A 16-ch patient-specific seizure onset and termination detection SoC with machine-learning and voltage-mode transcranial stimulation

Digest of Technical Papers - IEEE International Solid-State Circuits Conference

Volumn 58, Issue , 2015, Pages 394-395

  Altaf, Muhammad Awais Bin ^a   Zhang, Chen ^a   Yoo, Jerald ^a  

^a KHALIFA UNIVERSITY   (United Arab Emirates)

Author keywords

[No Author keywords available]

Indexed keywords

MACHINE LEARNING;

MEDICAL PRACTICE; MULTICHANNEL EEG; PATIENT SPECIFIC; PULSATING VOLTAGE; SEIZURE DETECTION; TERMINATION DETECTION; TRANSCRANIAL ELECTRICAL STIMULATIONS; ULTRA LOW POWER;

SYSTEM-ON-CHIP;

EID: 84940756849     PISSN: 01936530     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ISSCC.2015.7063092     Document Type: Conference Paper

References (8)

1. J. Yoo, et al., "An 8-channel scalable EEG acquisition SoC with fully integrated patient-specific seizure classification and recording processor, " ISSCC Dig. Tech. Papers, pp. 292-293, Feb. 2012.
2. M. Altaf, et al., "A 1.83uJ/Classification Nonlinear Support-Vector Machine-Based Patient-Specific Seizure Classification SoC, " ISSCC Dig. Tech. Papers, pp. 100-101, Feb. 2013.
3. W. Chen, et al., "A fully integrated 8-channel closed-loop neural-prosthetic SoC for real-Time epileptic seizure control, " ISSCC Dig. Tech. Papers, pp. 286-287, Feb. 2013.
4. J. Xu, et al., "A 60nV/√Hz 15-channel digital active electrode system for portable biopotential signal acquisition, " ISSCC Dig. Tech. Papers, pp. 424-425, Feb. 2014.
5. A. H. Shoeb, et al., "A machine-learning algorithm for detecting seizure termination in scalp EEG, " Epilepsy & Behavior, vol. 22, no. 1, pp. 36-43, Dec. 2011.
6. A. Berenyi, et al., "Closed-Loop Control of Epilepsy by Transcranial Electrical Stimulation, " Science, vol. 337, no. 6095, pp. 735-737, Aug. 2012.
7. T. Roh, et al., "A 2.14mW EEG neuro-feedback processor with transcranial electrical stimulation for mental-health management, " ISSCC Dig. Tech. Papers, pp. 318-319, Feb. 2014.
8. S. Kim, et al., "A 20μW intra-cardiac signal-processing IC with 82dB bio-impedance measurement dynamic range and analog feature extraction for ventricular fibrillation detection, " ISSCC Dig. Tech. Papers, pp. 302-303, Feb. 2013.