A low complexity patient-specific threshold based accelerator for the Grand-mal seizure disorder

This paper presents a 2-channel electroencephalograph (EEG) based seizure detection accelerator suitable for long-term continuous monitoring of patients suffering from the Grand-mal seizure disorder. The implementation is based on the novel slope based detection (SBD) algorithm to achieve start and end of seizure detection. The proposed SBD algorithm is verified experimentally using a full FPGA implementation with patients' recordings from Physionet Children Hospital Boston (CHB)-MIT EEG database with real-time seizure, information display on the Android phone through a low-power Bluetooth link. The patient-specific detection with specific threshold results in sensitivity, specificity, system latency, and detection latency of 91.2%, 93.6%, 0.5s, and 29.25 s, respectively, using the CHB-MIT EEG database.

[1]  Chen Zhang,et al.  21.8 A 16-ch patient-specific seizure onset and termination detection SoC with machine-learning and voltage-mode transcranial stimulation , 2015, 2015 IEEE International Solid-State Circuits Conference - (ISSCC) Digest of Technical Papers.

[2]  Chen Zhang,et al.  A 16-channel, 1-second latency patient-specific seizure onset and termination detection processor with dual detector architecture and digital hysteresis , 2015, 2015 IEEE Custom Integrated Circuits Conference (CICC).

[3]  Jerald Yoo,et al.  A hybrid OFDM body coupled communication transceiver for binaural hearing aids in 65nm CMOS , 2015, 2015 IEEE International Symposium on Circuits and Systems (ISCAS).

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

[5]  Mohamad Sawan,et al.  A Fully-Asynchronous Low-Power Implantable Seizure Detector for Self-Triggering Treatment , 2013, IEEE Transactions on Biomedical Circuits and Systems.

[6]  Jeffrey M. Hausdorff,et al.  Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .

[7]  Jerald Yoo,et al.  A 1.83µJ/classification nonlinear support-vector-machine-based patient-specific seizure classification SoC , 2013, 2013 IEEE International Solid-State Circuits Conference Digest of Technical Papers.

[8]  Muhammad Bin Altaf,et al.  A high accuracy and low latency patient-specific wearable fall detection system , 2017, 2017 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI).

[9]  S. Nasehi,et al.  Seizure Detection Algorithms Based on Analysis of EEG and ECG Signals: a Survey , 2012, Neurophysiology.

[10]  Naveen Verma,et al.  A Low-Power Processor With Configurable Embedded Machine-Learning Accelerators for High-Order and Adaptive Analysis of Medical-Sensor Signals , 2013, IEEE Journal of Solid-State Circuits.

[11]  Jerald Yoo,et al.  A 1.52 uJ/classification patient-specific seizure classification processor using Linear SVM , 2013, 2013 IEEE International Symposium on Circuits and Systems (ISCAS2013).

[12]  Chih-Wei Chang,et al.  A fully integrated 8-channel closed-loop neural-prosthetic SoC for real-time epileptic seizure control , 2013, 2013 IEEE International Solid-State Circuits Conference Digest of Technical Papers.