaging and electrophysiological studies as well as spinal fluid analysis were performed when indicated. The diagnosis of MS was based on the 2010 revised McDonald criteria. Results Seven patients were diagnosed as having MS. All patients with MS were female. Mean age at the time of MS diagnosis was 65.5±3.6 years, while pSS has been diagnosed at the mean age of 54.4±3.2 years. Mean time of MS development was approximately 10 years after the pSS diagnosis. pSS patients who developed MS had severe sicca symptoms without other extraglandular manifestations and had positive Ro (SSA) antibodies and a positive minor salivary gland biopsy. pSS patients with MS development were treated appropriately in the Neurology department with biological medications with some improvement of the sicca symptoms. Conclusions We found that 5.8% of pSS patients as having MS. This percentage of patients clearly indicates the possibility for the coexistence of a second autoimmune disease with similar if not common pathogenetic mechanisms. Thus patients with pSS should be evaluated carefully and screened appropriately for MS when indicated. Disclosure of Interest None declared

Wilson’s disease (WD) or hepatolenticular degeneration is an autosomal recessive genetic disorder caused by mutations in the ATP7B gene which leads to accumulation of copper primarily in the liver and brain. Clinical features include neuropsychiatric symptoms, liver disease, Kayser–Fleischer ring, and manifestations from other organs, whereas biochemistry reveals increased urinary copper excretion, reduced levels of serum ceruloplasmin, and high concentration of copper in the liver. The diagnosis is based on the combination of clinical features, biochemical and MRI findings, and the results of mutation analysis. However, WD remains a diagnostic challenge for the physician. WD is progressive and fatal if left untreated. Available treatments include D-Penicillamine, trientine, zinc, and tetrathiomolybdate.

Epilepsy is a chronic neurological condition caused by abnormal electrical activity of the human brain that affects up to 1% of the global population. Since seizures may occur at any time, long-term EEG recordings are more suitable to record ictal activity. To assist the inspection process various automated seizure detection methodologies have been reported demonstrating high performance. In this chapter the majority of such long-term EEG signal processing techniques and methods, used in the seizure detection domain, are presented. Emphasis is particularly given on providing a complete overview of the wide variety of methodologies from the last few years, which were evaluated using two well-known public EEG databases consisting of long-term scalp and intracranial EEG recordings. The purpose of this chapter is to provide an evaluation of the methods' performance under a common reference dataset to assess their suitability for implantable or ambulatory seizure detection devices.

Tremor is the most common motor disorder of Parkinson's disease (PD) and consequently its detection plays a crucial role in the management and treatment of PD patients. The current diagnosis procedure is based on subject-dependent clinical assessment, which has a difficulty in capturing subtle tremor features. In this paper, an automated method for both resting and action/postural tremor assessment is proposed using a set of accelerometers mounted on different patient's body segments. The estimation of tremor type (resting/action postural) and severity is based on features extracted from the acquired signals and hidden Markov models. The method is evaluated using data collected from 23 subjects (18 PD patients and 5 control subjects). The obtained results verified that the proposed method successfully: 1) quantifies tremor severity with 87 % accuracy, 2) discriminates resting from postural tremor, and 3) discriminates tremor from other Parkinsonian motor symptoms during daily activities.

An unsupervised methodology for the detection of Epileptic seizures in EEG recordings is proposed. The time-frequency content of the EEG signals is extracted using the Short Time Fourier Transform. The analysis focuses on the EEG energy distribution among the well-established delta, theta and alpha rhythms (2–13 Hz), as energy variations in these frequency bands are widely associated with seizure activity. Relying on seizure rhythmicity, the classification is performed by isolating the segments where each rhythm is more clearly and dominantly expressed over the others. For the first time, an unsupervised methodology is evaluated using more than 978 hours of EEG recordings from a public database. The results show that the proposed methodology achieves high seizure detection sensitivity with significantly reduced human intervention.

In this paper, a methodology for the automated detection and classification of transient events in electroencephalographic (EEG) recordings is presented. It is based on association rule mining and classifies transient events into four categories: epileptic spikes, muscle activity, eye blinking activity, and sharp alpha activity. The methodology involves four stages: 1) transient event detection; 2) clustering of transient events and feature extraction; 3) feature discretization and feature subset selection; and 4) association rule mining and classification of transient events. The methodology is evaluated using 25 EEG recordings, and the best obtained accuracy was 87.38%. The proposed approach combines high accuracy with the ability to provide interpretation for the decisions made, since it is based on a set of association rules.

