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

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)....