Pitfalls in EEG Analysis in Patients With Nonconvulsive Status Epilepticus: A Preliminary Study.

Objective: Electroencephalography (EEG) interpretations through visual (by human raters) and automated (by computer technology) analysis were still not reliable for the diagnosis of nonconvulsive status epilepticus (NCSE). This study aimed to identify typical pitfalls in the EEG analysis and make suggestions as to how those pitfalls might be avoided. Methods: We analyzed the EEG recordings of individuals who had clinically confirmed or suspected NCSE. Epileptiform EEG activity during seizures (ictal discharges) was visually analyzed by 2 independent raters. We investigated whether unreliable EEG visual interpretations quantified by low interrater agreement can be predicted by the characteristics of ictal discharges and individuals' clinical data. In addition, the EEG recordings were automatically analyzed by in-house algorithms. To further explore the causes of unreliable EEG interpretations, 2 epileptologists analyzed EEG patterns most likely misinterpreted as ictal discharges based on the differences between the EEG interpretations through the visual and automated analysis. Results: Short ictal discharges with a gradual onset (developing over 3 s in length) were liable to be misinterpreted. An extra 2 min of ictal discharges contributed to an increase in the kappa statistics of >0.1. Other problems were the misinterpretation of abnormal background activity (slow-wave activities, other abnormal brain activity, and the ictal-like movement artifacts), continuous interictal discharges, and continuous short ictal discharges. Conclusion: A longer duration criterion for NCSE-EEGs than 10 s that is commonly used in NCSE working criteria is recommended. Using knowledge of historical EEGs, individualized algorithms, and context-dependent alarm thresholds may also avoid the pitfalls.

False alarms reduction in non-convulsive status epilepticus detection via continuous EEG analysis.

OBJECTIVE: Frequent false alarms from computer-assisted monitoring systems may harm the safety of patients with non-convulsive status epilepticus (NCSE). In this study, we aimed at reducing false alarms in the NCSE detection based on preventing from three common errors: over-interpretation of abnormal background activity, dense short ictal discharges and continuous interictal discharges as ictal discharges. APPROACH: We analyzed 10 participants' hospital-archived 127-hour electroencephalography (EEG) recordings with 310 ictal discharges. To reduce the false alarms caused by abnormal background activity, we used morphological features extracted by visibility graph methods in addition to time-frequency features. To reduce the false alarms caused by over-interpreting short ictal discharges and interictal discharges, we created two synthetic classes-'Suspected Non-ictal' and 'Suspected Ictal'-based on the misclassified categories and constructed a synthetic 4-class dataset combining the standard two classes-'Non-ictal' and 'Ictal'-to train a 4-class classifier. Precision-recall curves were used to compare our proposed 4-class classification model and the standard 2-class classification model with or without the morphological features in the leave-one-out cross validation stage. The sensitivity and precision were primarily used as performance metrics for the detection of a seizure event. MAIN RESULTS: The 4-class classification model improved the performance of the standard 2-class model, in particular increasing the precision by 15% at an 80% sensitivity level when only time-frequency features were used. Using the morphological features, the 4-class classification model achieved the best performances: a sensitivity of 93% ± 12% and a precision of 55% ± 30% in the group level. 100% accuracy was reached in a participant's 4.3-hour recording with 5 ictal discharges. SIGNIFICANCE: False alarms in the NCSE detection were remarkably reduced using the morphological features and the proposed 4-class classification model.

The yield of long-term electrocardiographic recordings in refractory focal epilepsy.

OBJECTIVE: To determine the incidence of clinically relevant arrhythmias in refractory focal epilepsy and to assess the potential of postictal arrhythmias as risk markers for sudden unexpected death in epilepsy (SUDEP). METHODS: We recruited people with refractory focal epilepsy without signs of ictal asystole and who had at least one focal seizure per month and implanted a loop recorder with 2-year follow-up. The devices automatically record arrhythmias. Subjects and caregivers were instructed to make additional peri-ictal recordings. Clinically relevant arrhythmias were defined as asystole ≥ 6 seconds; atrial fibrillation < 55 beats per minute (bpm), or > 200 bpm and duration > 30 seconds; persistent sinus bradycardia < 40 bpm while awake; and second- or third-degree atrioventricular block and ventricular tachycardia/fibrillation. We performed 12-lead electrocardiography (ECG) and tilt table testing to identify non-seizure-related causes of asystole. RESULTS: We included 49 people and accumulated 1060 months of monitoring. A total of 16 474 seizures were reported, of which 4679 were captured on ECG. No clinically relevant arrhythmias were identified. Three people had a total of 18 short-lasting (<6 seconds) periods of asystole, resulting in an incidence of 2.91 events per 1000 patient-months. None of these coincided with a reported seizure; one was explained by micturition syncope. Other non-clinically relevant arrhythmias included paroxysmal atrial fibrillation (n = 2), supraventricular tachycardia (n = 1), and sinus tachycardia with a right bundle branch block configuration (n = 1). SIGNIFICANCE: We found no clinically relevant arrhythmias in people with refractory focal epilepsy during long-term follow-up. The absence of postictal arrhythmias does not support the use of loop recorders in people at high SUDEP risk.