In this paper, we describe the PERFORM system for the continuous remote monitoring and management of Parkinson's disease (PD) patients. The PERFORM system is an intelligent closed-loop system that seamlessly integrates a wide range of wearable sensors constantly monitoring several motor signals of the PD patients. Data acquired are pre-processed by advanced knowledge processing methods, integrated by fusion algorithms to allow health professionals to remotely monitor the overall status of the patients, adjust medication schedules and personalize treatment. The information collected by the sensors (accelerometers and gyroscopes) is processed by several classifiers. As a result, it is possible to evaluate and quantify the PD motor symptoms related to end of dose deterioration (tremor, bradykinesia, freezing of gait (FoG)) as well as those related to over-dose concentration (Levodopa-induced dyskinesia (LID)). Based on this information, together with information derived from tests performed with a virtual reality glove and information about the medication and food intake, a patient specific profile can be built. In addition, the patient specific profile with his evaluation during the last week and last month, is compared to understand whether his status is stable, improving or worsening. Based on that, the system analyses whether a medication change is needed--always under medical supervision--and in this case, information about the medication change proposal is sent to the patient. The performance of the system has been evaluated in real life conditions, the accuracy and acceptability of the system by the PD patients and healthcare professionals has been tested, and a comparison with the standard routine clinical evaluation done by the PD patients' physician has been carried out. The PERFORM system is used by the PD patients and in a simple and safe non-invasive way for long-term record of their motor status, thus offering to the clinician a precise, long-term and objective view of patient's motor status and drug/food intake. Thus, with the PERFORM system the clinician can remotely receive precise information for the PD patient's status on previous days and define the optimal therapeutical treatment.

PERFORM is a system for the monitoring, assessment and management of patient with Parkinson's disease (PD). It comprises of three subsystems: (i) Multi-Sensor Monitoring Unit, (ii) the Local Base Unit, and (iii) the Centralized Hospital unit. The wearable, multi-sensor monitoring unit (WMSMU) of the PERFORM system is presented in this work. This unit plays an essential role in the overall PERFORM system since it is responsible to record and pre-process accelerometer and gyroscope signals that are later used by the various components of the Local Base Unit in order to classify and quantify the symptoms and motor status of the PD patients. The WMSMU was evaluated in a large set of pilot studies in PD patients.

OBJECTIVE: To evaluate the contribution of amino-3-hydroxy-5-methyl-4-isoxazole proprionic acid (AMPA) glutamate receptors to the pathogenesis of parkinsonian signs and levodopa-induced dyskinesias. BACKGROUND: Motor fluctuations and dyskinesias reflect, in part, altered function of glutamate receptors of the NMDA subtype. The possible role of AMPA receptors, however, has not yet been examined. METHODS: The authors compared the ability of an AMPA agonist (CX516) and a noncompetitive AMPA antagonist (LY300164) to alter parkinsonian symptoms and levodopa-induced dyskinesia in MPTP-lesioned monkeys. Eight levodopa-treated parkinsonian monkeys received rising doses of each drug, first in monotherapy and then in combination with low-, medium-, and high-dose levodopa. RESULTS: CX516 alone, as well as when combined with low-dose levodopa, did not affect motor activity but induced dyskinesia. Moreover, following injection of the higher doses of levodopa, it increased levodopa-induced dyskinesia by up to 52% (p < 0.05). LY300164 potentiated the motor activating effects of low-dose levodopa, increasing motor activity by as much as 86% (p < 0.05), and that of medium-dose levodopa as much as 54% (p < 0.05). At the same time, LY300164 decreased levodopa-induced dyskinesia by up to 40% (p < 0.05). CONCLUSIONS: AMPA receptor upregulation may contribute to the expression of levodopa-induced dyskinesia. Conceivably, noncompetitive AMPA receptor antagonists could be useful, alone or in combination with NMDA antagonists, in the treatment of PD, by enhancing the antiparkinsonian effects of levodopa without increasing and possibly even decreasing levodopa-induced dyskinesia. Neurology

Levetiracetam (LEV), a novel antiepileptic drug, has demonstrated antidyskinetic effect in preclinical animal models of Parkinson's disease (PD) and in one open label study in PD patients with levodopa-induced dyskinesia. The acute antidyskinetic effects of LEV in patients with tardive dyskinesia were evaluated in an open label study. Eight patients received oral LEV (1,000 mg/day) for 1 month and blinded evaluations were performed at baseline and at the end of the treatment period. A significant reduction of the abnormal movements was recorded while psychiatric symptoms did not worsen and the adverse event profile was benign. LEV may be efficacious for the treatment of tardive dyskinesia and deserves further clinical testing. Mov Disord

