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Electroencephalographic (EEG) signal processing and machine learning can support neurologists’ work in discriminating Psychogenic Non-Epileptic Seizure (PNES) from epilepsy. PNES represents a neurological disease often misdiagnosed. Although the symptoms of PNES patients can be similar to those exhibited by epileptic patients, EEG signals during a psychogenic seizure do not show ictal patterns such as in epilepsy. Therefore, PNES diagnosis requires long-term EEG video. Applying signal processing and machine-learning methodologies could help clinicians find helpful information in the clinical diagnosis of PNES by analyzing EEG signals registered in resting conditions and in a short time. These methodologies should prevent long EEG recording sessions and avoid inducing seizures in the subjects. The aim of our study is to develop and validate several machine-learning models on a larger dataset, consisting of 225 EEGs (75 healthy, 75 PNES, and 75 subjects with epilepsy). A deep analysis of our results shows that changes in the evaluation strategy led to changes in accuracy from 45% to 83.98% for a standard Light Gradient Boosting Machine (LGBM) classifier. Our findings suggest that it is necessary to operate a very rigorous control in terms of experimental data collection (patient selection, signal acquisition) and terms of validation strategies to obtain and reproducible results.

Mutations of PRRT2, which encodes proline-rich transmembrane protein 2, are associated with heterogeneous phenotypes including benign familial infantile seizures (BFIS) and/or familial paroxysmal kinesigenic dystonia (PKD). Here, we performed mutation screening of PRRT2 in six Italian families with BFIS/PKD phenotypes. The mutation, c.649dupC (p.Arg217ProfsX8), was found in two families with BFIS phenotype. In a third BFIS family, a missense mutation, c.718C/T (R240X), was identified. All these mutations co-segregated with the disease and were not observed in 100 controls of matched ancestry. In one BFIS family that carried the c.649dupC mutation, one affected member developed afebrile focal seizures and died at age of 14 years of probable sudden unexpected death in epilepsy, while his brother also had simple febrile convulsions (FC) and performed poorly on complex psychomotor functioning. In another family carrying the c.718C/T mutation, two of three affected members also had simple FC. This study enlarges the clinical spectrum related to PPRT2 mutations and underscores the complexity of the phenotypic consequences of mutations in this gene.

Mesial temporal lobe epilepsy (MTLE) is the most common type of refractory epilepsy and is usually associated with hippocampal sclerosis (Hs). The pathogenesis of MTLE involves many biological pathways, some of which seem to be regulated by microRNAs (miRNAs). Increasing evidence shows that single nucleotide polymorphisms (SNPs) or mutations in miRNAs sequence may affect the processing and function of miRNAs and participate in the occurrence of diseases. In this study, the effect of the SNP of one neuronal miRNA, miR-124, on susceptibility to MTLE was investigated using a case control study. To understand the role, a common C/G polymorphism designated rs531564 in the molecular mechanisms of MTLE, we sought to determine whether this genetic variant could influence susceptibility to disease in a cohort of 307 MTLE patients and 306 healthy controls, using TaqMan allelic discrimination assay, on an Applied Biosystems PCR platform. No statistically significant differences were found in the allele or genotype distributions of the miR-124 rs531564 polymorphism among MTLE patients and MTLE-free control subjects (p > 0.05). Our results demonstrate that this SNP has no major role in genetic susceptibility to MTLE, at least in the population studied here.

OBJECTIVE: To compare circadian autonomic fluctuations in patients with Parkinson's Disease (PD) with or without REM sleep behavior disorder (RBD) by using heart rate variability (HRV) analysis. METHODS: This is a case-control study including 20 PD patients with RBD (PD-RBD) and 20 PD patients without RBD (PD). In all patients, we measured the components of HRV in the frequency domain during 24-h with daytime and night time recordings. Selected variables considered were low-frequency (LF) influenced by the sympathetic system and high-frequency (HF) influenced by the parasympathetic system. Moreover, we calculated night-to-day ratio for both LF (cardiac sympathetic index) and HF (cardiac parasympathetic index) spectral components. Video-polysomnography was performed in all patients to diagnose RBD. RESULTS: Both nocturnal LF and HF spectral power values were significantly higher in PD-RBD patients than in PD patients (P < 0.001 and P = 0.004 respectively). Moreover, in PD-RBD patients LF and HF values were higher at night than during the day while no difference between night time and daytime values was observed in patients with PD. Cardiac sympathetic index value was significantly higher in PD-RBD patients (median 1.83, range 1.65-3.66) than in PD patients (median 0.93, range 0.44-1.3) without overlap of individual values between groups (accuracy 100%). By contrast, cardiac parasympathetic index had sensitivity of 45% and specificity of 100% for differentiating between PD groups. CONCLUSIONS: Cardiac sympathetic index distinguishes PD-RBD patients from those with PD on an individual basis, thus representing a valid help in everyday clinical practice for screening of RBD in PD patients.

