Project: Brain Implants

May 30th, 2019

Implantable iEEG Monitoring and Seizure Detection System for Traumatic Brain Injury

An autonomous implantable electroencephalography (EEG) monitoring and seizure detection system is being developed for continuous real-time monitoring of traumatic brain injury (TBI) patients in their real environment, to track the condition of brain activity and potential epileptic seizure development.

TBI patients’ journey image *Image Credit: Craniectomy, Rehabilitation, and Cranioplasty images supplied by Anatomics Pty Ltd.

Unmet Clinical Need

It is crucial to monitor brain activity continuously to detect early seizures for proper treatment to prevent epilepsy development. Yet, current practise in EEG monitoring is only within hospital environments with bulky devices and up to 24 hours.

Therefore, a mobile and wireless continuous monitoring and automatic seizure detection system is needed.

The iEEG Monitoring and Seizure Detection System aims to provide a portable, yet high precision platform for medical practitioners, to identify and localise early seizures to facilitate optimised and personalised treatment. This invasive implantable system brings no additional operational risk as the target patients must have open head surgery, providing surgeons access to the patient’s brain, as part of the medical intervention.

Implantable iEEG Monitoring and Seizure Detection

Implantable iEEG monitoring and seizure detection system illustration.

  • Durashield® is utilised with multiple standalone implants. Thus, implants are invasive yet with no additional risk.
  • Inductive energy harvesting.
  • Ultra-Wide-Band (UWB) wireless communication for high data rate.
  • On-chip signal processing.
  • 3D micro-battery for high density energy storage.
  • On-chip cryptographic encryption for information security.
  • Automatically switch between Active (14mW) and Standby (1.4mW) mode depending on brain activity. Continuous low resolution (8-bit @ 512Hz) monitoring in Standby mode and precise high resolution (16-bit @ 10kHz) acquisition of seizures in Active mode.
  • On-chip Machine Learning with dedicated hardware accelerator for seizure detection and prediction.

On-Chip ML for Seizure Detection

An on-chip Machine Learning algorithm is being developed for automatic seizure detection and prediction by using Traumatic Brain Injury data provided by Monash University.

Laboratory animal model for Traumatic Brain Injury, EEG signal with seizure annotation and the Short-Time Fourier Transform (STFT) of the seizure EEG. *Image Credit:  Department of Neuroscience, Monash University.

Our highly skilled team of world class researchers and engineers is open to partnerships and collaborations for research, development, and commercialisation.

Contact us to learn more.

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