Relevant bibliographies by topics / Nocturnal frontal lobe epilepsy (NFLE)

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  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Nocturnal frontal lobe epilepsy (NFLE)'

Author: Grafiati
Published: 4 June 2021
Last updated: 13 February 2022

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Journal articles on the topic "Nocturnal frontal lobe epilepsy (NFLE)"

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Rathore, Geetanjali, Paul Larsen, Manish Parakh, and Cristina Fernandez. "Choking at Night: A Case of Opercular Nocturnal Frontal Lobe Epilepsy." Case Reports in Pediatrics 2013 (2013): 1–3. http://dx.doi.org/10.1155/2013/606385.

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Frontal lobe seizures have a tendency to occur in sleep and in most cases occur exclusively insleep; these individuals are said to have nocturnal frontal lobe (NFLE). NFLE can be difficult to distinguish clinically from various other sleep disorders, particularly parasomnias, which also present with paroxysmal motor activity in sleep. Interictal and ictal EEG findings are frequently unremarkable or nonspecific in both parasomnias and NFLE making the diagnosis even more difficult. Nocturnal epilepsy should be suspected in patients with paroxysmal events at night characterized by high frequency, repetition, extrapyramidal features, and marked stereotypy of attacks. Here we present a 13-year-old female who was extensively worked up for choking episodes at night. On repeat video EEG she was found to have frontal opercular seizures. Once on Carbamazepine, her seizures completely resolved.
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Halász, Péter, Anna Kelemen, and Anna Szűcs. "Physiopathogenetic Interrelationship between Nocturnal Frontal Lobe Epilepsy and NREM Arousal Parasomnias." Epilepsy Research and Treatment 2012 (May 10, 2012): 1–8. http://dx.doi.org/10.1155/2012/312693.

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Aims. To build up a coherent shared pathophysiology of NFLE and AP and discuss the underlying functional network. Methods. Reviewing relevant published data we point out common features in semiology of events, relations to macro- and microstructural dynamism of NREM sleep, to cholinergic arousal mechanism and genetic aspects. Results. We propose that pathological arousals accompanied by confused behavior with autonomic signs and/or hypermotor automatisms are expressions of the frontal cholinergic arousal function of different degree, during the condition of depressed cognition by frontodorsal functional loss in NREM sleep. This may happen either if the frontal cortical Ach receptors are mutated in ADNFLE (and probably also in genetically not proved nonlesional cases as well), or without epileptic disorder, in AP, assuming gain in receptor functions in both conditions. This hypothesis incorporates the previous “liberation theory” of Tassinari and the “state dissociation hypothesis” of Bassetti and Terzaghi). We propose that NFLE and IGE represent epileptic disorders of the two antagonistic twin systems in the frontal lobe. NFLE is the epileptic facilitation of the ergotropic frontal arousal system whereas absence epilepsy is the epileptic facilitation of burst-firing working mode of the spindle and delta producing frontal thalamocortical throphotropic sleep system. Significance. The proposed physiopathogenesis conceptualize epilepsies in physiologically meaningful networks.
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Halász, Péter. "Are Absence Epilepsy and Nocturnal Frontal Lobe Epilepsy System Epilepsies of the Sleep/Wake System?" Behavioural Neurology 2015 (2015): 1–15. http://dx.doi.org/10.1155/2015/231676.

