A smart headband for epileptic seizure detection

Epilepsy is a common neural disorder disease; about 1.7% of the global population has epilepsy. Most patients take antiepileptic drugs to reduce their seizures. Among them, nearly one-third of the patients are drug-resistant epilepsy. The alternative treatment is the resection surgery of removing the epileptogenic zone. However, all above patients will still have some seizures, which will influence the patients' quality-of-life, and further introduce danger and inconvenience to patients and people around. This paper presents the design and develop of the smart headband for epileptic seizure detection. The headband consists of a textile headband with flexible print circuit (FPC) inside, and fabric electrodes on it. The whole system includes the following circuits: an analog front-end circuitry for electroencephalography (EEG) record, an epileptic seizure detection System-on-Chip (SoC), and a Bluetooth Low Energy (BLE) Chip. The result of designed circuits yields a compact and low-power design of smart headband for epileptic seizure detection which is suitable for wearable usage.

[1]  Herming Chiueh,et al.  A multi-channel multi-mode physiological signals acquisition and analysis platform , 2013, 2013 IEEE International Symposium on Circuits and Systems (ISCAS2013).

[2]  K.B. Englehart,et al.  Multiple Binary Classifications via Linear Discriminant Analysis for Improved Controllability of a Powered Prosthesis , 2010, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[3]  Sheng-Fu Liang,et al.  A hierarchical approach for online temporal lobe seizure detection in long-term intracranial EEG recordings , 2013, Journal of neural engineering.

[4]  Sheng-Fu Liang,et al.  EEG-based absence seizure detection methods , 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

[5]  Sheng-Fu Liang,et al.  Combination of EEG Complexity and Spectral Analysis for Epilepsy Diagnosis and Seizure Detection , 2010, EURASIP J. Adv. Signal Process..

[6]  Sheng-Fu Liang,et al.  The Implementation of a Low-Power Biomedical Signal Processor for Real-Time Epileptic Seizure Detection on Absence Animal Models , 2011, IEEE Journal on Emerging and Selected Topics in Circuits and Systems.

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

[8]  S M Pincus,et al.  Approximate entropy as a measure of system complexity. , 1991, Proceedings of the National Academy of Sciences of the United States of America.

[9]  Chia-Hsiang Yang,et al.  A 28.6µW mixed-signal processor for epileptic seizure detection , 2013, 2013 Symposium on VLSI Circuits.