Movement-based seizure detection.

This is a critical review and comment on the use of movement detection in epileptic seizures. The detection of rhythmic movement components, such as the clonic part of tonic-clonic seizures, is essential in all seizure detection based on movement sensors. Of the many available movement sensor types, accelerometric sensors are used most often. Eleven video-electroencephalographic (EEG) and 1 field study have been carried out. The results of these clinical trials depend on the population, study design, and seizure evolution. In video-EEG monitoring units, sensitivity for tonic-clonic seizures varied from 31% to 95%, and positive predictive value from 4% to 60%. In a field trial in a residential adult population with intellectual disability, sensitivity was 14% and positive predictive value was 82%, whereas in patients admitted to an epilepsy clinic, a bed sensor had a sensitivity of 84% (no positive predictive value was given). The algorithms using the "rhythmic movement" component at the end of a tonic-clonic seizure are reliable (few false-positive alarms) but miss less typical seizure patterns that are mostly present in people with associated brain development disturbances. Other modalities (heart rate and electromyography) are needed to increase the detection performance. Advanced accelerometric techniques allow us to gain greater insight into seizure evolution patterns, possibilities for neuromodulation, and the influence of antiepileptic drugs on specific seizure components.

Automated video-based detection of nocturnal convulsive seizures in a residential care setting.

People with epilepsy need assistance and are at risk of sudden death when having convulsive seizures (CS). Automated real-time seizure detection systems can help alert caregivers, but wearable sensors are not always tolerated. We determined algorithm settings and investigated detection performance of a video algorithm to detect CS in a residential care setting. The algorithm calculates power in the 2-6 Hz range relative to 0.5-12.5 Hz range in group velocity signals derived from video-sequence optical flow. A detection threshold was found using a training set consisting of video-electroencephalogaphy (EEG) recordings of 72 CS. A test set consisting of 24 full nights of 12 new subjects in residential care and additional recordings of 50 CS selected randomly was used to estimate performance. All data were analyzed retrospectively. The start and end of CS (generalized clonic and tonic-clonic seizures) and other seizures considered desirable to detect (long generalized tonic, hyperkinetic, and other major seizures) were annotated. The detection threshold was set to the value that obtained 97% sensitivity in the training set. Sensitivity, latency, and false detection rate (FDR) per night were calculated in the test set. A seizure was detected when the algorithm output exceeded the threshold continuously for 2 seconds. With the detection threshold determined in the training set, all CS were detected in the test set (100% sensitivity). Latency was ≤10 seconds in 78% of detections. Three/five hyperkinetic and 6/9 other major seizures were detected. Median FDR was 0.78 per night and no false detections occurred in 9/24 nights. Our algorithm could improve safety unobtrusively by automated real-time detection of CS in video registrations, with an acceptable latency and FDR. The algorithm can also detect some other motor seizures requiring assistance.

EEG analysis of seizure patterns using visibility graphs for detection of generalized seizures.

BACKGROUND: The traditional EEG features in the time and frequency domain show limited seizure detection performance in the epileptic population with intellectual disability (ID). In addition, the influence of EEG seizure patterns on detection performance was less studied. NEW METHOD: A single-channel EEG signal can be mapped into visibility graphs (VGS), including basic visibility graph (VG), horizontal VG (HVG), and difference VG (DVG). These graphs were used to characterize different EEG seizure patterns. To demonstrate its effectiveness in identifying EEG seizure patterns and detecting generalized seizures, EEG recordings of 615h on one EEG channel from 29 epileptic patients with ID were analyzed. RESULTS: A novel feature set with discriminative power for seizure detection was obtained by using the VGS method. The degree distributions (DDs) of DVG can clearly distinguish EEG of each seizure pattern. The degree entropy and power-law degree power in DVG were proposed here for the first time, and they show significant difference between seizure and non-seizure EEG. The connecting structure measured by HVG can better distinguish seizure EEG from background than those by VG and DVG. COMPARISON WITH EXISTING METHOD: A traditional EEG feature set based on frequency analysis was used here as a benchmark feature set. With a support vector machine (SVM) classifier, the seizure detection performance of the benchmark feature set (sensitivity of 24%, FDt/h of 1.8s) can be improved by combining our proposed VGS features extracted from one EEG channel (sensitivity of 38%, FDt/h of 1.4s). CONCLUSIONS: The proposed VGS-based features can help improve seizure detection for ID patients.