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System epilepsy is an emerging concept interpreting major nonlesional epilepsies as epileptic dysfunctions of physiological systems. I extend here the concept of reflex epilepsy to epilepsies linked to input dependent physiological systems. Experimental and clinical reseach data were collected to create a coherent explanation of underlying pathomechanism in AE and NFLE. We propose that AE should be interpreted as epilepsy linked to the corticothalamic burst-firing mode of NREM sleep, released by evoked vigilance level oscillations characterized by reactive slow wave response. In the genetic variation of NFLE the ascending cholinergic arousal system plays an essential role being in strong relationship with a gain mutation of the nicotinic acethylcholin receptors, rendering the arousal system hyperexcitable. I try to provide a more unitary interpretation for the variable seizure manifestation integrating them as different degree of pathological arosuals and alarm reactions. As a supporting hypothesis the similarity between arousal parasomnias and FNLE is shown, underpinned by overlaping pathomechanism and shared familiarity, but without epileptic features. Lastly we propose that both AE and NFLE are system epilepsies of the sleep-wake system representing epileptic disorders of the antagonistic sleep/arousal network. This interpretation may throw new light on the pathomechanism of AE and NFLE.
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Pisano, Fabio, Giuliana Sias, Alessandra Fanni, Barbara Cannas, António Dourado, Barbara Pisano, and Cesar A. Teixeira. "Convolutional Neural Network for Seizure Detection of Nocturnal Frontal Lobe Epilepsy." Complexity 2020 (March 31, 2020): 1–10. http://dx.doi.org/10.1155/2020/4825767.

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The Nocturnal Frontal Lobe Epilepsy (NFLE) is a form of epilepsy in which seizures occur predominantly during sleep. In other forms of epilepsy, the commonly used clinical approach mainly involves manual inspection of encephalography (EEG) signals, a laborious and time-consuming process which often requires the contribution of more than one experienced neurologist. In the last decades, numerous approaches to automate this detection have been proposed and, more recently, machine learning has shown very promising performance. In this paper, an original Convolutional Neural Network (CNN) architecture is proposed to develop patient-specific seizure detection models for three patients affected by NFLE. The performances, in terms of accuracy, sensitivity, and specificity, exceed by several percentage points those in the most recent literature. The capability of the patient-specific models has been also tested to compare the obtained seizure onset times with those provided by the neurologists, with encouraging results. Moreover, the same CNN architecture has been used to develop a cross-patient seizure detection system, resorting to the transfer-learning paradigm. Starting from a patient-specific model, few data from a new patient are enough to customize his model. This contribution aims to alleviate the task of neurologists, who may have a robust indication to corroborate their clinical conclusions.
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Kanner, Andres M. "Nocturnal Frontal Lobe Epilepsy: There is Bad, Good, and Very Good News!" Epilepsy Currents 7, no. 5 (September 2007): 131–33. http://dx.doi.org/10.1111/j.1535-7511.2007.00200.x.

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Surgical Treatment of Drug-Resistant Nocturnal Frontal Lobe Epilepsy. Nobili L, Francione S, Mai R, Cardinale F, Castana L, Tassi L, Sartori I, Didato G, Citterio A, Colombo N, Galli C, Lo Russo G, Cossu M. Brain 2007;130(Pt 2):561–573. Of the cases with nocturnal frontal lobe epilepsy (NFLE) 30% are refractory to antiepileptic medication, with several patients suffering from the effects of both ongoing seizures and disrupted sleep. From a consecutive series of 522 patients operated on for drug-resistant focal epilepsy, 21 cases (4%), whose frontal lobe seizures occurred almost exclusively (>90%) during sleep, were selected. All patients underwent a comprehensive pre-surgical evaluation, which included history, interictal EEG, scalp video-EEG monitoring, high-resolution MRI and, when indicated, invasive recording by stereo-EEG (SEEG). There were 11 males and 10 females, whose mean age at seizure onset was 6.2 years, mean age at surgery was 24.7 years and seizure frequency ranged from <20/month to >300/month. Nine patients reported excessive daytime sleepiness (EDS). Prevalent ictal clinical signs were represented by asymmetric posturing (6 cases), hyperkinetic automatisms (10 cases), combined tonic posturing and hyperkinetic automatisms (4 cases) and mimetic automatisms (1 case). All patients reported some kind of subjective manifestations. Interictal and ictal EEG provided lateralizing or localizing information in most patients. MRI was unrevealing in 10 cases and it showed a focal anatomical abnormality in one frontal lobe in 11 cases. Eighteen patients underwent a SEEG evaluation to better define the epileptogenic zone (EZ). All patients received a microsurgical resection in one frontal lobe, tailored according to pre-surgical evaluations. Two patients were operated on twice owing to poor results after the first resection. Histology demonstrated a Taylor-type focal cortical dysplasia (FCD) in 16 patients and an architectural FCD in 4. In one case no histological change was found. After a post-operative follow-up of at least 12 months (mean 42.5 months) all the 16 patients with a Taylor's FCD were in Engel's Class Ia and the other 5 patients were in Engel's Classes II or III. After 6 months post-surgery EDS had disappeared in the 9 patients who presented this complaint pre-operatively. It is concluded that patients with drug-resistant, disabling sleep-related seizures of frontal lobe origin should be considered for resective surgery, which may provide excellent results both on seizures and on epilepsy-related sleep disturbances. An accurate pre-surgical evaluation, which often requires invasive EEG recording, is mandatory to define the EZ. Further investigation is needed to explain the possible causal relationships between FCD, particularly Taylor-type, and sleep-related seizures, as observed in this cohort of NFLE patients.
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Bisulli, Francesca, Luca Vignatelli, Federica Provini, Chiara Leta, Elio Lugaresi, and Paolo Tinuper. "Parasomnias and nocturnal frontal lobe epilepsy (NFLE): Lights and shadows – Controversial points in the differential diagnosis." Sleep Medicine 12 (December 2011): S27—S32. http://dx.doi.org/10.1016/j.sleep.2011.10.008.

