Modularity-Based Detection of Ripples in Scalp EEG

Ripples (80–250Hz) are promising markers of epileptogenic activity, but the diagnostic value of ripples in scalp EEG remains debated. In this study, we propose an unsupervised, cluster-based method to detect candidate ripples in scalp EEG and sort ripples according to their morphology and information content in the time-frequency domain. We also correlate the presence of ripples to the presence of interictal spikes, which are clinically recognized markers of epileptogenic activity. Our method combines feature-based agglomerative clustering and correlation-based community detection and was tested on scalp EEG from 3 children with epilepsy (age: 10±1 [mean ± SD], 2 male, 1 female). For each patient, one epoch of EEG during wakefulness and one epoch during sleep (stage N2–N3) were considered (wakefulness: 12.57±3.39 min; sleep: 14.68±0.49 min, mean ± SD). The proposed method showed high specificity in detecting ripples while rejecting artifacts and resulted in a minimal set of ripple templates that are consistent across patients and sleep condition. Also, the rate of ripples was higher in EEG channels that presented spikes (0.38±0.07 versus 0.24±0.07 ripples/min [mean ± SD]). Altogether, results indicate that morphology and spectral content of scalp ripples may be patient-independent and specific to the epileptogenic activity, which suggest scalp ripples as viable markers for noninvasive epilepsy diagnosis.