Value of re-interpretation of controversial EEGs in a tertiary epilepsy clinic.

OBJECTIVE: Diagnostic value and efficacy of re-interpretation of previous EEGs in 100 patients admitted to a tertiary epilepsy center with EEG results conflicting with the clinical diagnosis after the first visit. METHODS: EEGs were reclassified. A matched control group was included to assess the efficiency of the re-interpretation process. Efficacy was assessed by questionnaires and costs as number of technician hours needed. RESULTS: In 85 patients the previous EEG conclusion was known. In 43 the conclusion was altered. In 23 the epileptic activity changed from positive to negative (17) or the reverse (6). In 15 the focus changed (7 originally classified as generalized epileptic activity). In 5 the syndrome changed. 57% of the re-interpretation group needed no extra EEG afterwards. 96% of the re-interpretations were considered useful by requesting and 72% by not involved neurologists. The average time per EEG technologist per patient was 8,81h in controls and 5,40 in the re-interpretation group. CONCLUSIONS: In 43 from the 85 patients (51%) re-interpretation of 'controversial' EEGs led to a different opinion. The re-interpretations were useful and less time consuming, compared to new EEGs in controls. SIGNIFICANCE: Re-interpretation of 'controversial' EEGs is useful and cost effective.

Diagnostic accuracy of audio-based seizure detection in patients with severe epilepsy and an intellectual disability.

UNLABELLED: We evaluated the performance of audio-based detection of major seizures (tonic-clonic and long generalized tonic) in adult patients with intellectual disability living in an institute for residential care. METHODS: First, we checked in a random sample (n=17, 102 major seizures) how many patients have recognizable sounds during these seizures. In the second part of this trial, we followed 10 patients (who had major seizures with recognizable sounds) during four weeks with an acoustic monitoring system developed by CLB ('CLB-monitor') and video camera. In week 1, we adapted the sound detection threshold until, per night, a maximum of 20 sounds was found. During weeks 2-4, we selected the epilepsy-related sounds and performed independent video verification and labeling ('snoring', 'laryngeal contraction') of the seizures. The video images were also fully screened for false negatives. In the third part, algorithms in the CLB-monitor detected one specific sound (sleep-related snoring) to illustrate the value of automatic sound recognition. RESULTS: Part 1: recognizable sounds (louder than whispering) occurred in 23 (51%) of the 45 major seizures, 20 seizures (45%) were below this threshold, and 2 (4%) were without any sound. Part 2: in the follow-up group (n=10, 112 major seizures; mean: 11.2, range: 1-30), we found a mean sensitivity of 0.81 (range: 0.33-1.00) and a mean positive predictive value of 0.40 (range: 0.06-1.00). All false positive alarms (mean value: 1.29 per night) were due to minor seizures. We missed 4 seizures (3%) because of lack of sound and 10 (9%) because of sounds below the system threshold. Part 3: the machine-learning algorithms in the CLB-monitor resulted in an overall accuracy for 'snoring' of 98.3%. CONCLUSIONS: Audio detection of major seizures is possible in half of the patients. Lower sound detection thresholds may increase the proportion of suitable candidates. Human selection of seizure-related sounds has a high sensitivity and moderate positive predictive value because of minor seizures which do not need intervention. Algorithms in the CLB-monitor detect seizure-related sounds and may be used alone or in multimodal systems.

Seizure detection using dynamic warping for patients with intellectual disability.

Electroencephalography (EEG) is paramount for both retrospective analysis and real-time monitoring of epileptic seizures. Studies have shown that EEG-based seizure detection is very difficult for a specific epileptic population with intellectual disability due to the cerebral development disorders. In this work, a seizure detection method based on dynamic warping (DW) is proposed for patients with intellectual disability. It uses an EEG template of an individual subject's dominant seizure type, to extract the morphological features from EEG signals. A linear discriminant analysis (LDA) classifier is used to perform the seizure detection. Results show that the DW-based feature in the frequency domain is superior than that in the time domain, and the features extracted using wavelet transform method.

