TY - GEN
T1 - The EEG Analysis of Actual Left/Right Lateral Bending Movements in Patient of Lumbar Disc Herniation
AU - Li, Huihui
AU - Du, Wenjing
AU - Ivanov, Kamen
AU - Yang, Yuchao
AU - Zhan, Yang
AU - Wang, Lei
N1 - Funding Information:
This work was supported in part by National Natural Science Foundation of China (Grant Nos. 61401454, 71532014 and U1505251), the Key Research Program of the Chinese Academy of Sciences (KFZO-SW-202).
Publisher Copyright:
© 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - The purpose of this study was to investigate surface EEG of two groups (lumbar disc herniation (LDH) patients and healthy controls (HC)) when they performed real smooth movements (left/right lateral bending) with the maximum voluntary movement without pain. Thirty female LDH patients and thirty healthy controls volunteered to participate in the experiment. We also tested a healthy participant's motion imaginery (MI) of left/right lateral bending movement for over 200 times. We used Daubechies 4 (db4) wavelet to decompose EEG signal and we extracted δ, θ, α and β rhythms of the EEG signal. Wavelet entropy and sample entropy (SampEn) of four frequency bands were calculated. The results showed that there was significant difference of wavelet entropy EEG in T7, O2, and AF4 channels between the LDH and the healthy group when they did real left lateral bending. The topographic map also showed that SampEn value of four rhythms of the MI right lateral bending were significantly less than the values of the MI left lateral motion in healthy participant. Classification and Regression Trees (CART), Logistic regression (LR), Support Vector Machine (SVM), K-nearest neighbors (KNN), and the Linear discrimination analysis (LDA) classifiers showed averaged accuracies more than 96% for MI of left/right lateral bending.
AB - The purpose of this study was to investigate surface EEG of two groups (lumbar disc herniation (LDH) patients and healthy controls (HC)) when they performed real smooth movements (left/right lateral bending) with the maximum voluntary movement without pain. Thirty female LDH patients and thirty healthy controls volunteered to participate in the experiment. We also tested a healthy participant's motion imaginery (MI) of left/right lateral bending movement for over 200 times. We used Daubechies 4 (db4) wavelet to decompose EEG signal and we extracted δ, θ, α and β rhythms of the EEG signal. Wavelet entropy and sample entropy (SampEn) of four frequency bands were calculated. The results showed that there was significant difference of wavelet entropy EEG in T7, O2, and AF4 channels between the LDH and the healthy group when they did real left lateral bending. The topographic map also showed that SampEn value of four rhythms of the MI right lateral bending were significantly less than the values of the MI left lateral motion in healthy participant. Classification and Regression Trees (CART), Logistic regression (LR), Support Vector Machine (SVM), K-nearest neighbors (KNN), and the Linear discrimination analysis (LDA) classifiers showed averaged accuracies more than 96% for MI of left/right lateral bending.
UR - http://www.scopus.com/inward/record.url?scp=85077883560&partnerID=8YFLogxK
U2 - 10.1109/EMBC.2019.8857620
DO - 10.1109/EMBC.2019.8857620
M3 - Conference contribution
C2 - 31946913
AN - SCOPUS:85077883560
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 4707
EP - 4711
BT - 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
Y2 - 23 July 2019 through 27 July 2019
ER -
