Automatic A-Phase Detection of Cyclic Alternating Patterns in Sleep Using Dynamic Temporal Information

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 27; no. 9; pp. 1695 - 1703
• Main Authors: Hartmann, Simon, Baumert, Mathias
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.09.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adult; Algorithms; Automation; Classification; Cyclic alternating pattern (CAP); Databases, Factual; Datasets; Deep Learning; EEG; Electrocardiography; Electroencephalography; electroencephalography (EEG); Electroencephalography - methods; Feature extraction; Female; Humans; long short-term memory network (LSTM); Male; Neural networks; Neural Networks, Computer; Recurrent neural networks; Reproducibility of Results; Sensitivity; Sensitivity and Specificity; Signal processing; Sleep; Sleep - physiology; Sleep Stages - physiology
• DOI: 10.1109/TNSRE.2019.2934828
• ISSN: 1534-4320; 1558-0210

The identification of recurrent, transient perturbations in brain activity during sleep, so called cyclic alternating patterns (CAP), is of significant interest as they have been linked to neurological pathologies. CAP sequences comprise multiple, consecutive cycles of phasic activation (A-phases). Here, we propose a novel, automated system exploiting the dynamical, temporal information in electroencephalography (EEG) recordings for the classification of A-phases and their subtypes. Using recurrent neural networks (RNN), crucial information in the temporal behavior of the EEG is extracted. The automatic classification system is equipped to deal with the biasing issue of imbalanced data sets and uses state-of-the-art signal processing methods to reduce inter-subject variation. To evaluate our system, we applied recordings from the publicly available CAP Sleep Database on Physionet. Our results show that the RNN improved the detection accuracy by 3-5% and the F1-score by approximately 7% on two data sets compared to a normal feed-forward neural network. Our system achieves a sensitivity of approximately 76-78% and F1-score between 63-68%, significantly outperforming existing technologies. Moreover, its sensitivity for subtype classification of 60-63% (A1), 42-45% (A2), and 71-74% (A3) indicates superior multi-class classification performance for CAP detection. In conclusion, we have developed a fully automated high performance CAP scoring system that includes A-phase subtype classification. RNN classifiers yield a significant improvement in accuracy and sensitivity compared to previously proposed systems.