Epilepsy is a neurological disorder with prevalence of about 1-2% of the world’s population (Mormann, Andrzejak, Elger & Lehnertz, 2007). It is characterized by sudden recurrent and transient disturbances of perception or behaviour resulting from excessive synchronization of cortical neuronal networks; it is a neurological condition in which an individual experiences chronic abnormal bursts of electrical discharges in the brain. The hallmark of epilepsy is recurrent seizures termed "epileptic seizures". Epileptic seizures are divided by their clinical manifestation into partial or focal, generalized, unilateral and unclassified seizures (James, 1997; Tzallas, Tsipouras & Fotiadis, 2007a, 2009). Focal epileptic seizures involve only part of cerebral hemisphere and produce symptoms in corresponding parts of the body or in some related mental functions. Generalized epileptic seizures involve the entire brain and produce bilateral motor symptoms usually with loss of consciousness. Both types of epileptic seizures can occur at all ages. Generalized epileptic seizures can be subdivided into absence (petit mal) and tonic-clonic (grand mal) seizures (James, 1997).

The aim of this study is to detect freezing of gait (FoG) events in patients suffering from Parkinson's disease (PD) using signals received from wearable sensors (six accelerometers and two gyroscopes) placed on the patients' body. For this purpose, an automated methodology has been developed which consists of four stages. In the first stage, missing values due to signal loss or degradation are replaced and then (second stage) low frequency components of the raw signal are removed. In the third stage, the entropy of the raw signal is calculated. Finally (fourth stage), four classification algorithms have been tested (Naive Bayes, Random Forests, Decision Trees and Random Tree) in order to detect the FoG events. The methodology has been evaluated using several different configurations of sensors in order to conclude to the set of sensors which can produce optimal FoG episode detection. Signals recorded from five healthy subjects, five patients with PD who presented the symptom of FoG and six patients who suffered from PD but they do not present FoG events. The signals included 93 FoG events with 405.6s total duration. The results indicate that the proposed methodology is able to detect FoG events with 81.94% sensitivity, 98.74% specificity, 96.11% accuracy and 98.6% area under curve (AUC) using the signals from all sensors and the Random Forests classification algorithm.

Patients with advanced Parkinson's disease (PD) are known to develop motor complications after a few years of levodopa (L-dopa) therapy. Motor fluctuations develop with increasing severity of the disease, owing to loss of dopaminergic neurons and loss of the buffering capacity of the neurons to fluctuating dopamine levels. Dyskinesias develop as a result of pulsatile stimulation of the receptors and alterations in neuronal firing patterns. L-dopa remains the gold standard medication for the treatment of patients with advanced PD. However, once motor complications on L-dopa therapy emerge, clinicians may add on other classes of antiparkinsonian drugs such as dopamine agonists, catechol-O-methyl transferase inhibitors (COMTIs) or monoamine oxidase type B inhibitors (MAOBIs). The individualisation of the treatment seems to be the key for the best approach of advanced PD patients. The present review provides the most important current clinical data in the pharmacological treatment of motor symptoms in advanced PD and provides the clinician a simple algorithm in order to determine the best suitable treatment to advanced parkinsonian patients. Prog Neuropsychopharmacol Biol Psychiatry

ObjectiveIn this study, a methodology is presented for an automated levodopa-induced dyskinesia (LID) assessment in patients suffering from Parkinson's disease (PD) under real-life conditions. Methods and MaterialThe methodology is based on the analysis of signals recorded from several accelerometers and gyroscopes, which are placed on the subjects' body while they were performing a series of standardised motor tasks as well as voluntary movements. Sixteen subjects were enrolled in the study. The recordings were analysed in order to extract several features and, based on these features, a classification technique was used for LID assessment, i.e. detection of LID symptoms and classification of their severity. ResultsThe results were compared with the clinical annotation of the signals, provided by two expert neurologists. The analysis was performed related to the number and topology of sensors used; several different experimental settings were evaluated while a 10-fold stratified cross validation technique was employed in all cases. Moreover, several different classification techniques were examined. The ability of the methodology to be generalised was also evaluated using leave-one-patient-out cross validation. The sensitivity and positive predictive values (average for all LID severities) were 80.35% and 76.84%, respectively. ConclusionsThe proposed methodology can be applied in real-life conditions since it can perform LID assessment in recordings which include various PD symptoms (such as tremor, dyskinesia and freezing of gait) of several motor tasks and random voluntary movements.

