ArticleBrain sciences2020
Double-Step Machine Learning Based Procedure for HFOs Detection and Classification.
Article in Brain sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Decoding Intracranial EEG With Machine Learning: A Systematic Review.Frontiers in human neuroscience · 2022Pooled it
- AI-based localization of the epileptogenic zone using intracranial EEG.Epilepsia open · 2025Review
- Deep learning in intracranial EEG for seizure detection: advances, challenges, and clinical applications.Frontiers in neuroscience · 2025Review
- A Review of EEG-based Localization of Epileptic Seizure Foci: Common Points with Multimodal Fusion of Brain Data.Journal of medical signals and sensors · 2024Review
- Scalp high-frequency oscillations differentiate neonates with seizures from healthy neonates.Epilepsia open · 2023Article
- Epileptic seizure focus detection from interictal electroencephalogram: a survey.Cognitive neurodynamics · 2023Review
- An approach for reliably identifying high-frequency oscillations and reducing false-positive detections.Epilepsia open · 2022Article
- A consensus statement on detection of hippocampal sharp wave ripples and differentiation from other fast oscillations.Nature communications · 2022Review
- Epileptogenic high-frequency oscillations present larger amplitude both in mesial temporal and neocortical regions.Frontiers in human neuroscience · 2022Article
- Application of a convolutional neural network for fully-automated detection of spike ripples in the scalp electroencephalogram.Journal of neuroscience methods · 2021Article
- Automatic Detection of High-Frequency Oscillations Based on an End-to-End Bi-Branch Neural Network and Clinical Cross-Validation.Computational intelligence and neuroscience · 2021Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The need for automatic detection and classification of high-frequency oscillations (HFOs) as biomarkers of the epileptogenic tissue is strongly felt in the clinical field. In this context, the employment of artificial intelligence methods could be the missing piece to achieve this goal. This work proposed a double-step procedure based on machine learning algorithms and tested it on an intracranial electroencephalogram (iEEG) dataset available online. The first step aimed to define the optimal length for signal segmentation, allowing for an optimal discrimination of segments with HFO relative to those without. In this case, binary classifiers have been tested on a set of energy features. The second step aimed to classify these segments into ripples, fast ripples and fast ripples occurring during ripples. Results suggest that LDA applied to 10 ms segmentation could provide the highest sensitivity (0.874) and 0.776 specificity for the discrimination of HFOs from no-HFO segments. Regarding the three-class classification, non-linear methods provided the highest values (around 90%) in terms of specificity and sensitivity, significantly different to the other three employed algorithms. Therefore, this machine-learning-based procedure could help clinicians to automatically reduce the quantity of irrelevant data.
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