Modulation Effect of Acupuncture on Functional Brain Networks and Classification of Its Manipulation With EEG Signals

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 27; no. 10; pp. 1973 - 1984
• Main Authors: Yu, Haitao, Li, Xiang, Lei, Xinyu, Wang, Jiang
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.10.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Acupuncture; Acupuncture manipulation; Artificial intelligence; Brain; Classification; Classifiers; EEG; Electrodes; Electroencephalography; Feature extraction; Frequency dependence; functional brain network; Graph theory; Herbal medicine; Learning algorithms; Locking; Machine learning; Multichannel communication; Needles; Support vector machines; Synchronism; Synchronization
• ISSN: 1534-4320; 1558-0210; 1558-0210
• DOI: 10.1109/TNSRE.2019.2939655

Acupuncture manipulation is the key of Chinese medicine acupuncture therapy. In clinical practice, different acupuncture manipulations are required to achieve different therapeutic effects, which means it is crucial to distinguish different acupuncture manipulations. In this paper, we proposed a classification framework for different acupuncture manipulations, which employed the graph theory and machine learning method. Multichannel EEG signals evoked by acupuncture at "Zusanli" acupoint were recorded from healthy humans by two acupuncture manipulations: twirling-rotating (TR) and lifting-thrusting (LT). Phase locking value was used to estimate the phase synchronization of pair-wise EEG channels. It was found that acupunctured by TR manipulation exhibit significantly higher synchronization degree than acupunctured by LT manipulation. With the construction of functional brain network, the topological features of graph theory were extracted. Taken the network features as inputs, machine learning classifiers were established to classify acupuncture manipulations. The highest accuracy can achieve 92.14% with support vector machine. By further optimizing the network features utilized in machine learning classifiers, it was found that the combination of node betweenness and small world network index is the most effective factor for acupuncture manipulations classification. These findings suggested that our approach provides new ideas for automatically identify acupuncture manipulations from the perspective of functional brain networks and machine learning methods.