In this work a decision support tool for optimal administration of Levodopa (L-Dopa) in patients with Parkinson's Disease (PD) is presented. The so called Medication Change Proposer (MCP) is part of the PERFORM system, which monitors patient's motion status using a set of wearable sensors, assesses PD symptoms and their severity through dedicated symptoms detection modules (On-Off detection and Levodopa-Induced Dyskinesia detection), and proposes treatment plans to alleviate the symptoms. The methodology is based on the analysis of the outputs of the symptoms detection modules, taking into account the uncertainty (confidence) propagated by their respective classification algorithms. The evaluation of our methodology is performed using artificial follow-up data produced by a specially built simulation environment. The obtained results demonstrate that the MCP outputs compare well with the experts' decisions, indicating high performance.

Background: The use of dopamine agonists (DAs) for the treatment of restless legs syndrome (RLS) has been assessed in numerous randomized clinical trials (RCTs). Objectives: The aims of this study were to assess the reporting quality of published RCTs according to the Consolidated Standards of Reporting Trials (CONSORT) statement and to synthesize the study results in terms of efficacy and tolerability to inform the clinical management of RLS. Methods: PubMed and Cochrane Controlled Trials Register were searched for English-language RCTs that assessed the effects of DAs in RLS. Quality of reporting was measured using the proportion of 17 CONSORT checklist items included in each study. The 2 primary outcomes were pooled mean change from baseline in International RLS (IRLS) Study Group rating scale score (Δμ) (95% CI) and relative risk (RR) (95% CI) of response based on the Clinical Global Impression-Improvement (CGI-I) scale score. The pooled proportions of adverse events (PAEs) (95% CI) were also estimated. Results: Eighteen RCTs (N = 2848 patients) were included. Two of the 17 CONSORT checklist items were reported in 7 studies (39%) and 9 of the 17 items were reported in all 18 studies (100%). The differences in the IRLS scores and RR for CGI-I were significantly greater with pramipexole, ropinirole, rotigotine, and cabergoline compared with placebo. Results for heterogeneity were nonsignificant. The difference in Δμ (95% CI) was significant with pramipexole (−6.63 [−9.15 to −4.10]) versus ropinirole (−3.64 [−4.76 to 2.51]) (P = 0.04). The difference between pramipexole and rotigotine was nonsignificant. The pooled PAEs (95% CI) for pramipexole, ropinirole, and rotigotine were 4.8% (2.0% to 8.7%), 10.2% (2.6% to 22.1%), and 7.6% (1.3% to 18.5%), respectively. In the trial of sumanirole, the PAE value was 2% (0% to 5.4%). Conclusion: Based on the findings from the meta-analysis, DAs were significantly more efficacious in the treatment of RLS compared with placebo.

In the current work, a system for the monitoring, assessment and management of patients with chronic movement disorders such as Parkinson's disease (PD) is presented. The so called PERFORM system consists of the patient and the healthcare center subsystem. PERFORM monitors patient's motion status in daily activities, using a set of light wearable sensors. Based on the analysis of the acquired signals, PERFORM assesses PD symptoms and their severity, integrates patient's demographic, clinical and history data and proposes treatment plans based on advanced data mining algorithms. In this work we present two main modules of PERFORM system, the tremor assessment module and the data miner module.

Resting tremor (RT) is one of the most frequent signs of the Parkinson's disease (PD), occurring with various severities in about 75% of the patients. Current diagnosis is based on subjective clinical assessment, which is not always easy to capture subtle, mild and intermittent tremors. The aim of the present study is to assess the suitability and clinical value of a computer based real-time system as an aid to diagnosis of PD, in particular the presence of RT. Five healthy subjects were asked to simulate several severities of RT in hands and feet in three static activities. The behaviour of the subjects is measured using tri-axial accelerometers, which are placed at four different positions on the body. Frequency-domain features, strongly correlated with the RT activity, are extracted from the accelerometer data. The classification of RT severity based on those features, provided accuracy 76%. The real-time system designed for efficient extraction of those features and the provision of a continuous RT severity measure is described. Conf Proc IEEE Eng Med Biol Soc

