Scalable selection of EEG features for compression

Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers Pub Date : 2020-09-10 DOI:10.1145/3410530.3414438

Yuma Tsurugasaki, Koichi Shimoda, Michael Hefenbrock, Akihito Taya, Sejun Song, Y. Tobe

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Abstract

Telemedicine using information technology (IT) and communication networks is becoming common. Often, the medical doctor and the patient can discuss the problem by video teleconference and, if necessary, the patient's physiological data can be sent to the doctor. As part of this trend, we believe that brain waves can be used for telemedicine in the future. We expect that the diagnosis of remote patients will be realized by transferring electroencephalogram (EEG) data to a server or cloud. However, if EEG data are sent as they are, the data size will be significantly large. Thus, the compression of EEG data is desirable. Furthermore, should not affect the accuracy of diagnosis if data compression is performed. In this study, the relationship between the selected EEG signal features and the accuracy is investigated.

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可扩展的EEG特征压缩选择

利用信息技术和通信网络的远程医疗正变得越来越普遍。通常，医生和病人可以通过视频电话会议讨论问题，必要时，病人的生理数据可以发送给医生。作为这一趋势的一部分，我们相信脑电波在未来可以用于远程医疗。我们期望通过将脑电图(EEG)数据传输到服务器或云来实现远程患者的诊断。但是，如果将EEG数据按原样发送，则数据量将非常大。因此，脑电图数据的压缩是必要的。此外，如果进行了数据压缩，则不应影响诊断的准确性。在本研究中，研究了所选择的脑电信号特征与准确率之间的关系。

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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Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers

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