dc.description.abstract | This study is aiming at accurate prediction of the rehabilitation outcome after stroke by using the pre-rehab electroencephalogram (EEG) and machine learning techniques and using new data to validate. 53 stroke patients were recruited for this study. 37 of them are used to build the prediction model and other 16 are used to validation. All patients underwent 24-hour rehab program and rehabilitation outcome was measured with FM, TEMP and WMFT. For supervised machine learning methods, we first divided the data into two groups : good and general recovery, according to two criteria - (1) Type I : a level of 10% improvement of any above mentioned measures (i.e ∆FM> =6.6 ; ∆WMFT >=8.5; ∆TEMPA >=13.8 ) after rehab was considered as good recovery as suggested in several studies, resulting in 20 good recovery patients (out of 37); and (2) Type II: the scale after rehab up to 10 % improvement from the before rehab scale of any above mentioned measures was labeled as good recovery, resulting in 27 good recovery patients (out of 37). The EEG data were acquired during the shoulder flexion/extension for eighty trials before the rehabilitation and were pre-processed offline for filtering and epoching. The spectral density from 4-48Hz at contralesional primary motor cortex (CM1), ipsilateral primary motor cortex (IM1), contralesional premotor area (CPM), ipsilateral premotor area (IPM) and the supplementary motor area (SMA) were obtained by projecting the EEG data to the chosen sources using the generalized inverse of the lead-field matrix over peri-stimulus time and then used a time-frequency Morlet wavelet transform (wavelet number: 7). The absolute value of the resulting time-frequency responses were averaged over trials and entered dynamic causal modeling for induced responses (DCM_IR) as the observations that the model is trying to explain. Both the spectral density at all sources and the parameters given by DCM were served as the data features. These features entered Wrapper method to select features, and the selected features went into the four different classifiers:SVM, Logistic Regression, NaiveBayes, J48 for two-class classification.
The classification result suggests that, the best accuracy rate was 0.9295 when using DCM features of β+γ frequencies of Type I data partition and Logistic Regression. And the result of construct validity is 0.8125. The accuracy of prediction with EEG data is much higher than prediction with other clinical factors. Furthermore, beta rhythm within the motor network and Logistic Regression have significant roles in motor recovery prediction. There are three factors related to the predictive accuracy. They are age, time post-stroke and lesion area of stroke. We believe that our findings in this study have great benefits on developing a knowledge-based and individual rehab program. | en_US |