Nature and science of sleep · 2024
Research on Sleep Staging Based on Support Vector Machine and Extreme Gradient Boosting Algorithm.
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Wang Yiwen, Ye Shuming, Xu Zhi, Chu Yonghua, Zhang Jiarong, Yu Wenke
Аннотация
PURPOSE: To develop a sleep-staging algorithm based on support vector machine (SVM) and extreme gradient boosting model (XB Boost) and evaluate its performance.
METHODS: In this study, data features were extracted based on physiological significance, feature dimension reduction was performed through appropriate methods, and XG Boost classifier and SVM were used for classification. One hundred and twenty training sets and 80 test sets were randomly composed of the first 200 groups of data from the SHH1 database. The polysomnography (PSG) data of 20 real individuals in the clinic were selected as the experimental data. The C3 electroencephalogram (EEG), left and right electrooculogram (EOG), electromyogram (EMG), and other signals were analyzed. Finally, the stages were adjusted based on human sleep laws. The standard staging of the database and the doctor's diagnosis staging was used as the standard.
RESULTS: The SHHS1 database test results were as follows: the average accuracy was 83.24%, the precision and recall of Stage Wake and Stage 2 NREM sleep (N2) were over 80%, and the precision, F1-Score and recall of Stage 3 NREM sleep (N3) and Rapid Eye Movement (REM) were more than 70%. The clinical data test results were as follows: the average accuracy rate was 76.37%; for Wake and N3, the precision reached 85%; for Wake, N2, and REM, the recall rate reached over 70%; for Wake, the F-1 Score reached over 90%.
CONCLUSION: This study shows that the sleep staging results of the algorithm for the database and clinical data were similar. The staging results meet the requirements at the medical level.
METHODS: In this study, data features were extracted based on physiological significance, feature dimension reduction was performed through appropriate methods, and XG Boost classifier and SVM were used for classification. One hundred and twenty training sets and 80 test sets were randomly composed of the first 200 groups of data from the SHH1 database. The polysomnography (PSG) data of 20 real individuals in the clinic were selected as the experimental data. The C3 electroencephalogram (EEG), left and right electrooculogram (EOG), electromyogram (EMG), and other signals were analyzed. Finally, the stages were adjusted based on human sleep laws. The standard staging of the database and the doctor's diagnosis staging was used as the standard.
RESULTS: The SHHS1 database test results were as follows: the average accuracy was 83.24%, the precision and recall of Stage Wake and Stage 2 NREM sleep (N2) were over 80%, and the precision, F1-Score and recall of Stage 3 NREM sleep (N3) and Rapid Eye Movement (REM) were more than 70%. The clinical data test results were as follows: the average accuracy rate was 76.37%; for Wake and N3, the precision reached 85%; for Wake, N2, and REM, the recall rate reached over 70%; for Wake, the F-1 Score reached over 90%.
CONCLUSION: This study shows that the sleep staging results of the algorithm for the database and clinical data were similar. The staging results meet the requirements at the medical level.