Convolutional neural networks (CNN) have demonstrated state-of-the-art classification results in image categorization, but have received comparatively little attention for classification of one-dimensional physiological signals. We design a deep CNN architecture for automated sleep stage classiffication of human sleep EEG and EOG signals. The CNN proposed in this paper amply outperforms recent work that uses a different CNN architecture over a single-EEG-channel version of the same dataset. We show that the performance gains achieved by our network rely mainly on network depth, and not on the use of several signal channels. Performance of our approach is on par with human expert inter-scorer agreement. By examining the internal activation levels of our CNN, we find that it spontaneously discovers signal features such as sleep spindles and slow waves that figure prominently in sleep stage categorization as performed by human experts.

Download full-text PDF

Source
http://dx.doi.org/10.1109/TCBB.2019.2912955DOI Listing

Publication Analysis

Top Keywords

cnn architecture
8
sleep stage
8
sleep
5
cnn
5
deep learning
4
learning automated
4
automated feature
4
feature discovery
4
discovery classification
4
classification sleep
4

Similar Publications

Gastrointestinal (GI) disease examination presents significant challenges to doctors due to the intricate structure of the human digestive system. Colonoscopy and wireless capsule endoscopy are the most commonly used tools for GI examination. However, the large amount of data generated by these technologies requires the expertise and intervention of doctors for disease identification, making manual analysis a very time-consuming task.

View Article and Find Full Text PDF

Multi-label segmentation of carpal bones in MRI using expansion transfer learning.

Phys Med Biol

January 2025

Department of Trauma and Reconstructive Surgery, BG Hospital Bergmanntrost, Merseburger Straße 165 06112 Halle, Halle, Sachsen-Anhalt, 06112, GERMANY.

The purpose of this study was to develop a robust deep learning approach trained with a small in-vivo MRI dataset for multi-label segmentation of all eight carpal bones for therapy planning and wrist dynamic analysis. Approach: A small dataset of 15 3.0-T MRI scans from five health subjects was employed within this study.

View Article and Find Full Text PDF

Three-dimensional convolutional neural network for leak detection and localization in smart water distribution systems.

Water Res X

December 2024

Professor, Department of Civil and Architectural Engineering and Mechanics, The University of Arizona, Tucson, AZ 85721, USA.

Smart meters such as advanced metering infrastructure (AMI) can significantly improve identifying realistic sized leaks in water distribution networks (WDNs). However, to date, detection/localization methods for AMI systems are extremely limited. In this study, to examine the benefits of using AMIs for leak detection within distribution network, a three-dimensional (3D) convolutional neural network (CNN) deep learning (DL) model is proposed that can account for temporally and spatially distributed information of pressures.

View Article and Find Full Text PDF

Facial expression recognition faces great challenges due to factors such as face similarity, image quality, and age variation. Although various existing end-to-end Convolutional Neural Network (CNN) architectures have achieved good classification results in facial expression recognition tasks, these network architectures share a common drawback that the convolutional kernel can only compute the correlation between elements of a localized region when extracting expression features from an image. This leads to difficulties for the network to explore the relationship between all the elements that make up a complete expression.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!