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Keyvan Farhang Razi

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Publications associées (7)

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Programmable Seizure Detector Using a 32-bit RISC Processor for Implantable Medical Devices

Alexandre Schmid, Keyvan Farhang Razi

A programmable patient-customized epileptic seizure detector is proposed in this paper to enable neurologists and patients to have constructive interactions with the implantable medical device. The programmability feature is enabled by designing a low-powe ...

IEEE2023

Digital One-Shot Charge-balancing Method for Implantable Current-Mode Electrical Stimulation

Alexandre Schmid, Keyvan Farhang Razi

A low-power digital charge balancing system, which ensures the safe operation of constant-current biphasic stimulation is presented. The concept of the proposed charge-balancing technique is to utilize a hybrid method consisting of anodic pulse modulation ...

IEEE2023

Epilepsy is a common chronic neurological disorder that causes recurring seizures and affects more than 50 million people worldwide. Implantable medical devices (IMDs) are regarded as effective tools to cure patients who suffer from refractory epilepsy. Se ...

EPFL2023

An accurate epileptic seizure detector using intracranial electroencephalography (iEEG) recordings, suitable for low-power wearable/implantable applications, is presented. Eleven time-domain features with low hardware complexity are employed in the feature ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

Computation Complexity Reduction Technique for Accurate Seizure Detection Implants

Alexandre Schmid, Keyvan Farhang Razi

An automatic seizure detection method from high-resolution intracranial-EEG (iEEG) signals is presented to minimize the computational complexity and realize real-time accurate seizure detection for biomedical implants. Complex signal processing on a large ...

IEEE2022

Hardware-Friendly Random Forest Classification of iEEG Signals for Implantable Seizure Detection

Alexandre Schmid, Keyvan Farhang Razi

Early and accurate detection of epileptic seizures is an extremely important therapeutic goal due to the severity of complications it can prevent. To this end, a low-power machine learning-based seizure detection implemented on an FPGA is proposed in this ...

IEEE2022

Two-stage Hardware-Friendly Epileptic Seizure Detection Method with a Dynamic Feature Selection

Alexandre Schmid, Keyvan Farhang Razi

A novel low-complexity method of detecting epileptic seizures from intracranial encephalography (iEEG) signals is presented. In the proposed algorithm, coastline, energy and nonlinear energy features of iEEG signals are extracted in a patient-specific two- ...

IEEE2021