| Summary: | 碩士 === 國立成功大學 === 電機工程學系碩博士班 === 101 === Abstract
An automatic evaluation system of IADL (Instrumental Activities of Daily Living) is proposed, the system separates the total IADL scores into three categories: high (disability-free, IADL scores from 16 to 24 points), medium (mild disability, IADL scores from 8 to 15 points) and low (severe disability, IADL scores from 0 to 7 points). Single channel EEG device is applied to thirty seniors (from age 70 to 96) of the IADL scores uniform distribution to do the following IADL scenarios: (1) telephone using, (2) financial management. The brainwave data of chatting scenario is collected additionally and 5 features to classify the group of IADL scores are used as follows: (1) Average Amplitude (2) Power Ratio (3) Spectral Centroid (4) Spectral Edge Frequency 25% (5) Spectral Edge Frequency 50%. Besides, LDA (Linear Discriminant Analysis) algorithm is combined with 5 features mentioned above to evaluate IADL score. To find out the best classifier of IADL assessment, not only LDA classifier but SVM (Support Vector Machine) and KNN (K-th Nearest Neighbor) are used to compare the accuracy of IADL evaluation, and LOOCV (Leave-One-Out Cross-Validation) is used to verify the proposed system. Finally, the accuracy is about 90% and also higher than the other two classifiers when using LDA under the same feature. The groups of IADL scores of patients are classified exactly when using the brainwave data of chatting scenario.
The proposed system can help doctors to evaluate the results objectively before proceed the IADL interview to patients and combine with the judgment of doctors, the objective and accurate IADL scores are obtained.
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