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| Screening best combination of features based on adaptive vector machine for detecting sleep apnea syndrome |
| WANG Xinkang1 LIU Lei2 WANG Lianghong2 FAN Minghui2 |
1.Department of ECG Diagnosis, Fujian Provincial Hospital, Fujian Province, Fuzhou 350001, China;
2.College of Physics and Information Engineering, Fuzhou University, Fujian Province, Fuzhou 350108, China |
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Abstract There are so much characteristic parameters can be extracted based on adaptive vector machine for detecting sleep apnea syndrome. It has important significance which is selected the characteristic parameters to reduce the amounts of calculation applied in sleep apnea syndrome. This study adopted the electrocardiogram signals from limb guided lead-Ⅱ and then denoised the signal interference and detected the R-wave to get the heart rate variability data and ECG-derived respiratory data. Analysis these two data that we can obtain the twenty-two features in time domain and frequency domain, moreover, using support vector machines algorithm to classify the sleep apnea syndrome feature parameters. Compared the twenty-two features with optimal fifteen feature parameters we proposed, the amounts of calculation are decrease obviously without decay the classification accuracy. It can be used as an extension of clinical long time electrocardiogram detection because it can reduce the dependence on health care professional. Therefore, it has good economy and popularity.
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