Automatic detection of A-phase onsets based on convolutional neural networks Article uri icon


  • The electroencephalogram (EEG) conveys information related to different sleep processes. One of these processes is the Cyclic Alternating Pattern (CAP), which is correlated with sleep instability. CAP is composed of A-phases, which are short recurrent modifications to the EEG fluctuations that characterize the sleep stages. A-phase annotation is performed by trained clinicians by visual EEG inspection, thus this is a weary and time-consuming task. A-phase annotation is a three step task: 1) localization, 2) delineation and 3) categorization. We propose to resolve the first step, to identify the A-phase location by training a deep convolutional neural network (CNN) based on the A-phase clinical description: an abrupt modification of the basal EEG fluctuations. Whole night EEG recordings of nine healthy subjects were used in this study. As first step, a CNN was trained and tested with the Leave-One-Out scheme in a balanced dataset of 4s EEG segments where an A-phase onset was or was not present. As a second step, the trained CNNs were used to identify A-phase onsets across the whole night recording. The results showed an accuracy performance of 93%25, sensitivity of 94%25 and specificity of 91%25 for the balanced set. On the whole recording, the performance was: F-score of 58%25, recall of 70%25 and precision of 49%25. In conclusion, we present a simple fully automatic method to localize the onset of A-phases in EEG signals. It is based on the spectral characteristics of the EEG signal which define the A-phases and could be part of more complex systems. © 2022 Elsevier Ltd

publication date

  • 2022-01-01