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首页> 外文期刊>Biomedical signal processing and control >Implementation of wavelet packet transform and non linear analysis for emotion classification in stroke patient using brain signals
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Implementation of wavelet packet transform and non linear analysis for emotion classification in stroke patient using brain signals

机译：小波包变换和非线性分析在脑卒中患者情感分类中的应用

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Emotion perception in stroke patients is affected since there is abnormality in the brain. Here, researchers focused on the impact of left brain damage and right brain damage towards emotion recognition. Due to the impaired emotion recognition, it is a challenge for stroke patients to express themselves in daily communication. Hence, it is inspiring to see the possibility to predict patient's emotional state so as to prevent recurrent stroke. In this work, electroencephalograph (EEG) of 19 left brain damage patients (LBD), 19 right brain damage patients (RBD) and 19 normal control (NC) are collected as database. During data collection, six emotions (sad, disgust, fear, anger, happy and surprise) are induced by using audio visual stimuli. After normalization, EEG signals are filtered by using Butterworth 6th order band-pass filter at the cut-off frequencies of 0.5 Hz and 49 Hz. Then, wavelet packet transform (WPT) technique is implemented to localize five frequency bands: alpha (8 Hz-13 Hz), beta (13 Hz-30 Hz), gamma (30 Hz-49 Hz), alpha-to-gamma (8 Hz-49 Hz), beta-to-gamma (13 Hz-49 Hz). In WPT, four wavelet families are chosen: daubechies 4 (db4), daubechies 6 (db6), coiflet 5 (coif5) and symmlet 8 (sym8). Hurst exponents are extracted from each band and wavelet family and are classified by using K-nearest Neighbour (KNN) and Probabilistic Neural Network (PNN). Two classifications are done: comparison between three groups and comparison between six emotions. The results showed that all the H values are anti-correlated (0 < H < 0.5). From classification, the best frequency band is beta band, where sad emotion recorded the accuracy of 82.32% for LBD group. Meanwhile, both sad and fear emotion recorded 0.89 sensitivity score in LBD and RBD respectively. Due to its overall poor performance, RBD is found to have greater impairment in emotion recognition. (C) 2017 Elsevier Ltd. All rights reserved.
机译：由于脑部异常，中风患者的情感知觉会受到影响。在这里，研究人员集中于左脑损伤和右脑损伤对情绪识别的影响。由于情感识别受损，中风患者在日常交流中表达自己的能力是一项挑战。因此，令人鼓舞的是可以预测患者的情绪状态以防止中风复发。在这项工作中，收集了19例左脑损伤患者（LBD），19例右脑损伤患者（RBD）和19例正常对照（NC）的脑电图（EEG）作为数据库。在数据收集过程中，使用视听刺激会诱发六种情绪（悲伤，厌恶，恐惧，愤怒，快乐和惊奇）。归一化之后，通过使用Butterworth 6阶带通滤波器以0.5 Hz和49 Hz的截止频率对EEG信号进行滤波。然后，实施小波包变换（WPT）技术来定位五个频带：α（8 Hz-13 Hz），β（13 Hz-30 Hz），γ（30 Hz-49 Hz），α-γ（ 8 Hz-49 Hz），β-伽马（13 Hz-49 Hz）。在WPT中，选择了四个小波族：daubechies 4（db4），daubechies 6（db6），coiflet 5（coif5）和symmlet 8（sym8）。从每个频带和小波族中提取赫斯特指数，并使用K最近邻（KNN）和概率神经网络（PNN）对其进行分类。进行了两种分类：三组之间的比较和六种情绪之间的比较。结果表明，所有H值都是反相关的（0

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