Publications by authors named "Achraf Djemal"
Article Synopsis
- Diagnosing epilepsy through EEG signals is complex and error-prone due to variability and the large amount of data involved, making portable diagnostic systems challenging to develop.
- The paper proposes using compressive sensing to reduce EEG data while keeping important information, enabling better seizure classification using features extracted from the signals.
- Implemented on microcontrollers like STM32 and Raspberry Pi, this system achieved significant advances, including up to 70% data reduction, faster transmission times, notable energy savings, and a high classification accuracy of 98.78% with preserved signal quality.
Wearable Electromyography Classification of Epileptic Seizures: A Feasibility Study.
Bioengineering (Basel)
June 2023
Accurate diagnosis and classification of epileptic seizures can greatly support patient treatments. As many epileptic seizures are convulsive and have a motor component, the analysis of muscle activity can provide valuable information for seizure classification. Therefore, this paper present a feasibility study conducted on healthy volunteers, focusing on tracking epileptic seizures movements using surface electromyography signals (sEMG) measured on human limb muscles.
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