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Background: Changes in heart rate (HR) may provide an extracerebral indicator of seizure onset. The purpose of this study is to assess the frequency and timing of presentation of ictal tachycardia in a larger series of children with epilepsy grouped in different age groups as well as the influence of seizure characteristics., Methods: We retrospectively reviewed 732 seizures of 195 patients aged 0 to 14 (median 6.91) years with epilepsy of any cause. Patients were grouped according to the age in groups (1) <1 year (n = 18); (2) 1-2 years (n = 26); (3) 2-6 years (n = 43); (4) 6-10 years (n = 44); and (5) 10-14 years (n = 64). HR was assessed visually during the seizures and compared with HR 1 min before seizure onset. The time from seizure onset to ictal tachycardia, defined as an increase in HR by at least 33 %, was described. Ictal tachycardia was considered early if occurring in the first 10 s., Results: Ictal tachycardia occurred in at least one seizure in 70.3 % of patients and in 51.1 % of seizures. It was more frequent and earlier in focal seizures and in seizures occurring in sleep. >30 % of patients had ictal tachycardia in all of their seizures, this being more frequent in the age groups 6-10 and 10-14 years., Conclusions: Children older than 6 years especially with focal seizures during sleep could be ideal candidates for warning devices or stimulation therapies triggered by tachycardia detection. Future studies should aim to assess the implications in relation to the risk of SUDEP., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Objective: We propose a novel automated method called the S-Transform Gaussian Mixture detection algorithm (SGM) to detect high-frequency oscillations (HFO) combining the strengths of different families of previously published detectors., Approach: This algorithm does not depend on parameter tuning on a subject (or database) basis, uses time-frequency characteristics, and relies on non-supervised classification to determine if the events standing out from the baseline activity are HFO or not. SGM consists of three steps: the first stage computes the signal baseline using the entropy of the autocorrelation; the second uses the S-Transform to obtain several time-frequency features (area, entropy, and time and frequency widths); and in the third stage Gaussian mixture models cluster time-frequency features to decide if events correspond to HFO-like activity. To validate the SGM algorithm we tested its performance in simulated and real environments., Main Results: We assessed the algorithm on a publicly available simulated stereoelectroencephalographic (SEEG) database with varying signal-to-noise ratios (SNR), obtaining very good results for medium and high SNR signals. We further tested the SGM algorithm on real signals from patients with focal epilepsy, in which HFO detection was performed visually by experts, yielding a high agreement between experts and SGM., Significance: The SGM algorithm displayed proper performance in simulated and real environments and therefore can be used for non-supervised detection of HFO. This non-supervised algorithm does not require previous labelling by experts or parameter adjustment depending on the subject or database considered. SGM is not a computationally intensive algorithm, making it suitable to detect and characterize HFO in long-term SEEG recordings.

High frequency oscillations (HFOs, 80-500[Formula: see text]Hz) serve as novel electroencephalography (EEG) markers of epileptic tissue. The differentiation of physiological and epileptic HFO is an important challenge and is complicated by the fact that both types are generated in mesiotemporal structures. This study aimed to identify oscillation features that serve to distinguish physiological ripples associated with sleep spindles and epileptic ripples. We studied 19 patients with chronic intracranial EEG(iEEG) with mesiotemporal implantation and simultaneous scalp EEG. Sleep spindles, ripples and spikes were visually marked during nonrapid eye movement sleep stage 2. Ripples co-occurring with spikes and in seizure onset zone (SOZ) channels but outside of spindles were considered epileptic. The SOZ is defined by the origin of clinical seizures in iEEG. Ripples co-occurring with spindles were considered as models for physiological ripples. A correlation analysis showed a significant ripple amplitude peak - spindle trough - coupling, thus proving their physiological linkage. Epileptic ripples showed significantly higher values in all amplitude features than spindle ripples. All amplitude features and peaks per sample length showed a predictive value for the classification between model physiological ripples and epileptic ripples but indicate that the specificity is not sufficient for a reliable discrimination of single ripple events. The presented results suggest that a secure identification of epileptic ripples may be available to help identify the epileptic focus in the future.