Accurate differentiation between physiological and pathological ripples recorded with scalp-EEG.

OBJECTIVE: To compare scalp-EEG recorded physiological ripples co-occurring with vertex waves to pathological ripples co-occurring with interictal epileptiform discharges (IEDs). METHODS: We marked ripples in sleep EEGs of children. We compared the start of ripples to vertex wave- or IED-start, and duration, frequency, and root mean square (RMS) amplitude of physiological and pathological ripples using multilevel modeling. Ripples were classified as physiological or pathological using linear discriminant analysis. RESULTS: We included 40 children with and without epilepsy. Ripples started (χ2(1) = 38.59, p < 0.001) later if they co-occurred with vertex waves (108.2 ms after vertex wave-start) than if they co-occurred with IEDs (4.3 ms after IED-start). Physiological ripples had longer durations (75.7 ms vs 53.0 ms), lower frequencies (98.3 Hz vs 130.6 Hz), and lower RMS amplitudes (0.9 µV vs 1.8 µV, all p < 0.001) than pathological ripples. Ripples could be classified as physiological or pathological with 98 % accuracy. Ripples recorded in children with idiopathic or symptomatic epilepsy seemed to form two subgroups of pathological ripples. CONCLUSIONS: Ripples co-occurring with vertex waves or IEDs have different characteristics and can be differentiated as physiological or pathological with high accuracy. SIGNIFICANCE: This is the first study that compares physiological and pathological ripples recorded with scalp EEG.

A skew-based method for identifying intracranial EEG channels with epileptic activity without detecting spikes, ripples, or fast ripples.

OBJECTIVE: To develop a method for identifying intracranial EEG (iEEG) channels with epileptic activity without the need to detect spikes, ripples, or fast ripples. METHODS: We compared the skew of the distribution of power values from five minutes non-rapid eye movement stage N3 sleep for the 5-80 Hz, 80-250 Hz (ripple), and 250-500 Hz (fast ripple) bands of epileptic (located in seizure-onset or irritative zone) and non-epileptic iEEG channels recorded in patients with drug-resistant focal epilepsy. We optimized settings in 120 bipolar channels from 10 patients, compared the results to 120 channels from another 10 patients, and applied the method to channels of 12 individual patients. RESULTS: The distribution of power values was more skewed in epileptic than in non-epileptic channels in all three frequency bands. The differences in skew were correlated with the presence of spikes, ripples, and fast ripples. When classifying epileptic and non-epileptic channels, the mean accuracy over 12 patients was 0.82 (sensitivity: 0.76, specificity: 0.91). CONCLUSIONS: The 'skew method' can distinguish epileptic from non-epileptic channels with good accuracy and, in particular, high specificity. SIGNIFICANCE: This is an easy-to-apply method that circumvents the need to visually mark or automatically detect interictal epileptic events.

Ripples in scalp EEGs of children: co-occurrence with sleep-specific transients and occurrence across sleep stages.

Study Objectives: A dialogue between hippocampal ripples (80-250 Hz) and neocortical sleep-specific transients is important for memory consolidation. Physiological neocortical ripples can be recognized in scalp EEGs of children. We investigated how often scalp-EEG recorded ripples co-occur with different types of sleep-specific transients, the distribution and spatial extent of ripples with and without co-occurring sleep-specific transients, and the occurrence of ripples across sleep stages. Methods: We marked ripples in daytime sleep-EEGs of 19 children and determined for each ripple if it co-occurred with a sleep-specific transient. We compared the distribution of ripples without co-occurring sleep-specific transients to the distribution of all ripples. We estimated the spatial extent of simultaneously occurring ripples by counting how many EEG regions they comprised. We compared ripple rate per sleep stage using Friedman's analysis of variance and Wilcoxon signed-rank test. Results: 74.4 % of ripples co-occurred with sleep-specific transients: 27.8 % with vertex waves, 14.7 % with hypnagogic hypersynchrony, 13.7 % with slow waves, 12.2 % with spindles, and 6.0 % with K-complexes. Ripples without co-occurring sleep-specific transients showed the same central dominance but a significantly less pronounced midline dominance than the overall distribution pattern. Spatial extent was larger when ripples co-occurred with sleep-specific transients. Ripple rates during nonrapid eye movement (N) sleep stages N1 and N2 were higher than during N3 (T = 22.00, p = 0.02 and T = 23.00, p = 0.01). Conclusions: Scalp-EEG recorded physiological ripples co-occur with various sleep-specific EEG-transients, especially with vertex waves. These ripples occur most frequently during light sleep.

