Faculty of Engineering, Multimedia University, 63100, Cyberjaya, Selangor, Malaysia.
Adv Exp Med Biol. 2010;680:593-9. doi: 10.1007/978-1-4419-5913-3_65.
Electroencephalogram (EEG) serves as an extremely valuable tool for clinicians and researchers to study the activity of the brain in a non-invasive manner. It has long been used for the diagnosis of various central nervous system disorders like seizures, epilepsy, and brain damage and for categorizing sleep stages in patients. The artifacts caused by various factors such as Electrooculogram (EOG), eye blink, and Electromyogram (EMG) in EEG signal increases the difficulty in analyzing them. Discrete wavelet transform has been applied in this research for removing noise from the EEG signal. The effectiveness of the noise removal is quantitatively measured using Root Mean Square (RMS) Difference. This paper reports on the effectiveness of wavelet transform applied to the EEG signal as a means of removing noise to retrieve important information related to both healthy and epileptic patients. Wavelet-based noise removal on the EEG signal of both healthy and epileptic subjects was performed using four discrete wavelet functions. With the appropriate choice of the wavelet function (WF), it is possible to remove noise effectively to analyze EEG significantly. Result of this study shows that WF Daubechies 8 (db8) provides the best noise removal from the raw EEG signal of healthy patients, while WF orthogonal Meyer does the same for epileptic patients. This algorithm is intended for FPGA implementation of portable biomedical equipments to detect different brain state in different circumstances.
脑电图（EEG）是临床医生和研究人员研究大脑活动的一种非常有价值的非侵入性工具。它长期以来一直用于诊断各种中枢神经系统疾病，如癫痫发作、癫痫和脑损伤，并用于对患者的睡眠阶段进行分类。EEG 信号中由于眼电图（EOG）、眼动和肌电图（EMG）等各种因素引起的伪迹增加了分析它们的难度。离散小波变换已应用于这项研究中，用于从 EEG 信号中去除噪声。使用均方根（RMS）差来定量测量噪声去除的效果。本文报告了应用于 EEG 信号的小波变换作为去除噪声以检索与健康和癫痫患者相关的重要信息的方法的有效性。使用四种离散小波函数对健康和癫痫患者的 EEG 信号进行基于小波的噪声去除。通过适当选择小波函数（WF），可以有效地去除噪声，从而显著分析 EEG。这项研究的结果表明，WF Daubechies 8（db8）可以从健康患者的原始 EEG 信号中有效去除噪声，而 WF 正交 Meyer 则可以对癫痫患者进行相同的处理。该算法旨在用于 FPGA 实现便携式生物医学设备，以在不同情况下检测不同的大脑状态。