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Ali, Hamed, and Suzanne Stevens. "1219 A CASE OF A NIGHT TIME AFFAIR." Sleep 43, Supplement_1 (April 2020): A466. http://dx.doi.org/10.1093/sleep/zsaa056.1213.

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Abstract Introduction Sleep associated seizures especially Nocturnal Frontal Lobe Epilepsy (NFLE) represents a spectrum of challenging clinical manifestations presenting as complex nocturnal movements/behaviors, making the diagnosis often difficult. Report of Case A 64 y/o male, with history of ongoing complex movements occurring during his sleep, with no history of strokes or neurological deficits. Had extensive neurologic workup (all negative) including routine electroencephalogram (EEG), prolonged inpatient EEG (12 hours), and MRI of the brain. Home sleep study showing moderate obstructive sleep apnea (OSA) AHI 24/hour successfully treated with CPAP therapy (residual AHI 1.7/hour) with improved nighttime symptoms initially. Wife recalls events as happening only at night while sleep, as patient often confused upon waking up in the morning, at times appear to sit up and smack his lips. No nighttime hallucinations, sleep paralysis, or acting out dreams were reported. Had two episodes associated with tongue biting and loss of bladder control. Another episode happened after a daytime nap, patient went outside and was mowing his lawn, went “completely blank “, appeared confused. No daytime or nighttime seizures were ever noticed. Patient do not recall any of the above events. Repeat EEG was normal. MRI/MRA of the head /neck showed small tiny focus in left frontoparietal lobe, suggesting remote cortical ischemic injury. Polysomnography (PSG) with seizure montage showed Interictal epileptic discharges (IEDs) foci recorded in the frontal/frontopolar leads without accompanying body movements. Interictal spike and wave activity seen during stage N2. Initially treated with carbamazepine (had skin reaction) switched to levetiracetam with complete resolution of his symptoms. Conclusion This case illustrates the importance of reviewing the clinical history, behavior semiology, and diagnostic ancillary testing such as polysomnography with EEG monitoring in distinguishing nocturnal epileptic seizures from other nocturnal complex behavior disorders and parasomnias.
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SIDDIQUI, MOHD, GEETIKA SRIVASTAVA, and HASAN SAEED. "Diagnosis of Nocturnal Frontal Lobe Epilepsy (NFLE) Sleep Disorder Using Short Time Frequency Analysis of PSD Approach Applied on EEG Signal." Biomedical and Pharmacology Journal 9, no. 1 (April 28, 2016): 393–403. http://dx.doi.org/10.13005/bpj/951.

