Classification of electroencephalograms before or after applying transcutaneous electrical nerve stimulation therapy using fractional empirical mode decomposition

IF 3 4区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Jiaqi Liu, Bingo Wing-Kuen Ling, Zhaoheng Zhou, Weirong Wu, Ruilin Li, Qing Liu
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引用次数: 0

Abstract

It is worth noting that applying the transcutaneous electrical nerve stimulation (TENS) therapy at the superficial nerve locations can modulate the brain activities. This paper aims to further investigate whether applying the TENS therapy at the superficial nerve locations can improve the attention of the subjects or not when the subjects are playing the mathematical game or reading a technical paper. First, the electroencephalograms (EEGs) are acquired before and after the TENS therapy is applied at the superficial nerve locations. Then, both the EEGs acquired before and after applying the TENS therapy are mixed together. Next, the preprocessing is applied to these acquired EEGs. Second, the fractional empirical mode decomposition (FEMD) is employed for extracting the features. Subsequently, the genetic algorithm (GA) is employed for performing the feature selection to obtain the optimal features. Finally, the support vector machine (SVM) and the random forest (RF) are used to classify whether the EEGs are acquired before or after the TENS therapy is applied. Since the higher classification accuracy refers to the larger difference of the EEGs acquired before and after the TENS therapy is applied, the classification accuracy reflects the effectiveness of applying the TENS therapy for improving the attention of the subjects. It is found that the percentages of the classification accuracies based on the EEGs acquired via the one channel device during playing the online mathematical game via the SVM and the RF by our proposed method are between 78.90% and 98.31% as well as between 78.44% and 100%, respectively. The percentages of the classification accuracies based on the EEGs acquired via the eight channel device during playing the online mathematical game via the SVM and the RF by our proposed method are between 80.84% and 93.63% as well as between 86.83% and 99.09%, respectively. the percentages of the classification accuracies based on the EEGs acquired via the one channel device during reading a technical paper via the SVM and the RF by our proposed method are between 77.67% and 83.67% as well as between 79.61% and 84.69%, respectively. the percentages of the classification accuracies based on the EEGs acquired via the sixteen channel device during reading a technical paper via the SVM and the RF by our proposed method are between 82.30% and 90.02% as well as between 91.72% and 95.91%, respectively. As our proposed method yields a higher classification accuracy than the states of the arts methods, this demonstrates the potential of using our proposed method as a tool for the medical officers to perform the precise clinical diagnoses and make the therapeutic decisions based on TENS.

Abstract Image

利用分数经验模式分解法对经皮神经电刺激疗法前后的脑电图进行分类
值得注意的是,在浅表神经位置应用经皮神经电刺激疗法(TENS)可以调节大脑活动。本文旨在进一步研究在受试者玩数学游戏或阅读科技论文时,在浅表神经位置施加经皮神经电刺激疗法是否能提高受试者的注意力。首先,在浅表神经位置进行 TENS 治疗前后采集脑电图(EEG)。然后,将 TENS 治疗前后采集的脑电图混合在一起。然后,对这些获取的脑电图进行预处理。其次,采用分数经验模式分解(FEMD)提取特征。然后,采用遗传算法(GA)进行特征选择,以获得最佳特征。最后,利用支持向量机(SVM)和随机森林(RF)对脑电图进行分类,以确定脑电图是在 TENS 治疗之前还是之后采集的。由于分类准确率越高,说明应用 TENS 治疗前后获得的脑电图差异越大,因此分类准确率反映了应用 TENS 治疗改善受试者注意力的效果。研究发现,我们提出的方法通过 SVM 和 RF 对单通道设备获取的玩在线数学游戏时的脑电图进行分类,分类准确率分别为 78.90% 至 98.31%,以及 78.44% 至 100%。在玩在线数学游戏时通过八通道设备获取的脑电图,通过 SVM 和射频(RF)方法进行的分类准确率分别在 80.84% 和 93.63% 之间,以及 86.83% 和 99.09% 之间。在阅读技术论文时通过一通道设备获取的脑电图,通过 SVM 和射频(RF)方法进行的分类准确率分别在 77.67% 和 83.67% 之间,以及 86.83% 和 99.09% 之间。通过 SVM 和射频,我们提出的方法对阅读技术论文时通过 16 通道设备获取的脑电图进行分类,分类准确率分别为 82.30% 到 90.02%,以及 91.72% 到 95.91%。由于我们提出的方法比现有方法具有更高的分类准确性,这表明我们提出的方法具有潜力,可作为医务人员根据 TENS 进行精确临床诊断和做出治疗决策的工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Multimedia Tools and Applications
Multimedia Tools and Applications 工程技术-工程:电子与电气
CiteScore
7.20
自引率
16.70%
发文量
2439
审稿时长
9.2 months
期刊介绍: Multimedia Tools and Applications publishes original research articles on multimedia development and system support tools as well as case studies of multimedia applications. It also features experimental and survey articles. The journal is intended for academics, practitioners, scientists and engineers who are involved in multimedia system research, design and applications. All papers are peer reviewed. Specific areas of interest include: - Multimedia Tools: - Multimedia Applications: - Prototype multimedia systems and platforms
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