Analog Seizure Detection for Implanted Responsive Neurostimulation

Epilepsy can be treated with medication, however, 30% of epileptic patients are still drug resistive. Devices like responsive neurostimluation systems are implanted in select patients who may not be amenable to surgical resection. However, state-of-the-art devices suffer from low accuracy and high sensitivity. We propose a novel patient-specific seizure detection system based on naı̈ve Bayesian inference using Müller C-elements. The system improves upon the current leading neurostimulation device, NeuroPace’s RNS by implementing analog signal processing for feature extraction, minimizing the power consumption compared to the digital counterpart. Preliminary simulations were performed in MATLAB, demonstrating that through integrating multiple channels and features, up to 98% detection accuracy for individual patients can be achieved. Similarly, power calculations were performed, demonstrating that the system uses 6.5μW per channel, which when compared to the state-of-the-art NeuroPace system would increase battery life by up to 50%. Keywords—Seizure Detection; Refractory Epilepsy; Responsive Neurostimulation; Analog Signal Processing; Stochastic Computing; Naı̈ve Bayesian Inference

[1]  Yong Lian,et al.  A 1-V 450-nW fully integrated biomedical sensor interface system , 2008, 2008 IEEE Symposium on VLSI Circuits.

[2]  David H. K. Hoe,et al.  Bayesian inference using stochastic logic: A study of buffering schemes for mitigating autocorrelation , 2019, Int. J. Approx. Reason..

[3]  Mahsa Shoaran,et al.  Energy-Efficient Classification for Resource-Constrained Biomedical Applications , 2018, IEEE Journal on Emerging and Selected Topics in Circuits and Systems.

[4]  Brian M. Sutton,et al.  Stochastic p-bits for Invertible Logic , 2016, 1610.00377.

[5]  Chen Zhang,et al.  A 16-Channel Patient-Specific Seizure Onset and Termination Detection SoC With Impedance-Adaptive Transcranial Electrical Stimulator , 2015, IEEE Journal of Solid-State Circuits.

[6]  Joseph S. Friedman,et al.  Low-Energy Truly Random Number Generation with Superparamagnetic Tunnel Junctions for Unconventional Computing , 2017, 1706.05262.

[7]  Yong Lian,et al.  A 1V 22µW 32-channel implantable EEG recording IC , 2010, 2010 IEEE International Solid-State Circuits Conference - (ISSCC).

[8]  Mohamad Sawan,et al.  Towards accurate prediction of epileptic seizures: A review , 2017, Biomed. Signal Process. Control..

[9]  Anantha Chandrakasan,et al.  An 8-Channel Scalable EEG Acquisition SoC With Patient-Specific Seizure Classification and Recording Processor , 2013, IEEE Journal of Solid-State Circuits.

[10]  Supriyo Datta,et al.  Implementing p-bits With Embedded MTJ , 2017, IEEE Electron Device Letters.

[11]  Felice T. Sun,et al.  The RNS System: responsive cortical stimulation for the treatment of refractory partial epilepsy , 2014, Expert review of medical devices.

[12]  P. Boon,et al.  Neurostimulation for drug-resistant epilepsy: a systematic review of clinical evidence for efficacy, safety, contraindications and predictors for response. , 2018, Current opinion in neurology.

[13]  Joseph Picone,et al.  The Temple University Hospital Seizure Detection Corpus , 2018, Front. Neuroinform..

[14]  Mohamad Sawan,et al.  From Seizure Detection to Smart and Fully Embedded Seizure Prediction Engine: A Review , 2020, IEEE Transactions on Biomedical Circuits and Systems.

[15]  J. Gaitanis,et al.  Neuromodulation for the Treatment of Epilepsy: a Review of Current Approaches and Future Directions. , 2020, Clinical therapeutics.

[16]  Nanjian Wu,et al.  An ultra-low power CMOS random number generator , 2008 .

[17]  C. Teixeira,et al.  A personalized and evolutionary algorithm for interpretable EEG epilepsy seizure prediction , 2021, Scientific Reports.

[18]  David Blaauw,et al.  A robust −40 to 120°C all-digital true random number generator in 40nm CMOS , 2015, 2015 Symposium on VLSI Circuits (VLSI Circuits).

[19]  Timothy Denison,et al.  A 2.2/spl mu/W 94nV//spl radic/Hz, Chopper-Stabilized Instrumentation Amplifier for EEG Detection in Chronic Implants , 2007, 2007 IEEE International Solid-State Circuits Conference. Digest of Technical Papers.

[20]  Jacques Droulez,et al.  Bayesian Inference With Muller C-Elements , 2016, IEEE Transactions on Circuits and Systems I: Regular Papers.

[21]  Vikram Suresh,et al.  $\mu $ RNG: A 300–950 mV, 323 Gbps/W All-Digital Full-Entropy True Random Number Generator in 14 nm FinFET CMOS , 2016, IEEE Journal of Solid-State Circuits.

[22]  Jiawei Yang,et al.  Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram , 2018, Neural Networks.

[23]  Magdy Bayoumi,et al.  Efficient Epileptic Seizure Prediction Based on Deep Learning , 2019, IEEE Transactions on Biomedical Circuits and Systems.