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Sharma, Manish, Jainendra Tiwari, Virendra Patel, and U. Rajendra Acharya. "Automated Identification of Sleep Disorder Types Using Triplet Half-Band Filter and Ensemble Machine Learning Techniques with EEG Signals." Electronics 10, no. 13 (June 25, 2021): 1531. http://dx.doi.org/10.3390/electronics10131531.

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A sleep disorder is a medical condition that affects an individual’s regular sleeping pattern and routine, hence negatively affecting the individual’s health. The traditional procedures of identifying sleep disorders by clinicians involve questionnaires and polysomnography (PSG), which are subjective, time-consuming, and inconvenient. Hence, an automated sleep disorder identification is required to overcome these limitations. In the proposed study, we have proposed a method using electroencephalogram (EEG) signals for the automated identification of six sleep disorders, namely insomnia, nocturnal frontal lobe epilepsy (NFLE), narcolepsy, rapid eye movement behavior disorder (RBD), periodic leg movement disorder (PLM), and sleep-disordered breathing (SDB). To the best of our belief, this is one of the first studies ever undertaken to identify sleep disorders using EEG signals employing cyclic alternating pattern (CAP) sleep database. After sleep-scoring EEG epochs, we have created eight different data subsets of EEG epochs to develop the proposed model. A novel optimal triplet half-band filter bank (THFB) is used to obtain the subbands of EEG signals. We have extracted Hjorth parameters from subbands of EEG epochs. The selected features are fed to various supervised machine learning algorithms for the automated classification of sleep disorders. Our proposed system has obtained the highest accuracy of 99.2%, 98.2%, 96.2%, 98.3%, 98.8%, and 98.8% for insomnia, narcolepsy, NFLE, PLM, RBD, and SDB classes against normal healthy subjects, respectively, applying ensemble boosted trees classifier. As a result, we have attained the highest accuracy of 91.3% to identify the type of sleep disorder. The proposed method is simple, fast, efficient, and may reduce the challenges faced by medical practitioners during the diagnosis of various sleep disorders accurately in less time at sleep clinics and homes.
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Sharma, Manish, Virendra Patel, Jainendra Tiwari, and U. Rajendra Acharya. "Automated Characterization of Cyclic Alternating Pattern Using Wavelet-Based Features and Ensemble Learning Techniques with EEG Signals." Diagnostics 11, no. 8 (July 30, 2021): 1380. http://dx.doi.org/10.3390/diagnostics11081380.

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Sleep is highly essential for maintaining metabolism of the body and mental balance for increased productivity and concentration. Often, sleep is analyzed using macrostructure sleep stages which alone cannot provide information about the functional structure and stability of sleep. The cyclic alternating pattern (CAP) is a physiological recurring electroencephalogram (EEG) activity occurring in the brain during sleep and captures microstructure of the sleep and can be used to identify sleep instability. The CAP can also be associated with various sleep-related pathologies, and can be useful in identifying various sleep disorders. Conventionally, sleep is analyzed using polysomnogram (PSG) in various sleep laboratories by trained physicians and medical practitioners. However, PSG-based manual sleep analysis by trained medical practitioners is onerous, tedious and unfavourable for patients. Hence, a computerized, simple and patient convenient system is highly desirable for monitoring and analysis of sleep. In this study, we have proposed a system for automated identification of CAP phase-A and phase-B. To accomplish the task, we have utilized the openly accessible CAP sleep database. The study is performed using two single-channel EEG modalities and their combination. The model is developed using EEG signals of healthy subjects as well as patients suffering from six different sleep disorders namely nocturnal frontal lobe epilepsy (NFLE), sleep-disordered breathing (SDB), narcolepsy, periodic leg movement disorder (PLM), insomnia and rapid eye movement behavior disorder (RBD) subjects. An optimal orthogonal wavelet filter bank is used to perform the wavelet decomposition and subsequently, entropy and Hjorth parameters are extracted from the decomposed coefficients. The extracted features have been applied to different machine learning algorithms. The best performance is obtained using ensemble of bagged tress (EBagT) classifier. The proposed method has obtained the average classification accuracy of 84%, 83%, 81%, 78%, 77%, 76% and 72% for NFLE, healthy, SDB, narcolepsy, PLM, insomnia and RBD subjects, respectively in discriminating phases A and B using a balanced database. Our developed model yielded an average accuracy of 78% when all 77 subjects including healthy and sleep disordered patients are considered. Our proposed system can assist the sleep specialists in an automated and efficient analysis of sleep using sleep microstructure.
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Dissertations / Theses on the topic "Nocturnal frontal lobe epilepsy (NFLE)"

