Sharmila A, Geethanjali P
a School of Electrical Engineering , VIT University, Vellore, India.
J Med Eng Technol. 2018 Apr;42(3):217-227. doi: 10.1080/03091902.2018.1464075. Epub 2018 May 25.
Pattern recognition plays an important role in the detection of epileptic seizure from electroencephalogram (EEG) signals. In this pattern recognition study, the effect of filtering with the time domain (TD) features in the detection of epileptic signal has been studied using naive Bayes (NB) and supports vector machines (SVM). It is the first time the authors attempted to use TD features such as waveform length (WL), number of zero-crossings (ZC) and number of slope sign changes (SSC) derived from the filtered and unfiltered EEG data, and performance of these features is studied along with mean absolute value (MAV) which has been already attempted by the researchers. The other TD features which are attempted by the researchers such as standard deviation (SD) and average power (AVP) along with MAV are studied. A comparison is made in effect of filtering and without filtering for the University of Bonn database using NB and SVM for the TD features attempted first time along with MAV. The effect of individual and combined TD features is studied and the highest classification accuracy obtained in using direct TD features would be 99.87%, whereas it is 100% with filtered EEG data. The raw EEG data can be segmented and filtered using the fourth-order Butterworth band-pass filter.
模式识别在从脑电图（EEG）信号中检测癫痫发作方面发挥着重要作用。在这项模式识别研究中，使用朴素贝叶斯（NB）和支持向量机（SVM）研究了在癫痫信号检测中利用时域（TD）特征进行滤波的效果。这是作者首次尝试使用从滤波和未滤波的EEG数据中提取的TD特征，如波形长度（WL）、过零次数（ZC）和斜率符号变化次数（SSC），并结合研究人员已经尝试过的平均绝对值（MAV）来研究这些特征的性能。研究人员还尝试了其他TD特征，如标准差（SD）和平均功率（AVP）以及MAV。对于首次尝试的TD特征以及MAV，使用NB和SVM对波恩大学数据库在滤波和未滤波的情况下进行了效果比较。研究了单个和组合TD特征的效果，使用直接TD特征获得的最高分类准确率为99.87%，而使用滤波后的EEG数据时为100%。原始EEG数据可以使用四阶巴特沃斯带通滤波器进行分段和滤波。
