Patient-Specific Epileptic Seizure Prediction in Long-Term Scalp EEG Signal Using Multivariate Statistical Process Control

An accurate epileptic seizure prediction algorithm can alleviate the problem and reduce risks in the life of a patient suffering from epilepsy. The main motive of this work is to propose a model which can predict seizures well in advance of its occurrence. Multivariate statistical process control (MSPC) has been used for seizure predictions in long-term scalp EEG signal. It has been observed that excessive neuronal activity in the preictal period of seizure changes the electrical characteristic from chaotic to rhythmic behavior. These changes have been utilized for prediction. Eight temporal based features are used for predicting the seizures by using multivariate statistical process control, which is widely known as an anomaly monitoring method. 90 seizures from the CHB-MIT EEG data of ten patients are analyzed.ResultThe results of the proposed method demonstrated that 80 seizures out of 90 in preictal period were correctly predicted prior to the seizure onset, thereby giving a sensitivity of 88.89%. The false positive rate is observed to 0.39 per hour.ConclusionThis study proposed a temporal based patient-specific epileptic seizure prediction method using MSPC in long-term scalp EEG signals. It also provides the possibility of realizing an EEG-based epileptic seizure prediction system which requires less computational power.SignificanceThe proposed method does not require preictal data for modeling. The extracted features are computationally easy. The tested result shows good accuracy on the CHB-MIT data base.     

Extraction of reproducible seizure patterns based on EEG scalp correlations

The objective of this work is to identify similarities in the spatio-temporal dynamics of epileptic seizures, record with scalp EEG. A comprehensive method is proposed and applied in EEG of the patients who suffer from temporal lobe epilepsy. The method is based on the computation of the time-varying degree of non linear correlation between scalp electrodes at seizure onset and during seizure spread, determined by a nonlinear regression analysis. The quantification and coding of these similarity relations allow the comparison between two epileptic networks. Results show that reproducible patterns may be extracted from different seizures of the same patient and confirm the existence of different subtypes of temporal lobe epilepsy.     

Nonlinear analysis of EEG signals: Surrogate data analysis

ObjectivesThe electroencephalogram (EEG) signal contains information about the state and condition of the brain. The aim of the study is to conduct a nonlinear analysis of the EEG signals and to compare the differences in the nonlinear characteristics of the EEG during normal state and the epileptic state.DataThe EEG data used for this study – which consisted of epileptic EEG and normal EEG – were obtained from the EEG database available with the Bonn University, Germany.ResultsThe attractors seen in normal and epileptic human brain dynamics were studied and compared. Surrogate data analyses were conducted on two nonlinear measures, namely the largest Lyapunov exponent and the correlation dimension, to test the hypothesis whether EEG signals were in accordance with linear stochastic models.DiscussionsThe existence of deterministic chaos in brain activity is confirmed by the existence of a chaotic attractor; also, saturation of the correlation dimension towards a definite value is the manifestation of a deterministic dynamics. Also a reduction is observed between the dimensionalities of the brain attractors from normal state to the epileptic state. The evaluation of the largest Lyapunov exponent also confirms the lowering of complexity during an episode of seizure.ConclusionIn case of Lyapunov exponent of EEG data, the change due to surrogating is small suggesting that it is not representing the system complexity properly but there is a marked change in the case of correlation dimension value due to surrogating.     

A multi-context learning approach for EEG epileptic seizure detection

Background

Epilepsy is a neurological disease characterized by unprovoked seizures in the brain. The recent advances in sensor technologies allow researchers to analyze the collected biological records to improve the treatment of epilepsy. Electroencephalogram (EEG) is the most commonly used biological measurement to effectively capture the abnormalities of different brain areas during the EEG seizures. To avoid manual visual inspection from long-term EEG readings, automatic epileptic EEG seizure detection has become an important research issue in bioinformatics.