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Puligheddu, Monica Maria Francesca <1969&gt. "Rationale for an adjunctive therapy with fenofibrate in pharmacoresistant nocturnal frontal lobe epilepsy (NFLE)." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2015. http://amsdottorato.unibo.it/7057/.

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Nocturnal Frontal Lobe Epilepsy (NFLE) is characterized by onset during infancy or childhood with persistence in adulthood, family history of similar nocturnal episodes simulating non-REM parasomnias (sleep terrors or sleepwalking), general absence of morphological substrates, often by normal interictal electroencephalographical recordings (EEGs) during wakefulness. A family history of epilepsy may be present with Mendelian autosomal dominant inheritance has been described in some families. Recent studies indicate the involvement of neuronal nicotinic acetylcholine receptors (nAChRs) in the molecular mechanisms of NFLE. Mutations in the genes encoding for the α4 (CHRNA4) and ß2 (CHRNB2) subunits of the nAChR induce changes in the biophysical properties of nAChR, resulting generally in a “gain of function”. Preclinical studies report that activation of a nuclear receptor called type peroxisome proliferator-activated receptor (PPAR-α) by endogenous molecules or by medications (e.g. fenofibrate) reduces the activity of the nAChR and, therefore, may decrease the frequency of seizures. Thus, we hypothesize that negative modulation of nAChRs might represent a therapeutic strategy to be explored for pharmacological treatment of this form of epilepsy, which only partially responds to conventional antiepileptic drugs. In fact, carbamazepine, the current medication for NFLE, abolishes the seizures only in one third of the patients. The aim of the project is: 1)_to verify the clinical efficacy of adjunctive therapy with fenofibrate in pharmacoresistant NFLE and ADNFLE patients; focousing on the analysis of the polysomnographic action of the PPAR- agonist (fenofibrate). 2)_to demonstrate the subtended mechanism of efficacy by means of electrophysiological and behavioral experiments in an animal model of the disease: particularly, transgenic mice carrying the mutation in the nAChR 4 subunit (Chrna4S252F) homologous to that found in the humans. Given that a PPAR-α agonist, FENOFIBRATE, already clinically utilized for lipid metabolism disorders, provides a promising therapeutic avenue in the treatment of NFLE\ADNFLE.
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Rossi, Magi Lorenzo. "Graph-based analysis of brain resting-state fMRI data in nocturnal frontal lobe epileptic patients." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2015. http://amslaurea.unibo.it/8332/.

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Il lavoro che ho sviluppato presso l'unità di RM funzionale del Policlinico S.Orsola-Malpighi, DIBINEM, è incentrato sull'analisi dati di resting state - functional Magnetic Resonance Imaging (rs-fMRI) mediante l'utilizzo della graph theory, con lo scopo di valutare eventuali differenze in termini di connettività cerebrale funzionale tra un campione di pazienti affetti da Nocturnal Frontal Lobe Epilepsy (NFLE) ed uno di controlli sani. L'epilessia frontale notturna è una peculiare forma di epilessia caratterizzata da crisi che si verificano quasi esclusivamente durante il sonno notturno. Queste sono contraddistinte da comportamenti motori, prevalentemente distonici, spesso complessi, e talora a semiologia bizzarra. L'fMRI è una metodica di neuroimaging avanzata che permette di misurare indirettamente l'attività neuronale. Tutti i soggetti sono stati studiati in condizioni di resting-state, ossia di veglia rilassata. In particolare mi sono occupato di analizzare i dati fMRI con un approccio innovativo in campo clinico-neurologico, rappresentato dalla graph theory. I grafi sono definiti come strutture matematiche costituite da nodi e links, che trovano applicazione in molti campi di studio per la modellizzazione di strutture di diverso tipo. La costruzione di un grafo cerebrale per ogni partecipante allo studio ha rappresentato la parte centrale di questo lavoro. L'obiettivo è stato quello di definire le connessioni funzionali tra le diverse aree del cervello mediante l'utilizzo di un network. Il processo di modellizzazione ha permesso di valutare i grafi neurali mediante il calcolo di parametri topologici che ne caratterizzano struttura ed organizzazione. Le misure calcolate in questa analisi preliminare non hanno evidenziato differenze nelle proprietà globali tra i grafi dei pazienti e quelli dei controlli. Alterazioni locali sono state invece riscontrate nei pazienti, rispetto ai controlli, in aree della sostanza grigia profonda, del sistema limbico e delle regioni frontali, le quali rientrano tra quelle ipotizzate essere coinvolte nella fisiopatologia di questa peculiare forma di epilessia.
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Calandra, Buonaura Giovanna <1973&gt. "Wavelet analysis of heart rate variability related to nocturnal frontal lobe epilepsy seizures." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2010. http://amsdottorato.unibo.it/2782/.

