ArticleSensors (Basel, Switzerland)2022
Wearable Epileptic Seizure Prediction System Based on Machine Learning Techniques Using ECG, PPG and EEG Signals.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 45 citations in OpenAlex.
- Recent Progress in In-Ear EEG Technology and Its Emerging Real-World Applications: A Review.Micromachines · 2026Review
- Federated learning-enabled privacy-preserving framework for seizure forecasting and affective state analysis using multi-modal EEG-ECG data.Scientific reports · 2026Article
- Machine-learning methods for epilepsy diagnosis and therapeutic prevention: advances, setbacks, and opportunities.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2026Review
- Enhancing seizure prediction using a DC-SA-EBiLSTM framework with self-attention mechanism.Frontiers in neuroscience · 2026Article
- Development and Validation of an IMU Sensor-Based Behaviour-Alert Detection Collar for Assistance Dogs: A Proof-of-Concept Study.Animals : an open access journal from MDPI · 2025Article
- Innovating pediatric epilepsy: transforming diagnosis and treatment with AI.World journal of pediatrics : WJP · 2025Article
- Epileptic seizures diagnosis and prognosis from EEG signals using heterogeneous graph neural network.PeerJ. Computer science · 2025Article
- Enhancing detection of epileptic seizures using transfer learning and EEG brain activity signals.Computational and structural biotechnology journal · 2025Article
- Neural network-based method for measuring the impacts of epileptic brain activities on cardiac cycles.Frontiers in neurology · 2025Article
- Heart Rate Variability as a Tool for Seizure Prediction: A Scoping Review.Journal of clinical medicine · 2024Article
- SmartWear body sensors for neurological and neurosurgical patients: A review of current and future technologies.World neurosurgery: X · 2024Review
- Machine Learning and Artificial Intelligence Applications to Epilepsy: a Review for the Practicing Epileptologist.Current neurology and neuroscience reports · 2023Review
- Automatic Detection of Abnormal EEG Signals Using WaveNet and LSTM.Sensors (Basel, Switzerland) · 2023Article
- Remote collection of electrophysiological data with brain wearables: opportunities and challenges.Bioelectronic medicine · 2023Review
- Photoplethysmography Driven Hypertension Identification: A Pilot Study.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Epileptic seizures have a great impact on the quality of life of people who suffer from them and further limit their independence. For this reason, a device that would be able to monitor patients' health status and warn them for a possible epileptic seizure would improve their quality of life. With this aim, this article proposes the first seizure predictive model based on Ear EEG, ECG and PPG signals obtained by means of a device that can be used in a static and outpatient setting. This device has been tested with epileptic people in a clinical environment. By processing these data and using supervised machine learning techniques, different predictive models capable of classifying the state of the epileptic person into normal, pre-seizure and seizure have been developed. Subsequently, a reduced model based on Boosted Trees has been validated, obtaining a prediction accuracy of 91.5% and a sensitivity of 85.4%. Thus, based on the accuracy of the predictive model obtained, it can potentially serve as a support tool to determine the status epilepticus and prevent a seizure, thereby improving the quality of life of these people.
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Registered trials
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.