Results

We present a multi-context learning approach to automatically detect EEG seizures by incorporating a feature fusion strategy. We generate EEG scalogram sequences from the EEG records by utilizing waveform transform to describe the frequency content over time. We propose a multi-stage unsupervised model that integrates the features extracted from the global handcrafted engineering, channel-wise deep learning, and EEG embeddings, respectively. The learned multi-context features are subsequently merged to train a seizure detector.

Conclusions

To validate the effectiveness of the proposed approach, extensive experiments against several baseline methods are carried out on two benchmark biological datasets. The experimental results demonstrate that the representative context features from multiple perspectives can be learned by the proposed model, and further improve the performance for the task of EEG seizure detection.

     

Analysis of Epileptic Discharges from Implanted Subdural Electrodes in Patients with Sturge-Weber Syndrome

ObjectiveAlmost two-thirds of patients with Sturge-Weber syndrome (SWS) have epilepsy, and half of them require surgery for it. However, it is well known that scalp electroencephalography (EEG) does not demonstrate unequivocal epileptic discharges in patients with SWS. Therefore, we analyzed interictal and ictal discharges from intracranial subdural EEG recordings in patients treated surgically for SWS to elucidate epileptogenicity in this disorder.MethodsFive intractable epileptic patients with SWS who were implanted with subdural electrodes for presurgical evaluation were enrolled in this study. We examined the following seizure parameters: seizure onset zone (SOZ), propagation speed of seizure discharges, and seizure duration by visual inspection. Additionally, power spectrogram analysis on some frequency bands at SOZ was performed from 60 s before the visually detected seizure onset using the EEG Complex Demodulation Method (CDM).ResultsWe obtained 21 seizures from five patients for evaluation, and all seizures initiated from the cortex under the leptomeningeal angioma. Most of the patients presented with motionless staring and respiratory distress as seizure symptoms. The average seizure propagation speed and duration were 3.1 ± 3.6 cm/min and 19.4 ± 33.6 min, respectively. Significant power spectrogram changes at the SOZ were detected at 10–30 Hz from 15 s before seizure onset, and at 30–80 Hz from 5 s before seizure onset.SignificanceIn patients with SWS, seizures initiate from the cortex under the leptomeningeal angioma, and seizure propagation is slow and persists for a longer period. CDM indicated beta to low gamma-ranged seizure discharges starting from shortly before the visually detected seizure onset. Our ECoG findings indicate that ischemia is a principal mechanism underlying ictogenesis and epileptogenesis in SWS.     

A Physiology-Based Seizure Detection System for Multichannel EEG

Background

Epilepsy is a common chronic neurological disorder characterized by recurrent unprovoked seizures. Electroencephalogram (EEG) signals play a critical role in the diagnosis of epilepsy. Multichannel EEGs contain more information than do single-channel EEGs. Automatic detection algorithms for spikes or seizures have traditionally been implemented on single-channel EEG, and algorithms for multichannel EEG are unavailable.

Methodology

This study proposes a physiology-based detection system for epileptic seizures that uses multichannel EEG signals. The proposed technique was tested on two EEG data sets acquired from 18 patients. Both unipolar and bipolar EEG signals were analyzed. We employed sample entropy (SampEn), statistical values, and concepts used in clinical neurophysiology (e.g., phase reversals and potential fields of a bipolar EEG) to extract the features. We further tested the performance of a genetic algorithm cascaded with a support vector machine and post-classification spike matching.

Principal Findings

We obtained 86.69% spike detection and 99.77% seizure detection for Data Set I. The detection system was further validated using the model trained by Data Set I on Data Set II. The system again showed high performance, with 91.18% detection of spikes and 99.22% seizure detection.

Conclusion

We report a de novo EEG classification system for seizure and spike detection on multichannel EEG that includes physiology-based knowledge to enhance the performance of this type of system.     