Epileptic seizure detection on patients with mental retardation based on EEG features: A pilot study.

Mental retardation (MR) is one of the most common secondary disabilities in people with Epilepsy. However, to our knowledge there are no reliable seizure detection methods specified for MR-patients. In this paper we performed a pilot study on a group of six patients with mental retardation to assess what EEG features potentially work well on this group. A group of EEG features on the time, frequency and spatio-temporal domain were extracted, the modified wrapper approach was then employed as an improved feature subset selection method. Results show high variance on obtained features subset across this group, meanwhile there exist some common features which characterize the high-frequency components of epileptic EEG signals.

Accelerometer-based quantitative analysis of axial nocturnal movements differentiates patients with Parkinson's disease, but not high-risk individuals, from controls.

INTRODUCTION: There is a need for prodromal markers to diagnose Parkinson's disease (PD) as early as possible. Knowing that most patients with overt PD have abnormal nocturnal movement patterns, we hypothesised that such changes might occur already in non-PD individuals with a potentially high risk for future development of the disease. METHODS: Eleven patients with early PD (Hoehn & Yahr stage ≤2.5), 13 healthy controls and 33 subjects with a high risk of developing PD (HR-PD) were investigated. HR-PD was defined by the occurrence of hyperechogenicity of the substantia nigra in combination with prodromal markers (eg, slight motor signs, olfactory dysfunction). A triaxial accelerometer was used to quantify nocturnal movements during two nights per study participant. Outcome measurements included mean acceleration, and qualitative axial movement parameters, such as duration and speed. RESULTS: Mean acceleration of nocturnal movements was lower in patients with PD compared to controls. Frequency and speed of axial movements did not differ between patients with PD and controls, but mean size and duration were lower in PD. The HR-PD group did not significantly differ from the control group in any of the parameters analysed. CONCLUSIONS: Compared with controls, patients with PD had an overall decreased mean acceleration, as well as smaller and shorter nocturnal axial movements. These changes did not occur in our potential HR-PD individuals, suggesting that relevant axial movement alterations during sleep have either not developed or cannot be detected by the means applied in this at-risk cohort.

Can an early 24-hour EEG predict the response to the ketogenic diet? A prospective study in 34 children and adults with refractory epilepsy treated with the ketogenic diet.

PURPOSE: We examined whether early EEG changes in a 24-h EEG at 6 weeks of treatment were related to the later clinical response to the ketogenic diet (KD) in a 6-month period of treatment. METHODS: We examined 34 patients with heterogeneous epilepsy syndromes (21 children, 13 adults) and found 9 clinical responders (≥50% seizure reduction); this is a responder rate of 26%. We visually counted the interictal epileptic discharge index (IED index) in % during 2h of wakefulness and in the first hour of sleep (method 1), and also globally reviewed EEG changes (method 2), while blinded to the effect of the KD. RESULTS: At group level we saw a correlation between nocturnal reduction of IED-index at 6 weeks and seizure reduction in the follow-up period. A proportional reduction in IED index of 30% from baseline in the sleep EEG, was associated with being a responder to the diet (Pearson Chi-square p=0.04). EEG scoring method 2 observed a significantly larger proportion of patients with EEG-improvement in sleep in KD responders than in non-responders (p=0.03). At individual level, however, EEG changes did not correlate very strongly to the response to the diet, as IED reduction in sleep was also seen in 15% (method 1) to 26% (method 2) of the non-responders. CONCLUSION: Nocturnal reduction of IEDs is related to the response to the KD, however in daily clinical practice, an early EEG to predict seizure reduction should not be advised for individual patients.

Actigraphy as a diagnostic aid for REM sleep behavior disorder in Parkinson's disease.