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Introduction: Nocturnal frontal lobe epilepsy (NFLE) is a distinct syndrome of partial epilepsy whose clinical features comprise a spectrum of paroxysmal motor manifestations of variable duration and complexity, arising from sleep. Cardiovascular changes during NFLE seizures have previously been observed, however the extent of these modifications and their relationship with seizure onset has not been analyzed in detail. Objective: Aim of present study is to evaluate NFLE seizure related changes in heart rate (HR) and in sympathetic/parasympathetic balance through wavelet analysis of HR variability (HRV). Methods: We evaluated the whole night digitally recorded video-polysomnography (VPSG) of 9 patients diagnosed with NFLE with no history of cardiac disorders and normal cardiac examinations. Events with features of NFLE seizures were selected independently by three examiners and included in the study only if a consensus was reached. Heart rate was evaluated by measuring the interval between two consecutive R-waves of QRS complexes (RRi). RRi series were digitally calculated for a period of 20 minutes, including the seizures and resampled at 10 Hz using cubic spline interpolation. A multiresolution analysis was performed (Daubechies-16 form), and the squared level specific amplitude coefficients were summed across appropriate decomposition levels in order to compute total band powers in bands of interest (LF: 0.039062 - 0.156248, HF: 0.156248 - 0.624992). A general linear model was then applied to estimate changes in RRi, LF and HF powers during three different period (Basal) (30 sec, at least 30 sec before seizure onset, during which no movements occurred and autonomic conditions resulted stationary); pre-seizure period (preSP) (10 sec preceding seizure onset) and seizure period (SP) corresponding to the clinical manifestations. For one of the patients (patient 9) three seizures associated with ictal asystole were recorded, hence he was treated separately. Results: Group analysis performed on 8 patients (41 seizures) showed that RRi remained unchanged during the preSP, while a significant tachycardia was observed in the SP. A significant increase in the LF component was instead observed during both the preSP and the SP (p<0.001) while HF component decreased only in the SP (p<0.001). For patient 9 during the preSP and in the first part of SP a significant tachycardia was observed associated with an increased sympathetic activity (increased LF absolute values and LF%). In the second part of the SP a progressive decrease in HR that gradually exceeded basal values occurred before IA. Bradycardia was associated with an increase in parasympathetic activity (increased HF absolute values and HF%) contrasted by a further increase in LF until the occurrence of IA. Conclusions: These data suggest that changes in autonomic balance toward a sympathetic prevalence always preceded clinical seizure onset in NFLE, even when HR changes were not yet evident, confirming that wavelet analysis is a sensitive technique to detect sudden variations of autonomic balance occurring during transient phenomena. Finally we demonstrated that epileptic asystole is associated with a parasympathetic hypertonus counteracted by a marked sympathetic activation.
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Books on the topic "Nocturnal frontal lobe epilepsy (NFLE)"

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Nobili, Lino, Paola Proserpio, Steve Gibbs, and Giuseppe Plazzi. Sleep and epilepsy. Edited by Sudhansu Chokroverty, Luigi Ferini-Strambi, and Christopher Kennard. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199682003.003.0028.

