7.1 Conclusion
In this thesis, a fast parameter determination method is proposed. It is proposed that using the mean and the multiples of standard deviation finds the optimal model rapidly. The proposed parameter determination method is 416106 times faster than our previous work, and it can attain the same performance. Moreover, a processor core which bases on RISC technology consumes only 6 mW for real-time epileptic seizure detection algorithm.
Compared with our previous prototype, the measurement results show that the implemented processor can reduce 93.8% power consumption. The developed bio-signal processor, firmware and epileptic seizure detection algorithm is able to detect the seizure signals in 0.63-0.79 s, and the detection accuracy is 92-99%. The measurement results are based on the recorded EEG signals of four free moving animal models.
7.2 Future Work
The OpenRISC core will be integrate with analog front-end circuitries and stimulators to realize a chip-on-board seizure controller as shown in Fig. 7.1 (a). The AFE and stimulator board is shown in Fig. 7.1 (b), and the epileptic seizure detector board which uses Altera Cyclone III FPGA is shown in Fig. 7.1 (c). The successful of this research provides a solid base to integrate with analog front-end circuitries and stimulators to build up a system-on-a-chip solution. The developed seizure detector can be applied to monitor the online EEG signals and integrate with analog front-end circuitries and an electrical stimulator to perform a closed-loop seizure controller in the future.
27 mm × 25 mm 37 mm × 39 mm
AFE Board on the Head
Experimental Subject
Seizure Detector on the Back
AFE and Stimulator Board
Epileptic Seizure Detector Board
AFE I/F
Epileptic Seizure Detector in EP3C25E144C8N FPGA Pre-amplifier
and Filter
10-bit ADC
EEG Data (Digital)
AFE Control
Host I/F & Flash I/F 25.0 MHz
Oscillator
(a)
(b) (c)
Fig. 7.1 Closed-loop seizure controller using FPGA.
Publications
T.-J. Chen, H. Chiueh, S.-F. Liang, S.-T. Chang, C. Jeng, Y.-C. Hsu, and T.-C. Chien,
"The Implementation of a Low-Power Biomedical Signal Processor for Real-Time Epileptic Seizure Detection on Absence Animal Models," IEEE Journal on Emerging
and Selected Topics in Circuits and Systems, vol. 1, no. 4, Dec. 2011.
T.-J. Chen, C. Jeng, S.-T. Chang, H. Chiueh, S.-F. Liang, Y.-C. Hsu, and T.-C. Chien, "A Hardware Implementation of Real-Time Epileptic Seizure Detector on FPGA," in IEEE
Biomedical Circuit and Systems Conference (BioCAS '11), San Diego, USA, Nov. 10-12,
2011. S.-T. Chang, T.-J. Chen, C. Jeng, S.-F. Liang, and H. Chiueh, "Fast Training for Epileptic Seizure Detector," in 3rd International Conference on Neuroprosthetic Devices
(ICNPD '11), Sydney, Australia, Nov. 25-26, 2011, pp. 26-28.
C. Jeng, T.-J. Chen, S.-T. Chang, and H. Chiueh, "Implementation of Low-Power Multi-Channel Close-Loop Epileptic Seizure Detection," in 3rd International Conference
on Neuroprosthetic Devices (ICNPD '11), Sydney, Australia, Nov. 25-26, 2011, pp.
23-25.
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