Recognizing seizure using Poincaré plot of EEG signals and graphical features in DWT domain.

IF 1.5 4区 医学 Q2 MEDICINE, GENERAL & INTERNAL
Hesam Akbari, Muhammad Tariq Sadiq, Nastaran Jafari, Jingwei Too, Nasser Mikaeilvand, Antonio Cicone, Stefano Serra-Capizzano
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引用次数: 8

Abstract

Electroencephalography (EEG) signals are considered one of the oldest techniques for detecting disorders in medical signal processing. However, brain complexity and the non-stationary nature of EEG signals represent a challenge when applying this technique. The current paper proposes new geometrical features for classification of seizure (S) and seizure-free (SF) EEG signals with respect to the Poincaré pattern of discrete wavelet transform (DWT) coefficients. DWT decomposes EEG signal to four levels, and thus Poincaré plot is shown for coefficients. Due to patterns of the Poincaré plot, novel geometrical features are computed from EEG signals. The computed features are involved in standard descriptors of 2‑D projection (STD), summation of triangle area using consecutive points (STA), as well as summation of shortest distance from each point relative to the 45-degree line (SSHD), and summation of distance from each point relative to the coordinate center (SDTC). The proposed procedure leads to discriminate features between S and SF EEG signals. Thereafter, a binary particle swarm optimization (BPSO) is developed as an appropriate technique for feature selection. Finally, k-nearest neighbor (KNN) and support vector machine (SVM) classifiers are used for classifying features in S and SF groups. By developing the proposed method, we have archived classification accuracy of 99.3 % with respect to the proposed geometrical features. Accordingly, S and SF EEG signals have been classified. Also, Poincaré plot of SF EEG signals has more regular geometrical shapes as compared to S group. As a final remark, we notice that the Poincaré plot of coefficients in S EEG signals has occupied more space as compared to SF EEG signals (Tab. 3, Fig. 11, Ref. 57). Text in PDF www.elis.sk Keywords: EEG signal, DWT, Poincaré plot, geometrical feature, BPSO, SVM, KNN.

利用脑电信号的poincarcars图和DWT域的图形特征识别癫痫发作。
脑电图(EEG)信号被认为是医学信号处理中最古老的检测疾病的技术之一。然而,大脑的复杂性和脑电图信号的非平稳性对该技术的应用提出了挑战。本文根据离散小波变换(DWT)系数的poincar模式,提出了一种新的用于癫痫发作(S)和无癫痫发作(SF)脑电信号分类的几何特征。小波变换(DWT)将脑电信号分解为4个层次,用poincarcarr图表示系数。由于庞卡罗图的模式,从脑电图信号中计算出新的几何特征。计算出的特征涉及到2 - D投影的标准描述符(STD)、使用连续点的三角形面积求和(STA)、每个点相对于45度线的最短距离求和(SSHD)和每个点相对于坐标中心的距离求和(SDTC)。该方法可以区分S型和SF型脑电信号的特征。在此基础上,提出了一种基于二元粒子群算法的特征选择方法。最后,使用k近邻(KNN)和支持向量机(SVM)分类器对S和SF组中的特征进行分类。通过开发所提出的方法,我们已经存档了99.3%的分类精度相对于所提出的几何特征。据此,对S和SF脑电信号进行了分类。与S组相比,SF组脑电信号的poincarcarr图具有更规则的几何形状。最后,我们注意到,与SF脑电信号相比,S脑电信号中的系数poincar图占据了更多的空间(表3,图11,参考文献57)。关键词:脑电信号,DWT, poincar图,几何特征,BPSO, SVM, KNN
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来源期刊
CiteScore
2.60
自引率
0.00%
发文量
185
审稿时长
3-8 weeks
期刊介绍: The international biomedical journal - Bratislava Medical Journal – Bratislavske lekarske listy (Bratisl Lek Listy/Bratisl Med J) publishes peer-reviewed articles on all aspects of biomedical sciences, including experimental investigations with clear clinical relevance, original clinical studies and review articles.
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