Continuous Fault Tolerant Monitoring of the Brain in Epilepsy

Episodic brain disorders such as epilepsy have a considerable impact on a patient's quality of life and may be life-threatening when seizures cannot be controlled with medications. We are creating a second generation brain-implantable sensing and stimulating device (BISSD) based on CPS principles and practice. The BISSD will be composed of modules placed intracranially to continuously monitor brain state and vulnerability to seizure and intervene with electrical stimulation to block the development of seizure. We propose a systematic design and test roadmap beginning with safety and efficacy evaluation in a rat model of epilepsy. This roadmap is designed to meet regulatory needs to demonstrate safety and efficacy prior to testing and use in humans. In this phase of the work we will develop and test a fully functional miniaturized BISSD for a rat model of epilepsy. Importantly the design will be composable, scalable, certifiable, interoperable, and dependable. We argue these essential CPS aspects will allow programmatic scaling from rat to larger animals and then to human with efficient re-use of developed modules.

We propose the development of a BISSD as a paradigm medical device project exemplifying several defining aspects that inform and illustrate core CPS principles. First, to meet the important challenge of regulatory approval we propose a composable, scalable and certifiable framework that supports testing in multiple species. Second, we argue a BISSD must be wholly integrated with the patient and fully cognizant at every instant of brain state, including dynamic changes in both the normal and abnormal expression of brain physiology and therapeutic intervention. We thus seek a tight conjunction of the cyber solution which must monitor itself and monitor and stimulate the brain using implanted, adaptable, distributed, and networked electrodes, and the physical system which in this case is the intermittently failing human brain. Third, a BISSD must function for an extensive period of time, up to the life of the patient, because each surgery to place and retrieve a BISSD carries an attendant risk. We argue this requirement necessitates a dependable solution and seek to achieve reliability through both an understanding of the brain's foreign body response and a unique hierarchical fault-tolerant design. Fourth, we use advanced salient approaches to acquire, compress, and analyze sensor signals to achieve real-time monitoring and control of seizures.

This presentation describes innovative technologies which are being brought to bear on the direct sensing of the brain in epilepsy and intervention to control seizures. The first is the development of a brain implantable solution for the battery free wireless transmission of intracranial EEGs (icEEGs). The second is the development of a solution for the fault-tolerant monitoring of brain activity. In the first solution we have designed, fabricated and tested a prototype 64 channel brain implantable device for the wireless transmission of icEEGs. The device allows digital icEEG acquisition and transmission through a standard infra-red (IR) data link and has the potential to perform electrical stimulation. The device can be powered by an embedded battery, wired external power or battery free power through a radio frequency (RF) power link. Bench-top, ex-vivo and in-vivo rat evaluations of the 64-channel wireless icEEG device demonstrate proof-of-principle for an implantable solution to sense, condition, amplify, digitize and wirelessly transmit multi-channel icEEGs. The second solution stems from an argument for the inclusion of fault-tolerance in brain implantable devices to extend their dependability. We focus on multielectrode arrays (MEAs) and propose two redundancy based solutions. The first solution uses rows or columns of spare modules to replace faulty modules within a MEA. The second solution uses space redundancy with local reconfiguration. Different fault-tolerant solutions with varying degrees of redundancy and the equivalent graph models for these solutions have been described by us. A maximum matching algorithm is described to match faulty primary to functioning spare modules for MEA reconfiguration. The results of our analysis demonstrate that a considerable improvement in MEA dependability can be achieved with a well-designed increase in redundancy.

Through the incorporation of these two solutions we seek to build a powerful, scalable CPS framework for robust fault-tolerant implantable medical devices with real-time processing that can grow with advances in sensors, sensing modalities, time-series analysis, real-time computation, control, materials, power and knowledge of underlying biology. In addition to epilepsy, advances made here can be expected to benefit the treatment of other neurological and psychiatric brain disorders.