Resected Brain Tissue,Seizure Onset Zone and Quantitative EEG Measures: Towards Prediction of Post-Surgical Seizure Control

BackgroundEpilepsy surgery is a potentially curative treatment option for pharmacoresistent patients. If non-invasive methods alone do not allow to delineate the epileptogenic brain areas the surgical candidates undergo long-term monitoring with intracranial EEG. Visual EEG analysis is then used to identify the seizure onset zone for targeted resection as a standard procedure.MethodsDespite of its great potential to assess the epileptogenicty of brain tissue, quantitative EEG analysis has not yet found its way into routine clinical practice. To demonstrate that quantitative EEG may yield clinically highly relevant information we retrospectively investigated how post-operative seizure control is associated with four selected EEG measures evaluated in the resected brain tissue and the seizure onset zone. Importantly, the exact spatial location of the intracranial electrodes was determined by coregistration of pre-operative MRI and post-implantation CT and coregistration with post-resection MRI was used to delineate the extent of tissue resection. Using data-driven thresholding, quantitative EEG results were separated into normally contributing and salient channels.ResultsIn patients with favorable post-surgical seizure control a significantly larger fraction of salient channels in three of the four quantitative EEG measures was resected than in patients with unfavorable outcome in terms of seizure control (median over the whole peri-ictal recordings). The same statistics revealed no association with post-operative seizure control when EEG channels contributing to the seizure onset zone were studied.ConclusionsWe conclude that quantitative EEG measures provide clinically relevant and objective markers of target tissue, which may be used to optimize epilepsy surgery. The finding that differentiation between favorable and unfavorable outcome was better for the fraction of salient values in the resected brain tissue than in the seizure onset zone is consistent with growing evidence that spatially extended networks might be more relevant for seizure generation, evolution and termination than a single highly localized brain region (i.e. a “focus”) where seizures start.     

Scalp EEG for the diagnosis of epilepsy and photosensitivity in the baboon

Spontaneous seizures have been observed in several baboon species housed at the Southwest National Primate Research Center (SNPRC), including Papio hamadryas anubis and cynocephalus/anubis, hamadryas/anubis, and papio/anubis hybrids. The goal of this study was to establish a noninvasive, reliable electroencephalographic technique to characterize epilepsy phenotypes and assess photosensitivity in these subspecies. Thirty baboons with witnessed seizures, and 15 asymptomatic baboons underwent scalp electroencephalograms (EEGs) with photic stimulation (PS). The sensitivity and specificity of surface EEG for identifying interictal epileptic discharges (IEDs) in baboons with witnessed seizures were examined. The morphology of IEDs, electroclinical features of seizures and responses to PS, reproducibility of EEG findings, and intrarater reliability were also evaluated. Twenty-three seizure baboons (77%) demonstrated IEDs, predominantly with frequencies of 4-6 Hz in 18 baboons and 2-3 Hz in six baboons. Two seizure animals had a mixture of 2-3-Hz and 4-6-Hz IEDs. All animals with 2-3-Hz IEDs were 3 years old or younger. Myoclonic seizures (MS) and generalized tonic-clonic seizures (GTCS) were recorded in 13 baboons (43%). PS activated IEDs in 15 baboons (50%) and seizures in nine baboons. The presence of IEDs or seizures was not associated with a particular gender or species (Fisher exact test, alpha=0.05). Seizures were more common in animals >3 years old, while PS-induced IEDs and seizures were more prevalent in P.h. anubis/cynocephalus crosses compared to P.h. anubis. In the asymptomatic controls, IEDs were recorded in five baboons (33%), and photoparoxysmal responses were observed in two (13%). Surface EEG is a sensitive and reliable instrument for characterizing the epilepsy encountered in Papio species. Electroclinically, the seizure animals had generalized epilepsy with photosensitivity. The variation in IED morphology may be age-related or it may reflect different epileptic phenotypes. Ketamine provoked IEDs and seizures in most seizure animals and only in a few asymptomatic baboons; therefore, it may enhance the sensitivity of surface EEG for detecting a predisposition to epilepsy.     

