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首页> 外文期刊>Clinical EEG and neuroscience: official journal of the EEG and Clinical Neuroscience Society (ENCS) >Automatic Seizure Detection Based on Morphological Features Using One-Dimensional Local Binary Pattern on Long-Term EEG
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Automatic Seizure Detection Based on Morphological Features Using One-Dimensional Local Binary Pattern on Long-Term EEG

机译：在长期脑电图中使用一维局部二元图案的形态特征自动癫痫发作检测

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摘要

Epileptic neurological disorder of the brain is widely diagnosed using the electroencephalography (EEG) technique. EEG signals are nonstationary in nature and show abnormal neural activity during the ictal period. Seizures can be identified by analyzing and obtaining features of EEG signal that can detect these abnormal activities. The present work proposes a novel morphological feature extraction technique based on the local binary pattern (LBP) operator. LBP provides a unique decimal value to a sample point by weighing the binary outcomes after thresholding the neighboring samples with the present sample point. These LBP values assist in capturing the rising and falling edges of the EEG signal, thus providing a morphologically featured discriminating pattern for epilepsy detection. In the present work, the variability in the LBP values is measured by calculating the sum of absolute difference of the consecutive LBP values. Interquartile range is calculated over the preprocessed EEG signal to provide dispersion measure in the signal. For classification purpose, K-nearest neighbor classifier is used, and the performance is evaluated on 896.9 hours of data from CHB-MIT continuous EEG database. Mean accuracy of 99.7% and mean specificity of 99.8% is obtained with average false detection rate of 0.47/h and sensitivity of 99.2% for 136 seizures.
机译：脑脑的癫痫症神经障碍广泛地使用脑电图（EEG）技术诊断。 EEG信号本质上是非营养的，并且在ICTAL期间显示出异常神经活动。可以通过分析和获取可以检测这些异常活动的EEG信号的特征来识别癫痫发作。本工作提出了一种基于局部二元图案（LBP）操作员的新型形态特征提取技术。通过在使用本样本点阈值下阈值之后称重二进制结果，LBP为样本点提供独特的十进制值。这些LBP值有助于捕获脑电图信号的上升沿和下降沿，从而提供用于癫痫检测的形态学鉴别的鉴别模式。在本作工作中，通过计算连续LBP值的绝对差之和来测量LBP值的可变性。在预处理的EEG信号上计算句子范围，以提供信号中的色散度量。对于分类目的，使用k最近邻分类，并且在CHB-MIT连续EEG数据库中的896.9小时的数据中评估性能。 99.7％的平均准确性为99.8％的平均特异性，平均假检出率为0.47 / h，灵敏度为99.2％，持续136次癫痫发作。

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