Abstract
Continuous electroencephalographic monitoring of critically ill patients is an established procedure in intensive care units. Seizure detection algorithms, such as support vector machines (SVM), play a prominent role in this procedure. To correct for inter-human differences in EEG characteristics, as well as for intra-human EEG variability over time, dynamic EEG feature normalization is essential. Recently, the median decaying memory (MDM) approach was determined to be the best method of normalization. MDM uses a sliding baseline buffer of EEG epochs to calculate feature normalization constants. However, while this method does include non-seizure EEG epochs, it also includes EEG activity that can have a detrimental effect on the normalization and subsequent seizure detection performance. In this study, EEG data that is to be incorporated into the baseline buffer are automatically selected based on a novelty detection algorithm (Novelty-MDM). Performance of an SVM-based seizure detection framework is evaluated in 17 long-term ICU registrations using the area under the sensitivity-specificity ROC curve. This evaluation compares three different EEG normalization methods, namely a fixed baseline buffer (FB), the median decaying memory (MDM) approach, and our novelty median decaying memory (Novelty-MDM) method. It is demonstrated that MDM did not improve overall performance compared to FB (p <0.27), partly because seizure like episodes were included in the baseline. More importantly, Novelty-MDM significantly outperforms both FB (p = 0.015) and MDM (p = 0.0065).
Original language | English |
---|---|
Pages (from-to) | 1883-1892 |
Number of pages | 10 |
Journal | Medical & Biological Engineering & Computing |
Volume | 54 |
Issue number | 12 |
DOIs | |
Publication status | Published - Dec 2016 |
Keywords
- Seizure detection
- SVM
- PCA
- EEG
- Feature normalization
- NONCONVULSIVE STATUS EPILEPTICUS
- CRITICALLY-ILL
- CLASSIFICATION
- SYSTEM