Feature extraction and recognition of epileptiform activity in EEG by combining PCA with ApEn

This paper proposes a new method for feature extraction and recognition of epileptiform activity in EEG signals. The method improves feature extraction speed of epileptiform activity without reducing recognition rate. Firstly, Principal component analysis (PCA) is applied to the original EEG for dimension reduction and to the decorrelation of epileptic EEG and normal EEG. Then discrete wavelet transform (DWT) combined with approximate entropy (ApEn) is performed on epileptic EEG and normal EEG, respectively. At last, Neyman–Pearson criteria are applied to classify epileptic EEG and normal ones. The main procedure is that the principle component of EEG after PCA is decomposed into several sub-band signals using DWT, and ApEn algorithm is applied to the sub-band signals at different wavelet scales. Distinct difference is found between the ApEn values of epileptic and normal EEG. The method allows recognition of epileptiform activities and discriminates them from the normal EEG. The algorithm performs well at epileptiform activity recognition in the clinic EEG data and offers a flexible tool that is intended to be generalized to the simultaneous recognition of many waveforms in EEG.     

Incidence of seizures and EEG abnormalities among offspring of epileptic patients

Summary The marriage rate of epileptic patients was 62% in males und 78% in females. Compared with the rates in the general population, the male patients had a 15% lower rate, but there was no difference in females. There were 263 patients with at least one offspring selected for the study. There were 243 sons and 272 daughters (506 total, 1.9 per patient). Distribution by types of seizure was awakening grand mal, absence or myoclonic petit mal in 24%, grand mal with no aura in 21%, grand mal during sleep in 23%, diffuse grand mal in 7%, grand mal with aura in 13%, psychomotor seizure in 9%, and focal seizure in 3%. The probands were composed of 79% idiopathic and 21% symptomatic in pathogenetic classification. An epileptic EEG abnormality was demonstrated in 22% of male and 44% of female probands.The incidence of seizures among offspring was 2.4% (4.2% age-corrected) in a narrow sense (epilepsy) and 9.1% in a broad sense including febrile convulsions. The latter morbidity was 11.0% for the idiopathic and 3.2% for the symptomatic group; 11.0% for female and 6.9% for male probands; 10.2% for sons and 8.1% for daughters. The figure was higher for the probands with the age range at onset of seizure of 0–4 years (20.6%) and 20–29 years (12.6%) than for those with other age ranges; higher for those with awakening grand mal, absence, myoclonic petit mal, or grand mal with no aura than for those with other types of seizure; and higher for those with family history of epilepsy than those without it.Possible correlation of types of seizure between probands and offspring was demonstrated. Thirty-seven percent of offspring exhibited epileptic EEG abnormalities, and the ratio of epileptic EEG abnormalities to clinical manifestation is about 4:1.Possible existence of familial aggregation of EEG abnormalities and of two kinds of families with large or small epileptic predisposition was indicated.The importance of the role of hereditary and environmental factors in epileptic pathogenesis is proved, and the results of an investigation of congenital malformation among offspring of epileptic mothers are presented. These results were considered to be useful for genetic counseling of epileptic patients.     

Epilepsy and Nonlinear Dynamics

This overview summarizes findings obtained from analyzing electroencephalographic (EEG) recordings from epilepsy patients with methods from the theory of nonlinear dynamical systems. The last two decades have shown that nonlinear time series analysis techniques allow an improved characterization of epileptic brain states and help to gain deeper insights into the spatial and temporal dynamics of the epileptic process. Nonlinear EEG analyses can help to improve the evaluation of patients prior to neurosurgery, and with an unequivocal identification of precursors of seizures, they can be of great value in the development of seizure warning and prevention techniques.     

