应用四氢脑电波和光回归模型自动检测癫痫脑电图

2014 Annual IEEE India Conference (INDICON) Pub Date : 2014-12-01 DOI:10.1109/INDICON.2014.7030585

S. S. Khatavkar, J. Gawande

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引用次数: 1

摘要

在这项工作中，EEG信号被分解为五个子波段，即delta (0.8-4Hz)， theta (4-8Hz)， alpha (8-15Hz)， beta (15-30Hz)， gamma (30Hz以上)，使用三重半带滤波器组(THFB)。然后，计算每个子带的自回归(AR)模型。其次，估计各子带AR系数的功率谱密度(PSD)，用于正常和癫痫脑电的分类。观察到，使用THFB-AR建模方法进行分类比现有方法(近似熵)具有更好的分类精度。

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Automatic detection of epileptic EEG using THFB and auroregressive modeling

In this work, the EEG signal is decomposed into its five subbands viz. delta (0.8-4Hz), theta (4-8Hz), alpha (8-15Hz), beta (15-30Hz), gamma (above 30Hz) using Triplet Half-band Filter Bank (THFB). Then, the autoregressive (AR) model is computed for each subband. Next, power spectral density (PSD) of the AR coefficients of each subbands is estimated for classfication of normal and epileptic EEG. It is observed that classification performed using THFB-AR modeling method gives better classification accuracy than existing method (approximate entropy).

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2014 Annual IEEE India Conference (INDICON)

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