Automatic detection of A-phase onsets based on convolutional neural networks. (August 2022)

• Record Type: Journal Article
• Authors: Mendez, Martin O.; Arce-Santana, Edgar R.; Alba, Alfonso; Arce-Guevara, Valdemar; Murguía-Ibarra, José S.; Bianchi, Anna M.
• Is Part Of: Biomedical signal processing and control. Volume 77(2022)
• Publication Date: 2022-08
• Subjects: Convolutional neural networks -- Deep learning -- A-Phases -- Cyclic alternating pattern -- NREM sleep; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2022.103800
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: A-phase localization can be achieved based on abrupt EEG changes. Localization of the onset of A-phases could help to develop a fast screening of the sleep instability. Convolutional neural network (CNN) captures the EEG characteristics that identify the transitions and non-transitions induced by A-phases. Abstract: 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%, sensitivity of 94% and specificity of 91% for the balanced set. On the whole recording, the performance was: F-score of 58%, recall of 70% and precision of 49%. 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.