Demo: Multi-Modal Seizure Prediction System
Fuente:
arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866915021561266176 |
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| author | Saeizadeh, Ali del Prever, Pietro Brach Schonholtz, Douglas Guida, Raffaele Demirors, Emrecan Jimenez, Jorge M. Johari, Pedram Melodia, Tommaso |
| author_facet | Saeizadeh, Ali del Prever, Pietro Brach Schonholtz, Douglas Guida, Raffaele Demirors, Emrecan Jimenez, Jorge M. Johari, Pedram Melodia, Tommaso |
| contents | This demo presents SeizNet, an innovative system for predicting epileptic seizures benefiting from a multi-modal sensor network and utilizing Deep Learning (DL) techniques. Epilepsy affects approximately 65 million people worldwide, many of whom experience drug-resistant seizures. SeizNet aims at providing highly accurate alerts, allowing individuals to take preventive measures without being disturbed by false alarms. SeizNet uses a combination of data collected through either invasive (intracranial electroencephalogram (iEEG)) or non-invasive (electroencephalogram (EEG) and electrocardiogram (ECG)) sensors, and processed by advanced DL algorithms that are optimized for real-time inference at the edge, ensuring privacy and minimizing data transmission. SeizNet achieves > 97% accuracy in seizure prediction while keeping the size and energy restrictions of an implantable device. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_05817 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Demo: Multi-Modal Seizure Prediction System Saeizadeh, Ali del Prever, Pietro Brach Schonholtz, Douglas Guida, Raffaele Demirors, Emrecan Jimenez, Jorge M. Johari, Pedram Melodia, Tommaso Signal Processing Machine Learning This demo presents SeizNet, an innovative system for predicting epileptic seizures benefiting from a multi-modal sensor network and utilizing Deep Learning (DL) techniques. Epilepsy affects approximately 65 million people worldwide, many of whom experience drug-resistant seizures. SeizNet aims at providing highly accurate alerts, allowing individuals to take preventive measures without being disturbed by false alarms. SeizNet uses a combination of data collected through either invasive (intracranial electroencephalogram (iEEG)) or non-invasive (electroencephalogram (EEG) and electrocardiogram (ECG)) sensors, and processed by advanced DL algorithms that are optimized for real-time inference at the edge, ensuring privacy and minimizing data transmission. SeizNet achieves > 97% accuracy in seizure prediction while keeping the size and energy restrictions of an implantable device. |
| title | Demo: Multi-Modal Seizure Prediction System |
| topic | Signal Processing Machine Learning |
| url | https://arxiv.org/abs/2411.05817 |