Machine learning predicts epileptogenic activity from high-frequency oscillation rates

El aprendizaje automático predice la actividad epileptógena a partir de las tasas de oscilación de alta frecuencia

Analysis of Patient-3 (good result—NT). (A) Preoperative T1 MRI (left) and postoperative T1 MRI (right). (B) An example of HFO, defined as the co-occurrence of ripple and FR, highlighted in red in its respective filtered range. (C) The distribution of HFO rates across channels (events/minute). Channels with an HFO rate greater than the 95% threshold (black horizontal line) formed the HFO area, highlighted in green. Channels in the resected area are marked in red. The HFO area was included in the resection leading to a good result (TN). Credit: Frontiers in human neuroscience (2022). DOI: 10.3389/fnhum.2022.984306

In a groundbreaking study, researchers from HSE University, the RAN Institute of Linguistics, and the Pirogov Center measured and analyzed high-frequency oscillations (HFOs) in different regions of the brain. An automated detector predicted seizure outcomes based on HFO rates with an 85% accuracy rate and, by applying machine learning, made it possible to distinguish between epileptogenic and non-epileptogenic HFO.

The study findings are published in Frontiers in human neuroscience.

High frequency oscillations are short term. brain events that, when observed by electroencephalography (EEG), help to identify the regions of the brain that generate epileptic seizures. Retrospective studies confirm that resection of tissue in such regions can help stop seizures.

However, prospective studies, that is, those conducted to predict surgical outcomes, have reported mixed results. In some cases, resection of tissue in a region with a large amount of HFO detected, and therefore assumed to be epileptogenic, did not cause the seizures to cease.

According to the authors of this study, one of the reasons for the failure to predict surgical outcomes may have been the fact that the patients were observed during REM sleep or wakefulness. The authors further argue that HFO data from deep sleep (NREM) could significantly improve the prognostic value of HFO rates, but not much if NREM sleep periods were too short or too few. Another limitation of previous studies was the performance of the detectors used to measure HFO rates.

Researchers from HSE University, the RAN Linguistic Institute, and the Pirogov Center examined differences in HFO amplitude, duration, and frequency between healthy and epileptogenic brain tissue. They analyzed HFO rates in the mesial temporal and neocortical regions of patients during NREM sleep using an automated detector clinically validated in previous studies.

The study predicted seizure outcomes with 85% accuracy. It would have been impossible to achieve 100% accuracy, as the detector was unable to distinguish between epileptogenic and healthy HFO rates. This limitation was partly solved by applying machine learning.

The investigators found a marked difference in amplitude between the rates of epileptogenic and non-epileptogenic HFO in the neocortex (frontal, temporal, and parietal lobes). This difference was less pronounced in the mesial temporal regions, where HFO duration was a more important distinction. High-frequency epileptogenic oscillations are approximately the same in regions of the brain in terms of amplitude, frequency, shape and duration. However, healthy oscillations are very different, mainly in amplitude, in different regions.

“Our findings demonstrate that by looking at HFOs, we can detect epileptogenic areas. This result could be further improved in the future. Machine learning will make it possible to distinguish between epileptogenic and healthy oscillations based on their amplitude, frequency, and duration,” he said. Victor Karpychev, a research assistant at the HSE Center for Language and the Brain.

This study also indicates that the accuracy of using HFO to identify epileptogenic tissue may be higher if a reliable automatic detector is used during the patient’s NREM sleep phase.

More information:
Victor Karpychev et al, High-frequency epileptogenic oscillations have greater amplitude in both the mesial and neocortical temporal regions, Frontiers in human neuroscience (2022). DOI: 10.3389/fnhum.2022.984306

Provided by the Higher School of Economics of the National Research University

Citation: Machine Learning Predicts Epileptogenic Activity from High-Frequency Oscillation Rates (November 16, 2022) Accessed November 16, 2022 at -high-frequency-oscillation.html

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