TY - GEN
T1 - Scalable selection of EEG features for compression
AU - Tsurugasaki, Yuma
AU - Shimoda, Koichi
AU - Hefenbrock, Michael
AU - Taya, Akihito
AU - Song, Sejun
AU - Tobe, Yoshito
N1 - Publisher Copyright:
© 2020 ACM.
PY - 2020/9/10
Y1 - 2020/9/10
N2 - 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.
AB - 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.
KW - electroencephalogram
KW - features
KW - machine learning
KW - telemedicine
UR - https://www.scopus.com/pages/publications/85091870473
UR - https://www.scopus.com/pages/publications/85091870473#tab=citedBy
U2 - 10.1145/3410530.3414438
DO - 10.1145/3410530.3414438
M3 - Conference contribution
AN - SCOPUS:85091870473
T3 - UbiComp/ISWC 2020 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
SP - 712
EP - 715
BT - UbiComp/ISWC 2020 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
PB - Association for Computing Machinery
T2 - 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and 2020 ACM International Symposium on Wearable Computers, UbiComp/ISWC 2020
Y2 - 12 September 2020 through 17 September 2020
ER -