Epileptic seizures may begin hours in advance of clinical onset: a report of five patients

Mechanisms underlying seizure generation are traditionally thought to act over seconds to minutes before clinical seizure onset. We analyzed continuous 3- to 14-day intracranial EEG recordings from five patients with mesial temporal lobe epilepsy obtained during evaluation for epilepsy surgery. We found localized quantitative EEG changes identifying prolonged bursts of complex epileptiform discharges that became more prevalent 7 hr before seizures and highly localized subclinical seizure-like activity that became more frequent 2 hr prior to seizure onset. Accumulated energy increased in the 50 min before seizure onset, compared to baseline. These observations, from a small number of patients, suggest that epileptic seizures may begin as a cascade of electrophysiological events that evolve over hours and that quantitative measures of preseizure electrical activity could possibly be used to predict seizures far in advance of clinical onset.     

Behavioral management of epileptic seizures following EEG biofeedback training of the sensorimotor rhythm

Eight severely epileptic patients, four males and four females, ranging in age from 10 to 29 years, were trained to increase 12–14 Hz EEG activity from the regions overlying the Rolandic area. This activity, the sensorimotor rhythm(SMR), has been hypothesized to be related to motor inhibitory processes(Sterman, 1974). The patients represented a crosssection of several different types of epilepsy, including grand mal, myoclonic, akinetic, focal, and psychomotor types. Three of them had varying degrees of mental retardation. SMR was detected by a combination of an analog filtering system and digital processing. Feedback, both auditory and/or visual, was provided whenever one-half second of 12–14-Hz activity was detected in the EEG. Patients were provided with additional feedback keyed by the output of a 4–7-Hz filter which indicated the presence of epileptiform spike activity, slow waves, or movement. Feedback for SMR was inhibited whenever slow-wave activity spikes or movement was also present. During the treatment period most of the patients showed varying degrees of improvement. Two of the patients who had been severely epileptic, having multiple seizures per week, have been seizure free for periods of up to 1 month. Other patients have developed the ability to block many of their seizures. Seizure intensity and duration have also decreased. Furthermore, the successful patients demonstrated an increase in the amount of SMR and an increase in amplitude of SMR during the training period. Spectral analyses for the EEGs were performed periodically. The effectiveness of SMR conditioning for the control of epileptic seizures is evaluated in terms of patient characteristics and type of seizures.     

癫痫病人脑电信号的奇异谱

癫痫是一种常见的神经系统疾患,其唯一客观证据为脑电图的癫痫样发放。在癫痫发作间期,仅有偶发的很难辨别的癫痫样放电,为了正确诊断癫痫病,往往需要医生长时间监测病人的脑电信号,在对脑电信号进行相空间重构,进而对其进行奇异系统分析,发现癫痫病人无论在癫痫发作前、发作中、发作后,其脑电信号的奇异谱曲线不存在噪声平台,明显区别于正常人。是否可以认为脑电信号的奇异谱正代表着大脑的一种基本状态,癫痫患者在未发作时,大脑的基本状态已经处于异常。无论如休,奇异系统分析方法使得可以利用很短的一段脑电数据诊断癫痫。无疑为癫痫病人的临床诊断提供了一条简单、有效的途径。     

Epilepsy in kcnj10 Morphant Zebrafish Assessed with a Novel Method for Long-Term EEG Recordings

We aimed to develop and validate a reliable method for stable long-term recordings of EEG activity in zebrafish, which is less prone to artifacts than current invasive techniques. EEG activity was recorded with a blunt electrolyte-filled glass pipette placed on the zebrafish head mimicking surface EEG technology in man. In addition, paralysis of agarose-embedded fish using D-tubocurarine excluded movement artifacts associated with epileptic activity. This non-invasive recording technique allowed recordings for up to one hour and produced less artifacts than impaling the zebrafish optic tectum with a patch pipette. Paralyzed fish survived, and normal heartbeat could be monitored for over 1h. Our technique allowed the demonstration of specific epileptic activity in kcnj10a morphant fish (a model for EAST syndrome) closely resembling epileptic activity induced by pentylenetetrazol. This new method documented that seizures in the zebrafish EAST model were ameliorated by pentobarbitone, but not diazepam, validating its usefulness. In conclusion, non-invasive recordings in paralyzed EAST syndrome zebrafish proved stable, reliable and robust, showing qualitatively similar frequency spectra to those obtained from pentylenetetrazol-treated fish. This technique may prove particularly useful in zebrafish epilepsy models that show infrequent or conditional seizure activity.     