A new research methodology named Brain Computer Interface (BCI) studies novel human-computer interactions; by means of BCI electronics devices, paralyzed patients are able to interact with the environment using no muscular contractions. This technique provides an external electronics support to all persons with severe motor disabilities, by acquiring in continuous mode the electroencephalogram (EEG) signals and operating some processing to control a computer or other domotics devices. Patients are so allowed to control external devices or to communicate simple messages through the computer, just concentrating their attention either on codified movements or on a letter or icon on a digital keyboard. The use of a customized and optimized spatial filtering technique embedded in the BCI system, based on the detection of the Electroencephalographic activity, improves the accuracy of BCI system itself, thanks to the explicit separation of the signal activity of interest from artefact signals. In this chapter, after an overview of the state-of-the-art research on BCI systems, the spatial filtering problem in EEG signals acquisition will be illustrated. In particular, a spatial filtering algorithm, known as ICA (Independent Component Analysis) and its application will be discussed. Finally, the design and implementation of an embedded system for EEG signals acquisition and real-time processing for BCI applications will be presented. The system is based onto a very performing and reconfigurable hardware platform. Moreover ICA algorithm has been implemented for noise reduction and artifacts removal.

This paper presents the design of an amplifier for EEG signals. The study of EEG signals is very important for the detection of cerebral rhythmic and any abnormalities. Brain waves are classified with respect to human activity; these signals represent an extremely low dynamic range (5 – 200µV), in a frequency band of 0.5 – 40Hz. Brain electrical potentials are acquired through the use of special surface electrodes, with a monopolar mounting: sensing electrode is placed over the skull of interest, while along the mid line a second electrode is positioned for reference electrode. This work describes the single electronic stage for the conditioning of the analog acquired signal. The final simulations of the whole electronic circuit are showed and a comparison with the devices on the market is reported.

Partially or completely paralyzed patients can benefit from Brain Computer Interface (BCI) in which a continuous recording of electroencephalogram (EEG) is required, operating some processing and classification to control a computer or other devices. Patients are so allowed to control external devices or to communicate simple messages through the computer, just concentrating their attention on codified movements or on a letter or icon on a digital keyboard. Using an on-purpose optimized spatial filtering technique in a BCI system, based on the ElectroEncephaloGraphic activity detection, enables to improve its accuracy, due to the explicit separation of the signal activity of interest from non-interesting signals. In this paper, a novel implementation of the spatial filtering ICA algorithm, onto a very performing and flexible hardware platform, is presented. The designed system enables the acquisition and the subsequent ICA elaboration of ElectroEncephaloGraphic signals in real-time, resulting so very effective for BCI applications. The efficiency of the implemented technique has been experimentally demonstrated.

Summary Objective To evaluate if a standard awake EEG recording in the morning is superior to afternoon awake EEG session in detecting generalized epileptiform discharges (GEDs) in patients with juvenile myoclonic epilepsy (JME). Methods The study group included 29 consecutive patients (23 women; mean age 22.3±6.3 years; age at onset of JME 15.4±3.4 years) with JME. Out of 29 patients 5 were untreated, 9 patients were treated with valproate, 8 with lamotrigine, 6 with levetiracetam and 1 patient with valproate plus phenobarbital. Two routine consecutive interictal EEG recordings were performed at 9a.m. and at 3p.m., respectively, while the subject was awake, on the same day after a a regular nocturnal sleep at own home. Results The morning EEG recording showed GEDs (i.e., generalized polispike and waves, photoparoxysmal response, or both). in 20/29 patients. In 15 of these 20 patients, the afternoon recording was normal and this difference was statistically significant ( p ≤0.001). Moreover, there was a striking reduction of GEDs in three of the remaining five patients. Nine/29 patients had both morning and afternoon EEG recording normal. Conclusions The results of this study have illustrated a significant greater rate of detection of generalized epileptiform abnormalities by performing standard awake EEG in the morning in comparison with an afternoon session.

