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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Yu Wang Weiting Chen Kai Huang Qiufang Gu |
| Copyright Year | 2013 |
| Description | Author affiliation: Children's Hosp. of Fudan Univ., Shanghai, China (Qiufang Gu) || Software Eng. Inst., East China Normal Univ., Shanghai, China (Yu Wang; Weiting Chen; Kai Huang) |
| Abstract | Amplitude integrated electroencephalogram (aEEG), a cerebral function monitoring method, is widely used in response to the clinical needs for continuous EEG monitoring. The focus work of this paper is presenting a novel combined feature set of aEEG and applying random forest (RF) method to identify the normal and abnormal aEEG tracing. To that end, a complete experimental evaluation was conducted on 282 aEEG tracing cases (209 normal and 73 abnormal infants). Instead of the traditional aEEG signal processing and diagnosing methods only based on linear features, we considered both statistical and non-linear features. In our experiments, we extracted and combined different types of features for integrated and segmented signals. The experiments examined the RF algorithmic issues including parameter optimization, segmentation of data and imbalanced datasets processing. The performance of the RF was compared to five commonly used classifiers. The result shows that classification accuracy of our method is up to 91.46%. This also indicates our combined feature set is effective for aEEG classification. Besides, the RF-based method can reach exceptional specificity. This novel method to automatically detect aEEG could help medical staff to monitor the progress of infants at all times. |
| Sponsorship | IEEE Comput.Soc. |
| Starting Page | 285 |
| Ending Page | 290 |
| File Size | 186094 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781479913091 |
| DOI | 10.1109/BIBM.2013.6732504 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2013-12-18 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Radio frequency Pediatrics Sensitivity Amplitude-integrated electroencephalogram Vegetation Combined features Feature extraction Electroencephalography Random forest Monitoring |
| Content Type | Text |
| Resource Type | Article |
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