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A novel Capsule Neural Network based model for drowsiness detection using electroencephalography signals.
- Source :
-
Expert Systems with Applications . Sep2022, Vol. 201, pN.PAG-N.PAG. 1p. - Publication Year :
- 2022
-
Abstract
- The early detection of drowsiness has become vital to ensure the correct and safe development of several industries' tasks. Due to the transient mental state of a human subject between alertness and drowsiness, automated drowsiness detection is a complex problem to tackle. The electroencephalography signals allow us to record variations in an individual's brain's electrical potential, where each of them gives specific information about a subject's mental state. However, due to this type of signal's nature, its acquisition, in general, is complex, so it is hard to have a large volume of data to apply techniques of Deep Learning for processing and classification optimally. Nevertheless, Capsule Neural Networks are a brand-new Deep Learning algorithm proposed for work with reduced amounts of data. It is a robust algorithm to handle the data's hierarchical relationships, which is an essential characteristic for work with biomedical signals. Therefore, this paper presents a Deep Learning-based method for drowsiness detection with CapsNet by using a concatenation of spectrogram images of the electroencephalography signals channels. The proposed CapsNet model is compared with a Convolutional Neural Network, which is outperformed by the proposed model, which obtains an average accuracy of 86,44 % and 87,57% of sensitivity against an average accuracy of 75,86% and 79,47% sensitivity for the CNN, showing that CapsNet is more suitable for this kind of datasets and tasks. • A Capsule Network for drowsiness detection method is proposed. • The system is based on spectrogram images concatenate information from Fz and Pz. • The traditional drowsiness detection methods need a big number of data. • The proposed method improves the results even with a small number of images. • The proposed method outperforms state of the art in comparison with CNN and ML. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09574174
- Volume :
- 201
- Database :
- Academic Search Index
- Journal :
- Expert Systems with Applications
- Publication Type :
- Academic Journal
- Accession number :
- 156780029
- Full Text :
- https://doi.org/10.1016/j.eswa.2022.116977