Special Seminar: Empowering Implantable Devices, Dec 16 by Prof. Artan

Title: Empowering Implantable Devices

Prof. N. Sertac Artan, Department of Electrical and Computer Engineering, Polytechnic Institute of New York University

Date: Monday, December 16 Location: T-623, Conference Room (Electrical Engineering), Steinman Hall  – Time: 1:00 PM

From pacemakers to responsive neurostimulators, implantable devices offer vital treatment options. As new therapies are developed, the need for more capable implantable devices, running sophisticated algorithms in real-time, and generating large amounts of data traffic, grows. The severe power and space constraints impose significant challenges to the development of such devices, which should avoid tissue heating, frequent surgery for battery replacement, or high bandwidth requirements. These constraints lead to trade-offs in monitoring and treatment options. As complex engineering systems, the medical implants often require the interaction between various subsystems. An algorithm for extracting necessary information dictates the minimum requirements for the sensors and signal processing path to ensure the quality and integrity of the acquired physiological signals, as well as the bandwidth requirements of an optional telemetry subsystem. In return, the capabilities of the sensors and the signal processing path along with the tight power and space requirements dictate the features and limit the accuracy of these algorithms. Independent optimization of these subsystems neglecting their intricate interactions usually prevents these systems to fulfill their full potential. My research goal is to design next generation safe and highly-capable implantable devices focusing on cross subsystem optimization spanning from circuits and algorithms to networking based on characteristics of the specific target application. Currently, I am working on developing implantable devices for epilepsy in particular and for neurological diseases in general. In this talk, I will give some examples of our recent work on low-power VLSI circuits and epileptic seizure monitoring algorithms for embedded systems targeting implantable applications.

Neural Engineering