System-Level Modeling of a Safe Autonomous Closed-loop Epileptic Seizure Control Implant

Abstract

A system-level model of a closed-loop epilepsy control implant is designed and simulated using the Simulink software for the first time in this work. The proposed model consists of multi-channel signal acquisition, ADC, seizure detector, multi-channel electrical stimulator, temperature sensor, wireless data and power transmission blocks. The power dissipation and temperature elevation of implants are crucial parameters which must be precisely controlled to ensure the safety of patients. The system is optimized to offer a low-power design. In addition, the controller of the system modifies the stimulation parameters to maintain the temperature elevation at the tissue within 1℃. Intracranial EEG (iEEG) datasets of five patients from the Inselspital Bern are used to evaluate the performance of the presented system. A two-stage seizure detection method used in the bio-signal processor enables achieving accurate detection with an energy efficient-approach.

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

“…Subsequently, the charge balancing processor calculates the required balance current duration to lower the residual voltage to −V th2 in a one-shot manner. V th2 is defined as the maximum residual voltage that can be compensated by the offset balance current that is given in (2).…”

Computation Complexity Reduction Technique for Accurate Seizure Detection Implants

“…seizure detection using brain electroencephalogram (EEG) has become an attractive research topic to replace the continuous monitoring of patients' EEG signals by neurologists over a long period. The seizure detection unit which consists of feature extraction and classification is the heart of a closed-loop epilepsy control device [1]. Several feature extraction algorithms have already been reported in the literature to precisely detect/predict seizure occurrences.…”

“…[4] lacks a systematic correlation between feature extraction and channel selection units. [1] reduces the feature dimension by using a two-stage architecture feature extractor. In this case, the effective number of features processed by the detector is remarkably reduced.…”

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

“…Therefore, it is highly beneficial to demonstrate abnormal brain activity. The variance equation is given in (2).…”

“…needed [2]. Early detection would allow a successful neural stimulation or drug delivery that could avoid numerous complications including sudden falls, drowning, car accidents, and suicidal tendencies.…”