Detection of epileptic seizure in EEG signals using phase space reconstruction and euclidean distance of first-order derivative

机译：利用相空间重构和一阶导数的欧几里德距离检测脑电信号中的癫痫发作

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A most common disorder of human brain “Epilepsy”, which is a neurological disorder and is identified as unexpected and transient electrical disturbance of the brain. EEG is a widely used method of signal recording for detection of epileptic seizures. A modified method for classification of ictal (Epileptic seizure) and seizure-free EEG signals is proposed in this paper. The technique is employed for an epoch across the channel for feature extraction. The First order derivative (FOD) shows the rate of variability of the signal while the phase space reconstruction (PSR) shows the evolution of a system and the Euclidean distance measures the dispersion of the points in the 3-D PSR, these shows a better feature for ictal and normal EEG signals classification. The interquartile range of Euclidean distance has been used for feature selection due to its insensitivity towards the outlier. KNN classifier is used for classification of ictal and normal EEG signals. The methodology resulted detection of epileptic seizure in 0.1 second with the degree of performance, sensitivity-100%, Specificity-100%, and the Accuracy-100%.

机译：人脑最常见的疾病“癫痫病”是一种神经系统疾病，被确定为大脑的意外和暂时性电障碍。脑电图是检测癫痫发作的一种广泛使用的信号记录方法。本文提出了一种改进的分类方法，用于分类发作性（癫痫性发作）和无癫痫性脑电信号。该技术用于跨通道的时代以进行特征提取。一阶导数（FOD）表示信号的变化率，而相空间重构（PSR）表示系统的演化，而欧几里德距离衡量3-D PSR中点的离散度，这表明更好眼动和正常脑电信号分类的功能。欧几里德距离的四分位数范围已被用于特征选择，因为它对异常值不敏感。 KNN分类器用于对短暂和正常EEG信号进行分类。该方法可在0.1秒内检测出癫痫性癫痫发作的表现程度，灵敏度，灵敏度100％，特异性100％和准确性100％。

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来源
《2017 International Conference on Inventive Computing and Informatics》|2017年|821-826|共6页
会议地点 Coimbatore(IN)
作者
Salim Rukhsar; Haroon Rehman; Yusuf Uzzaman Khan; Ayesha Tooba Khan;
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作者单位

Department of Electrical Engineering, ZHCET Aligarh Muslim University Aligarh, U.P, India;

Department of Electrical Engineering, ZHCET Aligarh Muslim University Aligarh, U.P, India;

Department of Electrical Engineering, ZHCET, AMU, Aligarh, U.P, India;

Depar;

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正文语种 eng
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关键词
Electroencephalography; Feature extraction; Euclidean distance; Wavelet transforms; Epilepsy; Artificial neural networks;

机译：脑电图;特征提取;欧氏距离;小波变换;癫痫;人工神经网络;;

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专利

1. Identification of epileptic seizures in EEG signals using time-scale decomposition (ITD), discrete wavelet transform (DWT), phase space reconstruction (PSR) and neural networks [J] . Artificial Intelligence Review: An International Science and Engineering Journal . 2020,第4期

机译：使用时间尺度分解（ITD），离散小波变换（DWT），相空间重建（PSR）和神经网络识别EEG信号中的癫痫发作
2. Epileptic seizure prediction by the detection of seizure waveform from the pre-ictal phase of EEG signal [J] . Biomedical signal processing and control . 2020,第Mara期

机译：通过从脑电图的发作前阶段检测癫痫发作波形来预测癫痫发作
3. Multi-distance fluctuation based dispersion fractal for epileptic seizure detection in EEG signal [J] . Wijayanto Inung, Hartanto Rudy, Nugroho Hanung Adi Biomedical signal processing and control . 2021,第Auga期

机译：EEG信号中的癫痫癫痫检测多距离波动基于分数
4. Detection of Epileptic Seizure in EEG Signals Using Phase Space Reconstruction and Euclidean Distance of First-Order Derivative [C] . Salim Rukhsar, Haroon Rehman, Yusuf Uzzaman Khan, International Conference on Inventive Computing and Informatics . 2017

机译：使用相空间重建和一阶衍生的欧几里德距离检测脑电图信号中的癫痫癫痫发作
5. Detection of seizure onset in epileptic patients from intracranial EEG signals. [D] . Esteller, Rosana. 2000

机译：从颅内脑电图信号检测癫痫患者的癫痫发作。
6. Modeling and classification of gait patterns between anterior cruciate ligament deficient and intact knees based on phase space reconstruction Euclidean distance and neural networks [O] . Wenbao Wu, Wei Zeng, Limin Ma, 2018

机译：基于相空间重构欧氏距离和神经网络的前交叉韧带缺损和完整膝盖之间的步态模式建模和分类
7. Euclidean Distance Measure for the Electrocardiogram Fiducial Points Detection in the Phase-Space Formed with the Derivative Rule [O] . Matej Zajc, Emil Plesnik 2015

机译：欧几里德距离测量用于用衍生规则形成的相位空间中的心电图基准点检测

Detection of epileptic seizure in EEG signals using phase space reconstruction and euclidean distance of first-order derivative

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