BACKGROUND: Rapid eye movement (REM) sleep behavior disorder (RBD) is a common parasomnia in Parkinson's disease (PD) patients. The current International Classification of Sleep Disorders (ICSD-II) requires a clinical interview combined with video polysomnography (video-PSG) to diagnose. The latter is time consuming and expensive and not always feasible in clinical practice. Here we studied the use of actigraphy as a diagnostic tool for RBD in PD patients. METHODS: We studied 45 consecutive PD patients (66.7% men) with and without complaints of RBD. All patients underwent one night of video-PSG and eight consecutive nights of actigraphy. Based on previous studies, the main outcome measure was the total number of bouts classified as "wake", compared between patients with (PD + RBD) and without RBD (PD- RBD). RESULTS: 23 (51.1%) patients had RBD according to the ICSD-II criteria. The total number of wake bouts was significantly higher in RBD patients (PD + RBD 73.2 ± 40.2 vs. PD-RBD 48.4 ± 23.3, p = .016). A cut off of 95 wake bouts per night resulted in a specificity of 95.5%, a sensitivity of 20.1% and a positive predictive value of 85.7%. Seven patients were suspected of RBD based on the interview alone, but not confirmed on PSG; six of whom scored below 95 wake bouts per night on actigraphy. CONCLUSION: PD patients with RBD showed a significantly higher number of bouts scored as "wake" using actigraphy, compared to patients without RBD. In clinical practice, actigraphy has a high specificity, but low sensitivity in the diagnosis of RBD. The combination of actigraphy and previously reported RBD questionnaires may be a promising method to diagnose RBD in patients with PD.

Subjectively impaired bed mobility in Parkinson disease affects sleep efficiency.

BACKGROUND: Impaired bed mobility (IBM) may be an important reason for the high prevalence of sleep insomnia in Parkinson disease (PD). Here we assessed the influence of subjectively IBM on both subjective and objective sleep parameters in insomnia PD patients with (PD+IBM) and without (PD-IBM) concerns of IBM and controls with primary insomnia. METHODS: We included 44 PD patients with sleep initiation or maintenance concerns and 44 control subjects with primary insomnia. Sleep questionnaires, polysomnographic sleep parameters, activity data, and the number of body position changes were compared between PD patients and controls as well as within the PD group between PD+IBM vs PD-IBM subjects. RESULTS: There were 54.5% of PD subjects who reported having IBM. In the PD+IBM group, the number of body position changes was significantly lower than in PD-IBM (0.4/h [0.0-1.8] vs 1.4/h [0.0-4.6], P=.015). Sleep efficiency (SE) was lower in PD+IBM patients (63.5; 26.2-85.6) compared to PD-IBM patients (78.4; 54.8-92.6; P<.001). CONCLUSION: PD patients who report IBM have fewer sleep-related body position changes (i.e., nocturnal hypokinesia) than PD patients without such concerns. Furthermore, objective SE is significantly diminished in these patients.

Motor function in children with cryptogenic localization related epilepsy.

INTRODUCTION: In CLRE specific learning difficulties and motor problems may occur. The aim of this study is to examine whether CLRE or the accompanying specific learning difficulties are associated with the occurring problems in motor function. METHODS: Motor functioning in 140 children with CLRE and without epilepsy, as well as with and without specific learning difficulties is compared using Chi-square. RESULTS: In the CLRE group 35% score below the 5th percentile (poor motor function). No correlations with epilepsy variables or the occurrence of specific learning difficulties is found. DISCUSSION: A subgroup of about one-third of children with CLRE are at risk for poor motor function. Their development is best monitored using a multi-dimensional approach, including cognitive development and motor functioning.

An investigation of the phase locking index for measuring of interdependency of cortical source signals recorded in the EEG.

The phase locking index (PLI) was introduced to quantify in a statistical sense the phase synchronization of two signals. It has been commonly used to process biosignals. In this article, we investigate the PLI for measuring the interdependency of cortical source signals (CSSs) recorded in the Electroencephalogram (EEG). To this end, we consider simple analytical models for the mapping of simulated CSSs into the EEG. For these models, the PLI is investigated analytically and through numerical simulations. An evaluation is made of the sensitivity of the PLI to the amount of crosstalk between the sources through biological tissues of the head. It is found that the PLI is a useful interdependency measure for CSSs, especially when the amount of crosstalk is small. Another common interdependency measure is the coherence. A direct comparison of both measures has not been made in the literature so far. We assess the performance of the PLI and coherence for estimation and detection purposes based on, respectively, a normalized variance and a novel statistical measure termed contrast. Based on these performance measures, it is found that the PLI is similar or better than the CM in most cases. This result is also confirmed through analysis of EEGs recorded from epileptic patients.

Prognosis after temporal lobe epilepsy surgery: the value of combining predictors.