Physiological Ripples (± 100 Hz) in Spike-Free Scalp EEGs of Children With and Without Epilepsy.

Pathological high frequency oscillations (HFOs, >80 Hz) are considered new biomarkers for epilepsy. They have mostly been recorded invasively, but pathological ripples (80-250 Hz) can also be found in scalp EEGs with frequent epileptiform spikes. Physiological HFOs also exist. They have been recorded invasively in hippocampus and neocortex. There are no reports of spontaneously occurring physiological HFOs recorded with scalp EEG. We aimed to study ripples in spike-free scalp EEGs. We included 23 children (6 with, 17 without epilepsy) who had an EEG without interictal epileptiform spikes recorded during sleep. We differentiated true ripples from spurious ripples such as filtering effects of sharp artifacts and high frequency components of muscle artifacts by viewing ripples simultaneously in bipolar and average montage and double-checking the unfiltered signal. We calculated mean frequency, duration and root mean square amplitude of the ripples, and studied their shape and distribution. We found ripples in EEGs of 20 out of 23 children (4 with, 16 without epilepsy). Ripples had a regular shape and occurred mostly on central and midline channels. Mean frequency was 102 Hz, mean duration 70 ms, mean root mean square amplitude 0.95 µV. Ripples occurring in normal EEGs of children without epilepsy were considered physiological; the similarity in appearance suggested that the ripples occurring in normal EEGs of children with epilepsy were also physiological. The finding that it is possible to study physiological neocortical ripples in scalp EEG paves the way for investigating their occurrence during brain development and their relation with cognitive functioning.

Differentiating epileptic from non-epileptic high frequency intracerebral EEG signals with measures of wavelet entropy.

OBJECTIVE: To assess whether there is a difference in the background activity in the ripple band (80-200Hz) between epileptic and non-epileptic channels, and to assess whether this difference is sufficient for their reliable separation. METHODS: We calculated mean and standard deviation of wavelet entropy in 303 non-epileptic and 334 epileptic channels from 50 patients with intracerebral depth electrodes and used these measures as predictors in a multivariable logistic regression model. We assessed sensitivity, positive predictive value (PPV) and negative predictive value (NPV) based on a probability threshold corresponding to 90% specificity. RESULTS: The probability of a channel being epileptic increased with higher mean (p=0.004) and particularly with higher standard deviation (p<0.0001). The performance of the model was however not sufficient for fully classifying the channels. With a threshold corresponding to 90% specificity, sensitivity was 37%, PPV was 80%, and NPV was 56%. CONCLUSIONS: A channel with a high standard deviation of entropy is likely to be epileptic; with a threshold corresponding to 90% specificity our model can reliably select a subset of epileptic channels. SIGNIFICANCE: Most studies have concentrated on brief ripple events. We showed that background activity in the ripple band also has some ability to discriminate epileptic channels.

Electrocorticographic language mapping with a listening task consisting of alternating speech and music phrases.

OBJECTIVE: Electrocorticographic (ECoG) mapping of high gamma activity induced by language tasks has been proposed as a more patient friendly alternative for electrocortical stimulation mapping (ESM), the gold standard in pre-surgical language mapping of epilepsy patients. However, ECoG mapping often reveals more language areas than considered critical with ESM. We investigated if critical language areas can be identified with a listening task consisting of speech and music phrases. METHODS: Nine patients with implanted subdural grid electrodes listened to an audio fragment in which music and speech alternated. We analysed ECoG power in the 65-95 Hz band and obtained task-related activity patterns in electrodes over language areas. We compared the spatial distribution of sites that discriminated between listening to speech and music to ESM results using sensitivity and specificity calculations. RESULTS: Our listening task of alternating speech and music phrases had a low sensitivity (0.32) but a high specificity (0.95). CONCLUSIONS: The high specificity indicates that this test does indeed point to areas that are critical to language processing. SIGNIFICANCE: Our test cannot replace ESM, but this short and simple task can give a reliable indication where to find critical language areas, better than ECoG mapping using language tasks alone.