The Implementation of a Low-Power Biomedical Signal Processor for Real-Time Epileptic Seizure Detection on Absence Animal Models

Chen, Tsan-Jieh; Chiueh, Herming; Liang, Sheng-Fu; Chang, Shun-Ting; Jeng, Chi; Hsu, Yu-Wei; Chien, Tzu-Chieh

doi:10.1109/jetcas.2011.2174472

Cited by 27 publications

(7 citation statements)

References 27 publications

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“…Table II shows the 2-case study results of above described patients. Table III compares the epileptic seizure detectors in [3] and [8] with the experimental results from PCB-based detector [6] and those we got from the post-layout simulation of ASD. Due to the improvement of noise figure, the detection delay of the integrated ASD was slightly shorted comparing with the PCB-based detector.…”

Section: Post-layout Simulation and Validation Resultsmentioning

confidence: 99%

An integrated low-power asynchronous epileptic seizure detector

Mirzaei

Salam

Nguyen

2012

2012 IEEE Biomedical Circuits and Systems Conference (BioCAS)

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In this paper, we present a new asynchronous seizure detector that is part of an implantable integrated device intended to identify onset seizure and trigger focal treatment to block seizure progression. The proposed system eliminates the unnecessary clock gating during normal neural activity monitoring mode and reduces the total power consumption. The proposed detector includes analog and digital building blocks with a variable time frame and four concurrent variable voltage window detectors to extract seizure onset information. The algorithm is validated in Matlab and the system is implemented in standard 0.13µm CMOS process. Based on post-layout simulation results, the input referred noise of bioamplifier and the total power consumption of system are 4.4 µVrms and 7.1 µW, respectively. An accurate detection is achieved and no false alarms are recorded during analyzing the iEEG signals of two patients. The average detection delay of 9.5 sec is obtained after seizure onset.

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Section: Post-layout Simulation and Validation Resultsmentioning

confidence: 99%

An integrated low-power asynchronous epileptic seizure detector

Mirzaei

Salam

Nguyen

2012

2012 IEEE Biomedical Circuits and Systems Conference (BioCAS)

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“…Power consumption can be saved through the use of a power-efficient hardware computing platform. Chen et al (2011) carried out a realtime seizure detection system based on reduced instruction set computer (RISC) architecture. The measurement results show that the RISC architecture can reduce over 90% power consumption compared with its previous prototype, which was implemented on an enhanced 8051 microprocessor (Liang et al 2011).…”

Section: Discussionmentioning

confidence: 99%

“…The computational complexity of the LDA is relatively low and realizations in an embedded system have been presented (Hargrove et al 2010, Donohoo et al 2012. This algorithm is also successfully implemented on different processors for on-line detection of spontaneous absence seizures in animal models (Liang et al 2011 and in 24 h long-term uninterrupted EEG sequences (Chen et al 2011).…”

Section: Second Stage Of Seizure Confirmationmentioning

confidence: 99%

A hierarchical approach for online temporal lobe seizure detection in long-term intracranial EEG recordings

et al. 2013

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An on-line seizure detection method that can be applied to monitor continuous iEEG signals of patients who suffered from TLE was developed. An IC selection strategy to automatically determine the most seizure-related IC for seizure detection was also proposed. The system has advantages of (1) high detection accuracy, (2) low false alarm, (3) short detection latency, and (4) energy-efficient design for hardware implementation.

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“…10 shows the work-division of the mixed-signal MCU [18] . Compared to the 32-bit processor-based design [15] , the processing time of entropy is reduced by 40 times. Furthermore, FFT computations can be decreased by 59.6 times.…”

Section: Proposed Circuits and Systemsmentioning

confidence: 99%

“…First, the entire system can be fitted in the compact fabric headband. Second, its weight is only 50.3 g. Third, through the energy-efficient hardware accelerators and the low computational complexity classifier, the processing time and energy is reduced by 32.8 times and 8.5 times compared to the 32-bit processor-based design [15] . Last but not least, an APP for this smart headband is integrated with cloud to record patients’ health states.…”

Section: Introductionmentioning

confidence: 99%

An Ultra-Low Power Smart Headband for Real-Time Epileptic Seizure Detection

Lin

Istiqomah

Wang

et al. 2018

IEEE J. Transl. Eng. Health Med.

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In this paper, the design of a smart headband for epileptic seizure detection is presented. The proposed headband consists of four key components: 1) an analog front-end circuitry; 2) an epileptic seizure detection tag (ESDT); 3) a Bluetooth low-power chip; and 4) customized electrodes. All the above components are integrated into a fabric headband with only 50.3 g. The smart headband system dissipates 55.89 mW. The epileptic seizure detection algorithm inside ESDT is validated by using Boston Children’s Hospital’s CHB-MIT scalp EEG clinical database with the detection rate of 92.68% and the false alarm of 0.527/h. We develop a service APP connected to the cloud so that the patients’ health condition can be recorded and then referenced by doctors for further diagnosis or research.

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The Implementation of a Low-Power Biomedical Signal Processor for Real-Time Epileptic Seizure Detection on Absence Animal Models

Cited by 27 publications

References 27 publications

An integrated low-power asynchronous epileptic seizure detector

An integrated low-power asynchronous epileptic seizure detector

A hierarchical approach for online temporal lobe seizure detection in long-term intracranial EEG recordings

An Ultra-Low Power Smart Headband for Real-Time Epileptic Seizure Detection

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