Subject-level Normalization to Improve A-phase Detection of Cyclic Alternating Pattern in Sleep EEG.

Automatic detection systems for activation phases (A-phase) of the cyclic alternating pattern (CAP) in electroencephalograms (EEG) are designed to automatically score A-phases in any individual but typically fail to factor in EEG signal variations between individuals, e.g. due to sleep disorders, recording site differences or equipment differences. Here, we investigate the effect of subject-level normalization on the performance of an automatic A-phase detection system consisting of a recurrent neural network. We compared the classification performance of various subject-level normalization methods to the standard training set normalization. Systems were trained and tested on subjects with different sleep disorders using the publicly available CAP Sleep Database on Physionet. Subject-level normalization using Zscore or median and interquartile range (IQR) increases the F-score for A1-phases by +11-22% (Z-Score: +11-20%, Median/IQR: +16-22%), for A2-phases by +2-9% (Z-Score: +59%, Median/IQR: +2-7%), for A3-phases by -1 - +8% (Z-Score: +3-8%, Median/IQR: -1-+5%) as compared to the standard training data normalization when tested across sleep disorders. Our results show that subject-level normalization drastically improves the precision of A-phase detection in case the training population differs from the testing population.Clinical Relevance- Subject-level normalisation improves the automatic CAP scoring system performances for the general population by minimizing the effect of individual EEG differences.