Spiros Konitsiotis

Low RLS prevalence and awareness in central Greece: an epidemiological survey

Restless legs syndrome (RLS) is a sensorimotor disorder with a general population prevalence of 3–10%. A single, previous epidemiological study performed in south‐east Europe reported the lowest prevalence rate amongst European countries. We conducted a population‐based survey of RLS in central Greece. A total of 4200 subjects were randomly recruited. We used the international RLS study group criteria for diagnosis and the severity scale for severity assessment in subjects with RLS. We also included questions to assess the level of awareness of RLS in our region. A total of 3033 subjects were screened. The overall lifetime prevalence was 3.9% with a female‐to‐male ratio of 2.6:1. Nearly half of RLS patients reported moderate to severe intensity of symptoms. After adjustment for multiple comparisons we found no association of RLS with education level, smoking, alcohol intake, caffeine consumption, shift work, professional pesticide use or comorbid illness. Our study revealed a low level of awareness amongst the population and physicians in our region and sub‐optimal management. We provide further evidence for low prevalence of RLS in south‐east Europe and a low level of awareness of RLS in our region.

EEG Transient Event Detection and Classification Using Association Rules

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

Randomized trials of dopamine agonists in restless legs syndrome: A systematic review, quality assessment, and meta-analysis

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 (Deltamu) (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 Deltamu (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.

Levetiracetam in tardive dyskinesia: an open label study.

Levetiracetam (LEV), a novel antiepileptic drug, has demonstrated antidyskinetic effect in preclinical animal models of Parkinsons 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.

Real-time quantification of resting tremor in the Parkinson's disease

Resting tremor (RT) is one of the most frequent signs of the Parkinsons 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.

A data mining based approach for the EEG transient event detection and classification

An automated methodology which detects transient events in EEG recordings and classifies those as epileptic spikes, muscle activity, eye blinking activity and sharp alpha activity is presented. It is based on data mining algorithms and includes four stages: (I) EEG preprocessing and transient events detection, (II) clustering of transient events and feature extraction, (III) feature discretization and (IV) association rule mining and classification. The methodology is evaluated using a dataset of 25 EEG recordings and the obtained overall accuracy is 84.35%. The major advantage of our approach is that it is able to provide interpretation for the decisions made since it is based on a set of association rules.

Spontaneous intracranial hypotension: morphological findings and CSF flow dynamics studied by MRI

We report on a case of spontaneous intracranial hypotension (SIH) presenting with classic MR findings, such as diffuse smooth thickening and intense contrast enhancement of the dura matter, increased size of the pituitary gland and downward displacement of the brain. In this case an engorgement of the cavernous sinuses is reported as an additional imaging finding of SIH. Moreover, phase-contrast MR study of the CSF flow dynamics revealed at the level of the aqueduct a decrease of the systolic and diastolic flow volume of CSF. A normalization of the flow volume was observed when SIH subsided.

A wearable system for long-term ubiquitous monitoring of common motor symptoms in patients with Parkinson's disease

PERFORM is a system for the monitoring, assessment and management of patient with Parkinsons 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.

The Use of Kalman Filter in Biomedical Signal Processing

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.

PERFORM: A platform for monitoring and management of chronic neurodegenerative diseases: The Parkinson and Amyotrophic Lateral Sclerosis case

In this work we present a system for the monitoring and management of neurodegenerative diseases, such as the Parkinsons Disease (PD) and Amyotrophic Lateral Sclerosis (ALS). The purpose of the system is to monitor patients 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 patients environment.