AI Article Synopsis
- Sleep quality is vital for health, and EEG signals help analyze sleep status for better medical guidance.
- The study introduces an artificial data generation method to enhance small real data sets, achieving a classification model with 92.85% accuracy.
- By combining augmented data with a public database and using EEGNet, the research overcomes challenges of subject-independent analysis, allowing effective use of limited labeled data for personalized sleep analysis.
Article Abstract
Sleep is an essential part of human life, and the quality of one's sleep is also an important indicator of one's health. Analyzing the Electroencephalogram (EEG) signals of a person during sleep makes it possible to understand the sleep status and give relevant rest or medical advice. In this paper, a decent amount of artificial data generated with a data augmentation method based on Discrete Cosine Transform from a small amount of real experimental data of a specific individual is introduced. A classification model with an accuracy of 92.85% has been obtained. By mixing the data augmentation with the public database and training with the EEGNet, we obtained a classification model with significantly higher accuracy for the specific individual. The experiments have demonstrated that we can circumvent the subject-independent problem in sleep EEG in this way and use only a small amount of labeled data to customize a dedicated classification model with high accuracy.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11297866 | PMC |
| http://dx.doi.org/10.1007/s11571-023-10062-0 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Artificial Intelligence for Optical Coherence Tomography in Glaucoma.
Transl Vis Sci Technol
January 2025
Glaucoma Service, Wills Eye Hospital, Philadelphia, PA, USA.
Purpose: The integration of artificial intelligence (AI), particularly deep learning (DL), with optical coherence tomography (OCT) offers significant opportunities in the diagnosis and management of glaucoma. This article explores the application of various DL models in enhancing OCT capabilities and addresses the challenges associated with their clinical implementation.
Methods: A review of articles utilizing DL models was conducted, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), autoencoders, and large language models (LLMs).
Forensic sex classification by convolutional neural network approach by VGG16 model: accuracy, precision and sensitivity.
Int J Legal Med
January 2025
Centro de Estatística e Aplicações Universidade de Lisbao, CEAUL, Faculdade de Ciências da Universidade de Lisboa no Bloco C6 - Piso 4, Lisboa, 1749-016, Portugal.
Introduction: In the reconstructive phase of medico-legal human identification, the sex estimation is crucial in the reconstruction of the biological profile and can be applied both in identifying victims of mass disasters and in the autopsy room. Due to the inherent subjectivity associated with traditional methods, artificial intelligence, specifically, convolutional neural networks (CNN) may present a competitive option.
Objectives: This study evaluates the reliability of VGG16 model as an accurate forensic sex prediction algorithm and its performance using orthopantomography (OPGs).
Apple Vision Pro-guided Laparoscopic Radio Frequency Ablation for Liver Tumors: The Pioneer Experience.
Surg Innov
January 2025
Department of Surgery, Show Chwan Memorial Hospital, Changhua, Taiwan.
This study evaluates the feasibility of Apple Vision Pro goggles as an augmented reality (AR) surgical navigation tool for laparoscopic-assisted ultrasound-guided radiofrequency ablation (RFA) of liver tumors. Traditional RFA is effective but challenging due to the integration of multiple imaging modalities. The primary aim of this research is to assess how Vision Pro goggles can enhance the surgical navigation process during RFA, improving tumor localization and the overall effectiveness of the procedure.
View Article and Find Full Text PDFA Microfabrication Technique for High-Performance Diffractive Optical Elements Tailored for Numerical Simulation.
Nanomaterials (Basel)
January 2025
Key Laboratory of All Optical Network and Advanced Telecommunication Network, Ministry of Education, Institute of Lightwave Technology, Beijing Jiaotong University, Beijing 100044, China.
Diffractive optical elements (DOEs) are specialized optical components that manipulate light through diffraction for various applications, including holography, spectroscopy, augmented reality (AR) and virtual reality (VR), and light detection and ranging (LiDAR). The performance of DOEs is highly determined by fabricated materials and fabrication methods, in addition to the numerical simulation design. This paper presents a microfabrication technique optimized for DOEs, enabling precise control of critical parameters, such as refractive index (RI) and thickness.
View Article and Find Full Text PDFA Local Adversarial Attack with a Maximum Aggregated Region Sparseness Strategy for 3D Objects.
J Imaging
January 2025
Department of Precision Instrument, Tsinghua University, Beijing 100084, China.
The increasing reliance on deep neural network-based object detection models in various applications has raised significant security concerns due to their vulnerability to adversarial attacks. In physical 3D environments, existing adversarial attacks that target object detection (3D-AE) face significant challenges. These attacks often require large and dispersed modifications to objects, making them easily noticeable and reducing their effectiveness in real-world scenarios.
View Article and Find Full Text PDFWant 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!