基于声镊子的细胞力学特性表征的理论和实验研究
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1.A Real-Time Impedance Measurement System for EEG Based on Embedded System
- 关键词：
- Analog to digital conversion;Embedded systems;Electrodes;Signal processing;Electric impedance measurement;A/D conversion;Alternating current;Current sources;Electro-encephalogram (EEG);Impedance measurement;Measurement accuracy;Real time performance;Real-time impedance measurements
- Shen, Peng;Liu, Yunqing;Xiong, Wenqiang;He, Aijun;Zhang, Mengya
- 《13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2020》
- 2020年
- October 17, 2020 - October 19, 2020
- Virtual, Chengdu, China
- 会议
The contact between the electrodes in the electroencephalogram (EEG) machine and the scalp has a great influence on the quality of the EEG. In order to guarantee the quality of the collected brain waves, we designed an embedded real-time impedance measurement system for the EEG. The system uses the ADS1299 chip, which depends on the on-chip current source to generate an alternating current which is higher than the frequency of the EEG signal as the output signal for impedance measurement. After A/D conversion, the obtained signals contain both EEG signals and impedance measurement signals. Furthermore, the system uses stm32f723 as the main control chip to design a filter to separate the two kinds of signals, and calculate the impedance value between the electrode and scalp in real time. In this system, the EEG signal and the impedance value data are stored in the SD card in BDF format at the same time, which provides the basis for judging the effective EEG data in the later signal processing. At the same time, the impedance value is compared with the preset threshold, and the LED is used to indicate whether the scalp is in good contact. Based on stm32F723 embedded system, the whole system has high integration, small volume, high measurement accuracy and strong real-time performance, which is of great significance for obtaining high-quality EEG data. © 2020 IEEE.
...2.A Portable EEG Monitoring System for Neonatal Seizures
- 关键词：
- Monitoring;Machine learning;Wireless local area networks (WLAN);Bedside monitoring;Clinical detection;Electro-encephalogram (EEG);Feature representation;Hardware circuits;Low-power consumption;Monitoring system;Neonatal seizure
- Qu, Xiaoli;Liu, Yunqing;Shen, Peng;He, Aijun;Zhang, Ying
- 《13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2020》
- 2020年
- October 17, 2020 - October 19, 2020
- Virtual, Chengdu, China
- 会议
This paper introduces a portable electroencephalogram (EEG) monitoring system for neonatal seizures. The hardware circuit can acquire 8 channels of EEG data or expand to 32 channels. It is characterized in high accuracy, small size, and low power consumption, which can meet the needs of long-term bedside monitoring of newborns. Users can choose different modes to view the historical data of the acquired EEG signals, or real-time data transmitted via WiFi. To further analyze and process the data, this paper introduces a machine learning-based scheme to automatically distinguish between normal and abnormal amplitude-integrated EEG (aEEG) signals. The program extracts the effective feature representation from the data and trains the model. The model can predict the class of unknown aEEG samples, which is helpful for further clinical detection of neonatal brain conditions. © 2020 IEEE.
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