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- W2578391959 abstract "This project is about analyzing anatomical data obtained by pharyngometry with respect to obstructive sleep apnea (OSA) and the goal is to extract valued feature to discriminate OSA and non-OSA subjects. The key contributions of this thesis work are two-fold. First, we extract a rich set of 16 features from raw time series data and evaluate all them by ten-fold cross validation with different classifiers. We also did an extensive evaluation of the correlation of each feature with class labels based on t-test, Matthews Correlation Coefficient (MCC), Receiver Operating Characteristic (ROC), and Area Under the Curve (AUC). The experiment results show that several features such as the volume, volume variance, t-test selection feature and stretched length are very predictive. The accuracy of OSA and Non-OSA classification task can reach up to 77% with these features alone. The t-test conducted on these features show the significant correlation between the given features and the class labels. We also found an interesting result that on average OSA patients have smaller volume (stretch length) than non-OSA people. From medical point of view, this finding is consistent because OSA likely occurs when the muscles relax during sleep, causing soft tissue in the back of the throat to collapse and block the upper airway [11]. The second major contribution of this work is a method to search for local part of the time series from which the extracted features are most predictive. The main idea is based on an intuitive observation that large part of the time series which usually corresponds to normal part of the throat is not predictive. Therefore, using features extracted from the entire time series for classification is not effective because normal part of the throat contributes a big factor to the value of the features. In order to avoid this type of noise information we propose method to search for small part of time series which likely corresponds to abnormal part of the throat from which the extracted features could be more predictive. We did an exhaustive search for such predictive local parts. We found local parts from which the volume and stretch length were extracted and improved the classification accuracy from 65% to 75%. An interesting finding is that the most predictive local parts found by our algorithm are very close to the oropharyngeal junction point (OPJ), which has been already confirmed by some prior work in the literature [2] as an important point for discriminating OSA and non-OSA. They almost belong to the oropharynx region, which is consistent with the domain knowledge of OSA that the oropharynx region is the most possible obstructed site in the upper airway. Extracting Features to Discriminate OSA and non-OSA iii" @default.
- W2578391959 created "2017-01-26" @default.
- W2578391959 creator A5000426340 @default.
- W2578391959 date "2013-01-01" @default.
- W2578391959 modified "2023-09-25" @default.
- W2578391959 title "Extracting features to discriminate OSA and non-OSA" @default.
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