Artifact detection and localization in single-channel mobile EEG for sleep research using deep learning and attention mechanisms

• URL: http://arxiv.org/abs/2504.08469v2
• Date: Thu, 24 Apr 2025 13:33:33 GMT
• Title: Artifact detection and localization in single-channel mobile EEG for sleep research using deep learning and attention mechanisms
• Authors: Khrystyna Semkiv, Jia Zhang, Maria Laura Ferster, Walter Karlen,
• Abstract summary: Artifacts in the electroencephalogram (EEG) degrade signal quality and impact the analysis of brain activity.<n>Current methods for detecting artifacts in sleep EEG rely on simple threshold-based algorithms that require manual intervention.<n>We propose a convolutional neural network (CNN) model incorporating a convolutional block attention module (CNN-CBAM) to detect and identify the location of artifacts in the sleep EEG with attention maps.
• Score: 5.3125934435880895
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Artifacts in the electroencephalogram (EEG) degrade signal quality and impact the analysis of brain activity. Current methods for detecting artifacts in sleep EEG rely on simple threshold-based algorithms that require manual intervention, which is time-consuming and impractical due to the vast volume of data that novel mobile recording systems generate. We propose a convolutional neural network (CNN) model incorporating a convolutional block attention module (CNN-CBAM) to detect and identify the location of artifacts in the sleep EEG with attention maps. We benchmarked this model against six other machine learning and signal processing approaches. We trained/tuned all models on 72 manually annotated EEG recordings obtained during home-based monitoring from 18 healthy participants with a mean (SD) age of 68.05 y ($\pm$5.02). We tested them on 26 separate recordings from 6 healthy participants with a mean (SD) age of 68.33 y ($\pm$4.08), with contained artifacts in 4\% of epochs. CNN-CBAM achieved the highest area under the receiver operating characteristic curve (0.88), sensitivity (0.81), and specificity (0.86) when compared to the other approaches. The attention maps from CNN-CBAM localized artifacts within the epoch with a sensitivity of 0.71 and specificity of 0.67. This work demonstrates the feasibility of automating the detection and localization of artifacts in wearable sleep EEG.

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