PURPOSE: Although several independent predictors of seizure freedom after temporal lobe epilepsy surgery have been identified, their combined predictive value is largely unknown. Using a large database of operated patients, we assessed the combined predictive value of previously reported predictors included in a single multivariable model. METHODS: The database comprised a cohort of 484 patients who underwent temporal lobe surgery for drug-resistant epilepsy. Good outcome was defined as Engel class 1, one year after surgery. Previously reported independent predictors were tested in this cohort. To be included in our final prediction model, predictors had to show a multivariable p-value of <0.20. RESULTS: The final multivariable model included predictors obtained from the patient's history (absence of tonic-clonic seizures, absence of status epilepticus), magnetic resonance imaging [MRI; ipsilateral mesial temporal sclerosis (MTS), space occupying lesion], video electroencephalography (EEG; absence of ictal dystonic posturing, concordance between MRI and ictal EEG), and fluorodeoxyglucose positron emission tomography (FDG-PET; unilateral temporal abnormalities), that were related to seizure freedom in our data. The model showed an expected receiver-operating characteristic curve (ROC) area of 0.63 [95% confidence interval (CI) 0.57-0.68] for new patient populations. Intracranial monitoring and surgery-related parameters (including histology) were not important predictors of seizure freedom. Among patients with a high probability of seizure freedom, 85% were seizure-free one year after surgery; however, among patients with a high risk of not becoming seizure-free, still 40% were seizure-free one year after surgery. CONCLUSION: We could only moderately predict seizure freedom after temporal lobe epilepsy surgery. It is particularly difficult to predict who will not become seizure-free after surgery.

Decision-making in temporal lobe epilepsy surgery: the contribution of basic non-invasive tests.

PURPOSE: We studied the extent to which the widely used diagnostic tests contribute to the decision whether or not to perform temporal lobe epilepsy (TLE) surgery in The Netherlands. METHODS: This nation-wide, retrospective study included 201 consecutive patients referred for TLE surgery screening. The individual and combined contribution of nine index tests to the consensus decision to perform surgery was investigated. The contribution of each test was quantified using multivariable logistic regression and ROC curves. RESULTS: Surgery was performed in 119 patients (59%). Patient history and routine EEG findings were hardly contributory to decision-making, whereas a convergence of MRI with long-term interictal and ictal EEG findings correctly identified the candidates considered eligible for surgery (25% of total). Videotaped seizure semiology contributed less to the results. The area under the ROC curve of the combination of basic tests was 0.75. Ineligibility was never accurately predicted with any test combination. CONCLUSIONS: In the Dutch presurgical work-up, when MRI and long-term EEG findings were concordant, a decision for TLE surgery could be reached without further ancillary tests. Videotaped seizure semiology contributed less than expected to the final clinical decision. In our study, basic test findings alone were insufficient to exclude patients from surgery.

Detection of subtle nocturnal motor activity from 3-D accelerometry recordings in epilepsy patients.

This paper presents a first step towards reliable detection of nocturnal epileptic seizures based on 3-D accelerometry (ACM) recordings. The main goal is to distinguish between data with and without subtle nocturnal motor activity, thus reducing the amount of data that needs further (more complex) analysis for seizure detection. From 15 ACM signals (measured on five positions on the body), two features are computed, the variance and the jerk. In the resulting 2-D feature space, a linear threshold function is used for classification. For training and testing, the algorithm ACM data along with video data is used from nocturnal registrations in seven mentally retarded patients with severe epilepsy. Per patient, the algorithm detected 100% of the periods of motor activity that are marked in video recordings and the ACM signals by experts. From all the detections, 43%-89% was correct (mean =65%). We were able to reduce the amount of data that need to be analyzed considerably. The results show that our approach can be used for detection of subtle nocturnal motor activity. Furthermore, our results indicate that our algorithm is robust for fluctuations across patients. Consequently, there is no need for training the algorithm for each new patient.

Separation of spatially localized cortical activities in the EEG: model-based analysis and optimization.

The mapping of brain sources into the scalp electroencephalogram (EEG) depends on volume conduction properties of the head and on an electrode montage involving a reference. In this article, the source mapping (SM) is formalized mathematically in the form of an observation function (OF) matrix. The OF-matrix is used to analyze and optimize the SM for a generation model for the desynchronized spontaneous EEG. The optimization leads to a novel reference that minimizes the impact in the EEG of the sources located distant from the electrodes. Thereby, this reference separates spatially localized cortical activities in the EEG. For this reason, it is called the localized reference (LR). The LR is compared with the Hjorth Laplacian reference (HR), which is commonly used for recordings of localized cortical activities. The comparison is made in terms of the relative power contribution of the sources into EEG channels. For the model, the LR is found to have up to 15-20% better performance than the HR, and thus the LR is considered a good alternative to the HR when a head model is available. The HR is, however, a fair approximation of the LR and thus is close to optimum for practical intents and purposes.