| 摘要(英) |
Dystonia is a spontaneous neurological movement disorder, which refers to a group of muscles with involuntary excessive contraction. Its basic feature is excessive muscle activity required for exercise. In clinical terms, the main pathogenesis of patients with acquired dystonia is not fully understood, so the diagnosis can only rely on the experience of the physician to diagnose. Therefore, the purpose of this research is to build an objective computer diagnosis system for task-specific focal hand dystonia by machine learning.
In this study, 12 patients with Writer′s Cramp and 14 normal subjects were collected. They performed a wrist extension of 8 seconds for 15 minutes, and collect electroencephalography (EEG) signals at this time. Use dynamic causal modeling (DCM) to analyze the connection between patients and normal people′s motor neural network, and use the method of supervised machine learning to analyze the connection between patients and normal people′s motor neural network and find out the differences, establish a computer diagnosis system.
The results of the study showed that before the start of exercise, the power of the Theta band on the five brain regions (LM1, LPM, SMA, RM1, RPM) and the Alpha band on the three brain regions (LPM, RM1, RPM) were significantly less than normal peoele. After the start of exercise, the power of the Theta band on the four brain regions (LM1, LPM, RM1, RPM) and the Beta band on the two brain regions (RM1, RPM) were significantly greater than normal people. In the part of brain functional connections, our research results found that there are 3 abnormal connections in patients, of which Two of them are connection with reduced excitement , and one is a connection with a changed connection mechanism. The strength of these three connections is lower than normal people, which means that the increase of connection inhibition is very important for patients to perform specific tasks. Among them, there are two abnormal connections in the supplementary motor area as more important brain areas. In the part of the supporting diagnosis system, we discussed the impact of the change of Theta frequency band on the diagnosis system. We changed the Theta frequency band from 4-8Hz to Low Frequency (4-12Hz). It can be seen that in the case of 4-8Hz, the classification method is SVM and naivebayes algorithm can get higher accuracy, which are 89.4% and 89.8%, respectively. If the frequency band is changed to Low Frequency (4-12Hz), the algorithms with higher accuracy are SVM, Logistic regression, and naivebayes. The accuracy is 90.99%, 90.42%, and 90.55%. In terms of frequency band combination,we can see that in algorithms with higher accuracy, Theta band is included in the best combination, indicating that in our supporting diagnosis system, the Theta band is more able to distinguish between patients and normal people. The important frequency band of the difference. In addition, according to the important characteristic connections analyzed by our supporting diagnostic system, we also found that the connections related to the premotor cortex area and supplementary motor cortex area on the affected side are indeed considered to be used to determine whether it is an important brain area of the patient or normal, Roughly consistent with the results we have calculated. In the future, we hope that the number of subjects we receive can be greater to avoid the phenomenon of over fitting, which will affect the accuracy of auxiliary diagnosis. In the future, more different algorithms can be added to test which algorithm is more suitable for our research.
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