The Kalman Filter (KF) is a powerful tool in the analysis of the evolution of a dynamical model in time. The filter provides with a flexible manner to obtain recursive estimation of the parameters, which are optimal in the mean square error sense. The properties of KF along with the simplicity of the derived equations make it valuable in the analysis of signals. In this chapter an overview of the Kalman Filter, its properties and its applications is presented. More specifically, we focus on the application of Kalman Filter in the Electroencephalogram (EEG) processing, addressing extensions of Kalman Filter such as the Kalman Smoother (KS) in the time varying autoregressive (TVAR) model. The model can be written in a state – space form and the employment of KF provides with an estimation of the AR parameters which can be used for the estimation of the non – stationary signal. It is also demonstrated how these parameters can be used as input features of the signal in a clustering approach. The Kalman Filter is an estimator with interesting properties like optimality in the Minimum Mean Square Error (MMSE). After its discovery in 1960 (Kalman, 1960), this estimator has been used in many fields of engineering such as control theory, communication systems, speech processing, biomedical signal processing, etc. An analogous estimator has been proposed for the smoothing problem (Rauch et al., 1963), which includes three different types of smoothers, namely fixed-lag, fixed-point and fixed interval (Anderson & Moore, 1979; Brown, 1983). In this chapter we address the fixed interval smoother. The difference between the two estimators, the Kalman Filter and the Kalman Smoother, it is related on how they use the observations to perform estimation. The Kalman Filter uses only the past and the present observations to perform estimation, while the Kalman Smoother uses also the future observations for the estimation. This means that the Kalman Filter is used for on - line processing while the Kalman Smoother for batch processing. The derivations of these two estimators is presented in (Kay, 1993; Grewal & Andrews, 2001; Haykin, 2001). Both estimators are recursive in nature. This means that the estimate of the present state is updated using the previous state only and not the entire past states. The Kalman Filter is not only an estimator but also a learning method (Grewal & Andrews, 2001; Bishop, 2006). The observations are used to learn the states of the model. The Kalman Filter is also a computational tool and some problems may exist due to the finite precision arithmetic of the computers.

In this work we present a system for the monitoring and management of neurodegenerative diseases, such as the Parkinson's Disease (PD) and Amyotrophic Lateral Sclerosis (ALS). The purpose of the system is to monitor patient's motor symptoms, and assist the clinician in the evaluation of both the current patient status and the disease progression. The system progresses one step further and suggests appropriate patient treatment changes, based on previously stored or accumulated medical knowledge. In this work, we focus on the description of the wearable platforms used to monitor the patient motor status at the patient's environment.

In this work we describe a system for the monitoring and management of patients with neurodegenerative diseases, focusing on Parkinson's Disease and Amyotrophic Lateral Sclerosis. The system exploits a single wearable sensors' setting to detect and quantify all patient symptoms. An easy to use touchscreen interface allows patients and caregivers to provide additional useful information and assist patients to perform standard predefined tests which otherwise are performed in the clinician's office. The system exploits patient information to suggest appropriate treatment changes based on accumulated medical knowledge. In this paper the architecture of the system, as well as, its innovative features are presented.

Objective: To assess in patients with late-onset idiopathic restless legs syndrome (RLS) the brain iron content with magnetic resonance relaxometry, and brain activation during dorsiflexion and plantar flexion of both feet, using fMRI. Methods: The study was approved by the institutional review board, and informed consent was obtained. Twenty-five RLS patients (14 women, 11 men; age range 55–82 years; mean 66.5 ± 8.9 years; disease duration 6.5 ± 4.5 years) and 12 sex- and age-matched controls were studied. A T1-weighted high-resolution three-dimensional spoiled gradient echo sequence was used for structural imaging, a multislice spin echo Τ2-weighted sequence was used for T2 relaxometry, and a single-shot multislice gradient echo planar sequence was used for fMRI. The motor paradigm consisted of alternating periods of rest and movement, each 40 seconds in duration. Region of interest analysis was used on the T2 relaxometry maps. Statistical parametric mapping software was used for analysis of the functional data. Results: T2 relaxation time was significantly higher in patients than in controls in the substantia nigra pars compacta. Within-group analysis showed that both patients and controls activated the primary motor cortex, the primary somatosensory cortex, the somatosensory association cortex, and the middle cerebellar peduncles. Patients also activated the thalamus, putamen, middle frontal gyrus, and cingulate gyrus. Between-group analysis showed that patients had higher activation of the dorsolateral prefrontal cortex. Conclusion: Late-onset restless legs syndrome is associated with low iron content of the basal ganglia and increased activity of the dorsolateral prefrontal cortex. GLOSSARY: C = constant offset parameter added to compensate for background noise bias; CN = caudate nucleus; DMT1 = divalent metal transporter 1; DN = dentate nucleus; EPI = echo planar imaging; GP = globus pallidus; IRLS = International RLS Study Group Rating Scale; JHRLSS = Johns Hopkins RLS Severity Scale; MIP = maximum intensity projection; MR = magnetic resonance; PLMS = periodic limb movements in sleep; Pu = putamen; RLS = restless leg syndrome; RN = red nucleus; ROI = region of interest; SN = substantia nigra; SNc = substantia nigra pars compacta; SNr = substantia nigra pars reticulata; So = signal amplitude at echo time = 0; SPM = statistical parametric mapping; S(TE) = signal intensity at echo time; TE = echo time; Th = thalamus; TR = repetition time.