ArticleFrontiers in systems neuroscience2018
A Comparison of Machine Learning Classifiers for Energy-Efficient Implementation of Seizure Detection.
Article in Frontiers in systems neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 56 citations in OpenAlex.
- Decoding Intracranial EEG With Machine Learning: A Systematic Review.Frontiers in human neuroscience · 2022Pooled it
- An edge-AI enabled wearable platform for real-time epileptic seizure detection with geolocated alerting.BMC biomedical engineering · 2026Article
- Multiscale predictive modeling robustly improves the accuracy of pseudo-prospective seizure forecasting in drug-resistant epilepsy.bioRxiv : the preprint server for biology · 2025Article
- Assistive Artificial Intelligence in Epilepsy and Its Impact on Epilepsy Care in Low- and Middle-Income Countries.Brain sciences · 2025Review
- Artificial intelligence in epilepsy - applications and pathways to the clinic.Nature reviews. Neurology · 2024Review
- Landscape and future directions of machine learning applications in closed-loop brain stimulation.NPJ digital medicine · 2023Review
- Seizure count forecasting to aid diagnostic testing in epilepsy.Epilepsia · 2022Article
- Evaluation of Feature Selection Methods for Classification of Epileptic Seizure EEG Signals.Sensors (Basel, Switzerland) · 2022Article
- Time-Series Generative Adversarial Network Approach of Deep Learning Improves Seizure Detection From the Human Thalamic SEEG.Frontiers in neurology · 2022Article
- Prediction of Seizure Recurrence. A Note of Caution.Frontiers in neurology · 2021Article
- A Comparison of Energy-Efficient Seizure Detectors for Implantable Neurostimulation Devices.Frontiers in neurology · 2021Article
- Identification of early liver toxicity gene biomarkers using comparative supervised machine learning.Scientific reports · 2020Article
- A review of epileptic seizure detection using machine learning classifiers.Brain informatics · 2020Review
- Visual seizure annotation and automated seizure detection using behind-the-ear electroencephalographic channels.Epilepsia · 2020Article
- A quadratic linear-parabolic model-based EEG classification to detect epileptic seizures.Journal of biomedical research · 2019Article
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Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The closed-loop application of electrical stimulation via chronically implanted electrodes is a novel approach to stop seizures in patients with focal-onset epilepsy. To this end, an energy efficient seizure detector that can be implemented in an implantable device is of crucial importance. In this study, we first evaluated the performance of two machine learning algorithms (Random Forest classifier and support vector machine (SVM)) by using selected time and frequency domain features with a limited need of computational resources. Performance of the algorithms was further compared to a detection strategy implemented in an existing closed loop neurostimulation device for the treatment of epilepsy. The results show a superior performance of the Random Forest classifier compared to the SVM classifier and the reference approach. Next, we implemented the feature extraction and classification process of the Random Forest classifier on a microcontroller to evaluate the energy efficiency of this seizure detector. In conclusion, the feature set in combination with Random Forest classifier is an energy efficient hardware implementation that shows an improvement of detection sensitivity and specificity compared to the presently available closed-loop intervention in epilepsy while preserving a low detection delay.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.