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This chapter on sleep and epilepsy examines the activating and deactivating properties of NREM and REM sleep states on interictal epileptic activity and seizures. It reviews specific epileptic syndromes in which seizures manifest a tendency to present exclusively or predominantly during sleep or upon wakening. Particular attention is paid to the description of the different forms of nocturnal frontal lobe epilepsy: autosomal dominant and lesional. There is also a discussion of the negative bidirectional relationship between epilepsy and sleep disorders (sleep apneas and parasomnias) and the effect of pharmacological and nonpharmacological treatments. Finally, a brief review of the relationship between sleep and sudden unexpected death in epilepsy is given.
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Book chapters on the topic "Nocturnal frontal lobe epilepsy (NFLE)"

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Tinuper, Paolo, and Francesca Bisulli. "Autosomal Dominant Nocturnal Frontal Lobe Epilepsy." In Atlas of Epilepsies, 1125–34. London: Springer London, 2010. http://dx.doi.org/10.1007/978-1-84882-128-6_166.

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Tinuper, P., G. Plazzi, F. Provini, A. Cerullo, and E. Lugaresi. "The Syndrome of Nocturnal Frontal Lobe Epilepsy." In Somatic and Autonomic Regulation in Sleep, 125–35. Milano: Springer Milan, 1997. http://dx.doi.org/10.1007/978-88-470-2275-1_8.

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Motamedi, Gholam, and Ronald Lesser. "Familial and Sporadic Nocturnal Frontal Lobe Epilepsy (NFLE)— Electroclinical Features." In Epilepsy, 445–50. CRC Press, 2011. http://dx.doi.org/10.1201/b10866-41.

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"Nocturnal Frontal Lobe Epilepsy." In Epilepsy, 442–43. CRC Press, 2011. http://dx.doi.org/10.1201/b10866-40.

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Milton, John, Austin Quan, and Ivan Osorio. "Nocturnal Frontal Lobe Epilepsy." In Epilepsy, 501–10. CRC Press, 2011. http://dx.doi.org/10.1201/b10866-47.

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Tinuper, Paolo, Elio Lugaresi, Federico Vigevano, and Samuel F. Berkovic. "Nocturnal frontal lobe epilepsy." In Epilepsy and Movement Disorders, 97–110. Cambridge University Press, 2001. http://dx.doi.org/10.1017/cbo9780511629419.008.

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Brown, Molly, and Gregory Mathews. "Autosomal Dominant Nocturnal Frontal Lobe Epilepsy." In Epilepsy, 469–75. CRC Press, 2011. http://dx.doi.org/10.1201/b10866-44.

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"Autosomal Dominant Nocturnal Frontal Lobe Epilepsy." In Pediatric Epilepsy Case Studies, 299–306. CRC Press, 2008. http://dx.doi.org/10.1201/b13612-47.

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Rona, Sabine. "Nocturnal Paroxysmal Dystonia and Frontal Lobe Epilepsy." In Epilepsy and Sleep, 241–49. Elsevier, 2001. http://dx.doi.org/10.1016/b978-012216770-6/50035-4.

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Steinlein, Ortrud K. "Genetic heterogeneity in familial nocturnal frontal lobe epilepsy." In Progress in Brain Research, 1–15. Elsevier, 2014. http://dx.doi.org/10.1016/b978-0-444-63326-2.00001-6.

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Conference papers on the topic "Nocturnal frontal lobe epilepsy (NFLE)"

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Pisano, B., B. Cannas, G. Milioli, A. Montisci, F. Pisano, M. Puligheddu, G. Sias, and A. Fanni. "Autosomal dominant nocturnal frontal lobe epilepsy seizure characterization through wavelet transform of eeg records and self organizing maps." In 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2016. http://dx.doi.org/10.1109/mlsp.2016.7738861.

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