Paroxysmal dysarthria is a transient dysfunction in the initiation, control or coordination of speech characterized by sudden onset, stereotyped patterns and short duration, occurring many times during the day [1]. The combination of paroxysmal episodes of severe dysarthria and ataxia has rarely been reported in multiple sclerosis (MS) patients and in other neurological diseases such a Beh1et’s and midbrain infarction [1–3]. We report a case of a patient with relapsing–remitting multiple sclerosis (MS–RR) who showed paroxysmal dysarthria as the only manifestation of an acute brainstem relapse. Treatment with carbamazepine was not effective for this patient, but lamotrigine completely suppressed paroxysmal attacks. A 36-year-old woman with a 14-year history of clinically definite multiple sclerosis, free of relapses for about 1 year, presented to our clinic in January 2010 with episodes of severe dysarthria lasting a few seconds, occasionally accompanied by rising burning sensation. Initially, the episodes occurred five or six times a day; later, despite steroid treatment (intravenous methylprednisolone 500 mg/ day for 5 days), the episodes became more frequent, once every 2–5 min. Neurological examination between the attacks showed only residual signs from previous relapses with decreased paresthesia at lower limbs, brisk tendon reflexes and Babinski sign. EEG recording during the attacks showed no paroxysmal discharges (see video). A brain MRI revealed a new right paramedian midbrain lesion below the level of the red nucleus (Fig. 1a) with gadolinium enhancement (Fig 1b). Carbamazepine (orally, 600 mg/day for 10 days) was started with no improvement, while lamotrigine (orally, 100 mg/day) markedly improved paroxysmal dysarthria, resulting in an almost complete suppression of the attacks in about 2 weeks. Because the discontinuation of Lamotrigine led to an immediate recurrence of paroxysmal symptoms, the drug was administered again at the dose of 100 mg/day and the patient remained free from the paroxysmal episodes for a 10-month follow up. A new brain MRI performed 3 months after the beginning of dysarthria showed the disappearance of midbrain lesion enhancement (Fig. 1c). We described one MS patient with episodes of severe dysarthria as the only manifestation of an acute relapse. Brain MRI showed a new right paramedian midbrain lesion below the level of the red nucleus with gadolinium enhancement. In previous reports [1–4], paroxysmal dysarthria, with or without ataxia, has rarely been reported in MS patients and in other neurological diseases such a Beh1et’s and midbrain infarction. All described patients had in common a lower midbrain lesion below the level of the red nucleus, suggesting that an interruption of the cerebellothalamocortical pathway inducing parietal diaschisis and, Electronic supplementary material The online version of this article (doi:10.1007/s00415-011-5901-8) contains supplementary material, which is available to authorized users.

Background REM sleep behaviour disorder (RBD) is a parasomnia characterized by suppression of muscular atonia and abnormal, often violent, motor behaviours during REM sleep. RBD can be either idiopathic or associated with degenerative disorders, such as dementia with Lewy bodies (LBD), multiple system atrophy (MSA), progressive sopranuclear paralysis (PSP) or Parkinson’s disease (PD). Myocardial 123Metaiodiobenzylguanidine (MIBG) enables the assessment of postganglionic sympathetic cardiac never terminals. Reduced myocardial MIBG uptake of the tracer suggests cardiac sympathetic denervation. MIBG uptake allows a differentiation between Lewy body disease and taupathies and other parkinsonisms. Indeed cardiac innervation is impaired in nearly all patients with PD and LBD, while it is preserved in patients with parkinsonisms, such as MSA and PSP. Recently, decreased MIBG uptake has been also described in patients with RBD.

Sleep apnoea syndrome (SAS) is a disease consisting in the nocturnal cessation of oronasal airflow at least 10 seconds in duration. The standard method for SAS diagnosis is the polysomnographic exam (PSG). However it does not permit a mass screening because it has high cost and requires long term monitoring. This paper presents a preliminary software system prototype for snoring signal analysis, whose main goal is to support the doctor in SAS diagnosis and patient follow-up. The design of the system is modular to allow a future hardware implementation in a portable device for personal snore collection and monitoring.

Background Partial epilepsy represents almost 70% of the epileptic syndrome. It is characterized by partial seizures that arise from the restricted area of the cerebral cortex. Temporal lobe epilepsy (TLE) is the most frequent form of cryptogenic partial epilepsies [1]. The mesial temporal sclerosis is the pathologic abnormality most frequently detected in post mortem studies of subjects with TLE. Voxel-based morphometry (VBM) is a new MRI tool that has been developed to assess differences of brain tissue volumes between subject groups and has been applied in various studies of patients with TLE [2]. In refractory TLE (rTLE) gray (GM) and white (WM) matter abnormalities are not confined to the hippocampus but also are found in extrahippocampal structures [3]. Less is known about mild TLE (mTLE). In the present work, we used optimized voxel-based morphometry (VBM) to identify GM abnormalities beyond the hippocampus in rTLE and in mTLE with evidence of hippocampal sclerosis (HS). Materials and methods Brain MRI and VBM of GM with modulation was performed on 30 unrelated patients with mTLE (56% women; mean age 35.6 + 15.2 years), 19 patients with rTLE (52% women; mean age 38.4 + 17.4 years) and 37 healthy controls (25 women, mean age 37.3 + 10.6 years). MRI diagnosis of MTS was based on the atrophy of the hippocampal formation and/or mesial temporal hyperintensity on FLAIR or T2 images, or both. Results