Complicated relationship between autism with regression and epilepsy

We retrospectively evaluated a set of 205 children with autism and compared it to the partial sub-set of 71 (34.6%) children with a history of regression. From 71 children with regression, signs of epileptic processes were present in 43 (60.6%), 28 (65.12%) suffered clinical epileptic seizures, and 15 (34.9%) just had an epileptiform abnormality on the EEG. In our analysis, autistic regression is substantially more associated with epileptic process symptoms than in children with autism and no history of regression. More than 90% of children with a history of regression also show IQ < 70 and reduced functionality. Functionality and IQ further worsens with the occurrence of epileptic seizures (98% of children with regression and epilepsy have IQ < 70). We proved that low IQ and reduced functionality significantly correlate rather with epileptic seizures than just sub-clinical epileptiform abnormality on EEG. Clinical epileptic seizures associated with regression significantly influence the age of regression and its clinical type. The age of regression is higher compared to children with regression without epileptic seizures (in median: 35 months of age in patients with seizures while only 24 months in other patients). Patients with seizures revealed regression after 24th months of age in 68% of cases, while patients without seizures only in 27%. However, coincidence with epilepsy also increased the occurrence of regression before the 18th month of age (23% of patients), while only 4% of patients without epilepsy revealed regression before the 18th month. Epileptic seizures are significantly associated especially with behaviour regression rather than speech regression or regression in both behaviour and speech. Also epileptic seizures diagnosed before correct diagnosis of autism were significantly associated with delayed regression (both behavioural and speech regression).     

Early Postnatal EEG Features of Perinatal Arterial Ischaemic Stroke with Seizures

Background

Stroke is the second most common cause of seizures in term neonates and is associated with abnormal long-term neurodevelopmental outcome in some cases.

Objective

To aid diagnosis earlier in the postnatal period, our aim was to describe the characteristic EEG patterns in term neonates with perinatal arterial ischaemic stroke (PAIS) seizures.

Design

Retrospective observational study.

Patients

Neonates >37 weeks born between 2003 and 2011 in two hospitals.

Method

Continuous multichannel video-EEG was used to analyze the background patterns and characteristics of seizures. Each EEG was assessed for continuity, symmetry, characteristic features and sleep cycling; morphology of electrographic seizures was also examined. Each seizure was categorized as electrographic-only or electroclinical; the percentage of seizure events for each seizure type was also summarized.

Results

Nine neonates with PAIS seizures and EEG monitoring were identified. While EEG continuity was present in all cases, the background pattern showed suppression over the infarcted side; this was quite marked (>50% amplitude reduction) when the lesion was large. Characteristic unilateral bursts of theta activity with sharp or spike waves intermixed were seen in all cases. Sleep cycling was generally present but was more disturbed over the infarcted side. Seizures demonstrated a characteristic pattern; focal sharp waves/spike-polyspikes were seen at frequency of 1–2 Hz and phase reversal over the central region was common. Electrographic-only seizure events were more frequent compared to electroclinical seizure events (78 vs 22%).

Conclusions

Focal electrographic and electroclinical seizures with ipsilateral suppression of the background activity and focal sharp waves are strong indicators of PAIS. Approximately 80% of seizure events were the result of clinically unsuspected seizures in neonates with PAIS. Prolonged and continuous multichannel video-EEG monitoring is advocated for adequate seizure surveillance.     

