This paper presents a unified approach to multiscale detection of transient evoked otoacoustic emissions (TEOAEs). Using statistical detection theory, it is shown that the optimal detector involves a time windowing operation where the window can be estimated from ensemble correlation information. The detector performs adaptive splitting of the signal into different frequency bands using either wavelet or wavelet packet decomposition. A simplified detector is proposed in which signal energy is omitted. The results show that the simplified detector performs significantly better than existing TEOAE detectors based on wave reproducibility or the modified variance ratio, whereas the detector involving signal energy does not offer such a performance advantage.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1109/TBME.2006.876626 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Multiscale Analysis of Alzheimer's Disease Using Feature Fusion in Cognitive and Sensory Brain Regions.
Digit Biomark
December 2024
Electrical and Computer Engineering, Western Michigan University, Kalamazoo, MI, USA.
Introduction: This research is focused on early detection of Alzheimer's disease (AD) using a multiscale feature fusion framework, combining biomarkers from memory, vision, and speech regions extracted from magnetic resonance imaging and positron emission tomography images.
Methods: Using 2D gray level co-occurrence matrix (2D-GLCM) texture features, volume, standardized uptake value ratios (SUVR), and obesity from different neuroimaging modalities, the study applies various classifiers, demonstrating a feature importance analysis in each region of interest. The research employs four classifiers, namely linear support vector machine, linear discriminant analysis, logistic regression (LR), and logistic regression with stochastic gradient descent (LRSGD) classifiers, to determine feature importance, leading to subsequent validation using a probabilistic neural network classifier.
Multi scale multi attention network for blood vessel segmentation in fundus images.
Sci Rep
January 2025
Department of Data Science and Artificial Intelligence, Sunway University, 47500, Petaling Jaya, Selangor Darul Ehsan, Malaysia.
Precise segmentation of retinal vasculature is crucial for the early detection, diagnosis, and treatment of vision-threatening ailments. However, this task is challenging due to limited contextual information, variations in vessel thicknesses, the complexity of vessel structures, and the potential for confusion with lesions. In this paper, we introduce a novel approach, the MSMA Net model, which overcomes these challenges by replacing traditional convolution blocks and skip connections with an improved multi-scale squeeze and excitation block (MSSE Block) and Bottleneck residual paths (B-Res paths) with spatial attention blocks (SAB).
View Article and Find Full Text PDFEnhanced ResNet-50 for garbage classification: Feature fusion and depth-separable convolutions.
PLoS One
January 2025
School of Business Economics, European Union University, Montreux, Switzerland.
As people's material living standards continue to improve, the types and quantities of household garbage they generate rapidly increase. Therefore, it is urgent to develop a reasonable and effective method for garbage classification. This is important for resource recycling and environmental improvement and contributes to the sustainable development of production and the economy.
View Article and Find Full Text PDFBackground: Tau protein accumulation is closely linked to synaptic and neuronal loss in Alzheimer's disease (AD), resulting in progressive cognitive decline. Although tau-PET imaging is a direct biomarker of tau pathology, it is costly, carries radiation risks, and is not widely accessible. Resting-state functional MRI (rs-fMRI) complexity-an entropy-based measure of BOLD signal variation-has been proposed as a non-invasive surrogate biomarker of early neuronal dysfunction associated with tau pathology.
View Article and Find Full Text PDFResearch of Orthodontic Soft Tissue Profile Prediction Based on Conditional Generative Adversarial Networks.
J Dent
January 2025
Department of Orthodontics, School of Stomatology, Capital Medical University, Beijing 100050, China. Electronic address:
Objective: This study constructed a new conditional generative adversarial network (CGAN) model to predict changes in lateral appearance following orthodontic treatment.
Methods: Lateral cephalometric radiographs of adult patients were obtained before (T1) and after (T2) orthodontic treatment. The expanded dataset was divided into training, validation, and test sets by random sampling in a ratio of 8:1:1.
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!