Performance Estimation of an Implantable Epileptic Seizure Detector with a Low-power On-chip Oscillator

Abstract: Implantable closed-loop epilepsy controllers require ideally both accurate epileptic seizure detection andlow power consumption. On-chip oscillators can be used in implantable devices because they consume less powerthan other oscillators such as crystal oscillators. In this study, we investigated the tolerable error range of a lowerpower on-chip oscillator without losing the accuracy of seizure detection. We used 24 ictal and 14 interictal intrac-ranial electroencephalographic segments recorded from epilepsy surgery patients. The performance variations withrespect to oscillator frequency errors were estimated in terms of specificity, modified sensitivity, and detection timingdifference of seizure onset using Generic Osorio Frei Algorithm. The frequency errors of on-chip oscillators wereset at ± 10% as the worst case. Our results showed that an oscillator error of ± 10% affected both specificity andmodified sensitivity by less than 3%. In addition, seizure onsets were detected with errors earlier or later than with-out errors and the average detection timing difference varied within less than 0.5 s range. The results suggest thaton-chip oscillators could be useful for low-power implantable devices without error compensation circuitry requiringsignificant additional power. These findings could help the design of closed-loop systems with a seizure detector andautomated stimulators for intractable epilepsy patients.Key words: implantable biomedical devices, low power, on-chip oscillators, performance evaluation, seizure detector

[1]  J. Carey Brain Facts: A Primer on the Brain and Nervous System. , 1990 .

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

[3]  Hoi-Jun Yoo,et al.  A Low-Energy Crystal-Less Double-FSK Sensor Node Transceiver for Wireless Body-Area Network , 2012, IEEE Journal of Solid-State Circuits.

[4]  Ali H. Shoeb,et al.  Application of Machine Learning To Epileptic Seizure Detection , 2010, ICML.

[5]  G. Bergey,et al.  Characterization of early partial seizure onset: Frequency, complexity and entropy , 2012, Clinical Neurophysiology.

[6]  A.P. Chandrakasan,et al.  A 65 nm Sub-$V_{t}$ Microcontroller With Integrated SRAM and Switched Capacitor DC-DC Converter , 2008, IEEE Journal of Solid-State Circuits.

[7]  F. Mormann,et al.  Seizure prediction: the long and winding road. , 2007, Brain : a journal of neurology.

[8]  Jefferson Soldera,et al.  A temperature compensated CMOS relaxation oscillator for low power applications , 2012, 2012 25th Symposium on Integrated Circuits and Systems Design (SBCCI).

[9]  Dustin Scheinost,et al.  Altered functional connectivity in seizure onset zones revealed by fMRI intrinsic connectivity , 2014, Neurology.

[10]  I. Osorio,et al.  Real‐Time Automated Detection and Quantitative Analysis of Seizures and Short‐Term Prediction of Clinical Onset , 1998, Epilepsia.

[11]  Hongwei Hao,et al.  Epileptic seizure detection with the local field potential of anterior thalamic of rats aiming at real time application , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[12]  Pedro P. Irazoqui,et al.  A hardware-algorithm co-design approach to optimize seizure detection algorithms for implantable applications , 2010, Journal of Neuroscience Methods.

[13]  Hyang Woon Lee,et al.  Neuromodulation Therapy: Nonmedical, Nonsurgical Treatment for Intractable Epilepsy , 2014 .

[14]  K. Makinwa,et al.  A Low-Voltage Mobility-Based Frequency Reference for Crystal-Less ULP Radios , 2009, IEEE Journal of Solid-State Circuits.

[15]  Josemir W Sander,et al.  The natural history and prognosis of epilepsy. , 2015, Epileptic disorders : international epilepsy journal with videotape.

[16]  Minkyu Je,et al.  A precision relaxation oscillator with a self-clocked offset-cancellation scheme for implantable biomedical SoCs , 2009, 2009 IEEE International Solid-State Circuits Conference - Digest of Technical Papers.

[17]  Mohamad Sawan,et al.  A Novel Low-Power-Implantable Epileptic Seizure-Onset Detector , 2011, IEEE Transactions on Biomedical Circuits and Systems.

[18]  Sheng-Fu Liang,et al.  A Portable Wireless Online Closed-Loop Seizure Controller in Freely Moving Rats , 2011, IEEE Transactions on Instrumentation and Measurement.

[19]  Sungwook Kim,et al.  Adaptive Online Voltage Scaling Scheme Based on the Nash Bargaining Solution , 2011 .

[20]  Xiao Han,et al.  Seizure localization using three-dimensional surface projections of intracranial EEG power , 2013, NeuroImage.

[21]  Sourabh Ravindran,et al.  Low complexity algorithms for heart rate and epileptic seizure detection , 2009, 2009 2nd International Symposium on Applied Sciences in Biomedical and Communication Technologies.

[22]  Hillel J. Chiel,et al.  Ultra-Low-Power and Robust Digital-Signal-Processing Hardware for Implantable Neural Interface Microsystems , 2011, IEEE Transactions on Biomedical Circuits and Systems.