An energy efficient epileptic seizure detector

Epilepsy is one of the most common neurological disorders affecting up to 1% of the world's population and approximately 2.5 million people in the United States. Seizures in more than 30% of epilepsy patients are resistant to anti-epileptic drugs. A significant biomedical research is focused on the development of an energy efficient implantable integrated circuit for real-time detection of seizures. In this paper we propose an architecture for an implantable seizure detector using a hyper-synchronous signal detection circuit and signal rejection algorithm (SRA). The proposed seizure detector (SD) continuously monitors neural signals for hyper-synchronous pulses and extracts the seizure onset signal. If the pulses in an epoch exceed a threshold value, a seizure is declared. The design was validated using Simulink®. The signal rejection algorithm (SRA) reduces false detection and minimal circuitry leads to a 12% reduction of power consumption.

[1]  Mohamad Sawan,et al.  Low-power CMOS-based epileptic seizure onset detector , 2009, 2009 Joint IEEE North-East Workshop on Circuits and Systems and TAISA Conference.

[2]  Mohamad Sawan,et al.  A low-power implantable device for epileptic seizure detection and neurostimulation , 2010, 2010 Biomedical Circuits and Systems Conference (BioCAS).

[3]  Kaushik Roy,et al.  The design and hardware implementation of a low-power real-time seizure detection algorithm , 2009, Journal of neural engineering.

[4]  Ali H. Shoeb,et al.  Application of machine learning to epileptic seizure onset detection and treatment , 2009 .

[5]  Kunjan Patel,et al.  Low power real-time seizure detection for ambulatory EEG , 2009, 2009 3rd International Conference on Pervasive Computing Technologies for Healthcare.

[6]  F. Mormann,et al.  Mean phase coherence as a measure for phase synchronization and its application to the EEG of epilepsy patients , 2000 .

[7]  Zhihua Wang,et al.  An Energy-Efficient ASIC for Wireless Body Sensor Networks in Medical Applications , 2010, IEEE Transactions on Biomedical Circuits and Systems.

[8]  Elias Kougianos Nanoelectronic Mixed-Signal System Design [Book Reviews] , 2017, IEEE Consumer Electronics Magazine.

[9]  Ali Shoeb,et al.  Patient-specific seizure onset detection. , 2004, Epilepsy & behavior : E&B.

[10]  Naveen Verma,et al.  A Micro-Power EEG Acquisition SoC With Integrated Feature Extraction Processor for a Chronic Seizure Detection System , 2010, IEEE Journal of Solid-State Circuits.

[11]  Ivan Osorio,et al.  Analog seizure detection and performance evaluation , 2006, IEEE Transactions on Biomedical Engineering.

[12]  Ralph G Andrzejak,et al.  Nonrandomness, nonlinear dependence, and nonstationarity of electroencephalographic recordings from epilepsy patients. , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.