Widespread EEG Changes Precede Focal Seizures

The process by which the brain transitions into an epileptic seizure is unknown. In this study, we investigated whether the transition to seizure is associated with changes in brain dynamics detectable in the wideband EEG, and whether differences exist across underlying pathologies. Depth electrode ictal EEG recordings from 40 consecutive patients with pharmacoresistant lesional focal epilepsy were low-pass filtered at 500 Hz and sampled at 2,000 Hz. Predefined EEG sections were selected immediately before (immediate preictal), and 30 seconds before the earliest EEG sign suggestive of seizure activity (baseline). Spectral analysis, visual inspection and discrete wavelet transform were used to detect standard (delta, theta, alpha, beta and gamma) and high-frequency bands (ripples and fast ripples). At the group level, each EEG frequency band activity increased significantly from baseline to the immediate preictal section, mostly in a progressive manner and independently of any modification in the state of vigilance. Preictal increases in each frequency band activity were widespread, being observed in the seizure-onset zone and lesional tissue, as well as in remote regions. These changes occurred in all the investigated pathologies (mesial temporal atrophy/sclerosis, local/regional cortical atrophy, and malformations of cortical development), but were more pronounced in mesial temporal atrophy/sclerosis. Our findings indicate that a brain state change with distinctive features, in the form of unidirectional changes across the entire EEG bandwidth, occurs immediately prior to seizure onset. We postulate that these changes might reflect a facilitating state of the brain which enables a susceptible region to generate seizures.     

Automated detection of schizophrenia using optimal wavelet-based [... formula ...] norm features extracted from single-channel EEG

Schizophrenia (SZ) is a mental disorder, which affects the ability of human thinking, memory, and way of living. Manual screening of SZ patients is tedious, laborious and prone to human errors. Hence, we developed a computer-aided diagnosis (CAD) system to diagnose SZ patients accurately using single-channel electroencephalogram (EEG) signals. The EEG signals are nonlinear and non-stationary. Hence, we have used wavelet-based features to capture the hidden non-stationary nature present in the signal. First, the EEG signals are subjected to the the wavelet decomposition through six iterations, which yields seven sub-bands. The l1 norm is computed for each sub-band. The extracted norm features are disseminated to various classification algorithms. We have obtained the highest accuracy of 99.21% and 97.2% using K-nearest neighbor classifiers with ten-fold and leave-one-subject-out cross-validations. The developed single-channel EEG wavelet-based CAD model can help the clinicians to confirm the outcome of their manual screening and obtain an accurate diagnosis.     

A double-blind investigation of the relationship between seizure activity and the sleep EEG following EEG biofeedback training

The sleep EEGs of eight medically refractory epileptic patients were examined as part of a double-blind, ABA crossover study designed to determine the effectiveness of EEG biofeedback for the control of seizures. The patients were initially reinforced for one of three EEG criteria recorded from electrodes placed over sensorimotor cortex: (a) suppression of 3- to 7-Hz activity, (b) enhancement of 12- to 15-Hz activity, or (c) simultaneous suppression of 3- to 7-Hz and enhancement of 11- to 19-Hz activity. Reinforcement contingencies were reversed during the second or B phase, and then reinstated in their original form during the final A′ phase. All-night polysomnographic recordings were obtained at the end of each conditioning phase and were subjected to both visual and computer-based power spectral analyses. Four of the patients showed changes in their nocturnal paroxysmal activity that were either partially or totally consistent with the ABA′ contingencies of the study. The spectral data proved difficult to interpret, though two trends emerged from the analyses. Decreases in nocturnal 4- to 7-Hz activity were correlated with decreases in seizure activity, and increases in 8- to 11-Hz activity were correlated with decreases in seizure activity. These findings were shown to strengthen the hypothesis that EEG biofeedback may produce changes in the sleep EEG that are related to seizure incidence.