myBrain: a novel EEG embedded system for epilepsy monitoring
myBrain: a novel EEG embedded system for epilepsy monitoring
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DOI：
10.1080/03091902.2017.1382585
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发表时间：
2017-01-01
影响因子：
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通讯作者：
Dias, Nuno
中科院分区：
文献类型：
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作者：
Pinho, Francisco;Cerqueira, Joao;Dias, Nuno
世界卫生组织指出，成功的医疗服务需要有效的医疗设备作为预防、诊断、治疗和康复的工具。一些研究已经得出结论，较长的监测周期和门诊设置可能会提高诊断的准确性和治疗选择的成功率。通过脑电图（EEG）对癫痫患者进行长期监测已被认为是改善此类患者诊断、疾病分类和治疗的有力工具。这项工作提出了一种无线和可穿戴的脑电图采集平台的发展，适用于住院和门诊环境中的长期和短期监测。开发的平台具有32个无源干电极，具有24位分辨率的模数信号转换和每个通道250Hz至1000Hz的可变采样频率，嵌入在独立模块中。模块上的计算机嵌入式系统运行Linux(R)操作系统，该操作系统控制两个软件框架之间的接口，两个软件框架相互作用以满足信号采集的实时性约束以及并行记录、处理和无线数据传输。开发了一种纺织结构来容纳所有组件。从硬件、软件和信号质量三个方面对平台性能进行了评估。通过电化学阻抗谱对电极进行了表征，并对运行癫痫识别算法的操作系统性能进行了评估。通过两种不同的方法对信号质量进行了全面评估：脑电图参考信号回放和临床级脑电图系统在α波替代和稳态视觉诱发电位范式中的基准测试。所提出的平台似乎可以有效地监测住院和门诊癫痫患者，并为新的门诊治疗方案和非临床脑电图应用铺平了道路。
The World Health Organisation has pointed that a successful health care delivery, requires effective medical devices as tools for prevention, diagnosis, treatment and rehabilitation. Several studies have concluded that longer monitoring periods and outpatient settings might increase diagnosis accuracy and success rate of treatment selection. The long-term monitoring of epileptic patients through electroencephalography (EEG) has been considered a powerful tool to improve the diagnosis, disease classification, and treatment of patients with such condition. This work presents the development of a wireless and wearable EEG acquisition platform suitable for both long-term and short-term monitoring in inpatient and outpatient settings. The developed platform features 32 passive dry electrodes, analogue-to-digital signal conversion with 24-bit resolution and a variable sampling frequency from 250Hz to 1000Hz per channel, embedded in a stand-alone module. A computer-on-module embedded system runs a Linux((R)) operating system that rules the interface between two software frameworks, which interact to satisfy the real-time constraints of signal acquisition as well as parallel recording, processing and wireless data transmission. A textile structure was developed to accommodate all components. Platform performance was evaluated in terms of hardware, software and signal quality. The electrodes were characterised through electrochemical impedance spectroscopy and the operating system performance running an epileptic discrimination algorithm was evaluated. Signal quality was thoroughly assessed in two different approaches: playback of EEG reference signals and benchmarking with a clinical-grade EEG system in alpha-wave replacement and steady-state visual evoked potential paradigms. The proposed platform seems to efficiently monitor epileptic patients in both inpatient and outpatient settings and paves the way to new ambulatory clinical regimens as well as non-clinical EEG applications.