Publication
Implantable electronic medical device (IEMD) is an emerging technology that plays an important role in the treatment of several neurological disorders such as epilepsy, especially in the cases of drug-resistant epilepsy. Recent developments and studies show the possibility of accurate detection of seizure onset and stopping the seizures or shortening their duration using electrical neural stimulation. By employing these devices, the quality of the life of the patients who could not benefit from anti-epileptic medicines or surgery will improve, significantly. An IEMD uses a neural recording interface to sense and amplify neural signals. Increasing the number of recording channels from few recording channels to hundreds is a promising feature of the future IEMDs. However, employing hundreds of recording channels brings several challenges including minimizing the power consumption which relates to the temperature elevation in the device and may damage the tissues around the IEMD. In order to implement a low-power implantable electronic device for neural recording, epilepsy detection and epilepsy control with a high spatiotemporal resolution, some techniques should be devised to solve this issue.
Closed-loop implantable electronics is a new trend for controlling seizures by detecting them and applying an electrical stimulation to the brain or a group of nerves. These devices employ electrical stimulators; however, electrical stimulation requires very careful consideration due to the safety issues. A poor design of the electrical stimulator may damage the brain tissue and endanger the life of the patients. Hence, charge balancers are introduced to ensure the safety of the electrical stimulation. In addition, closed-loop implantable devices for seizure control require an accurate seizure onset detector. These devices should detects all the seizures with the least latency to stop the seizures or stop them to spread in the brain.
The first part of this thesis presents a low-power and compact size seizure detection system while discussing the challenges in the design of neural amplifiesr, feature extractors and data compression circuits. Then a comprehensive study on electrical stimulation and charge balancing are provided. Consequently, circuit techniques to achieve a low-power, low-are and accurate charge balancers are present. Finally, a neural recording system based on orthogonal sampling and two closed-loop stimulation systems for epilepsy control are proposed. Orthogonal sampling enables reducing the number of the ADCs in conventional recording systems into one single unit while simultaneously recording from several channels. Consequently, the ADC bandwidth and dynamic range is effectively employed and shared between all the channels without any loss in the temporal information of the channels during sampling, which is not the case of time-multiplexed ADCs. In an IoT based closed-loop stimulation system for epilepsy control, the system is