Real-life signals such as biomedical signals are non-stationary and random in their pattern, and cannot be characterized by any specific waveform or spectral content. Processing of these natural signals involves consideration of certain significant attributes such as their non-stationary behavior over time, scaling behavior, translation invariance. Due to their random behavior, the existing discriminative methods often fail to provide a reasonable quantification performance, thereby resulting in poor classification rates. In order to address this issue, there exists a need for defining a suitable theoretical framework for biomedical signals. We have proposed, a robust Time-Frequency Nonnegative Matrix Factorization (TF-NMF) framework that uses sparse representation for quantification of sleep signals. This scheme incorporates a novel feature extraction algorithm. For signals that are nonstationary in nature, the degree of sparsity is lower compared to the stationary signals. This results into poor classification accuracy. However our proposed approach has proven that using NMF as input to the sparse representation for classification will improve the discrimination performance. Overall, maximum cross-validation performance of 87:9% was obtained, using the leave-one-out (LOO) approach for sleep abnormality detection using EMG signals. Although the computational complexity of the proposed algorithm might be higher compared to the other similar methods, this TF-NMF based method shows great potential for quantification and localization of time varying signals.
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http://dx.doi.org/10.1109/EMBC.2013.6610501 | DOI Listing |
J Anat
December 2024
Department of Veterinary Anatomy, Physiology and Pathology, Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, UK.
Understanding normal structural and functional anatomy is critical for health professionals across various fields such as medicine, veterinary, and dental courses. The landscape of anatomical education has evolved tremendously due to several challenges and advancements in blended learning approaches, which have led to the adoption of the use of high-fidelity 3D digital models in anatomical education. Cost-effective methods such as photogrammetry, which creates digital 3D models from aligning 2D photographs, provide a viable alternative to expensive imaging techniques (i.
View Article and Find Full Text PDFProc Natl Acad Sci U S A
December 2024
Committee on Computational Neuroscience, Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL 60637.
Everything that the brain sees must first be encoded by the retina, which maintains a reliable representation of the visual world in many different, complex natural scenes while also adapting to stimulus changes. This study quantifies whether and how the brain selectively encodes stimulus features about scene identity in complex naturalistic environments. While a wealth of previous work has dug into the static and dynamic features of the population code in retinal ganglion cells (RGCs), less is known about how populations form both flexible and reliable encoding in natural moving scenes.
View Article and Find Full Text PDFHeliyon
August 2024
Department of Automation, Xiamen University, Xiamen, 361005, China.
In previous research, the prevailing assumption was that Graph Neural Networks (GNNs) precisely depicted the interconnections among nodes within the graph's architecture. Nonetheless, real-world graph datasets are often rife with noise, elements that can disseminate through the network and ultimately affect the outcome of the downstream tasks. Facing the complex fabric of real-world graphs and the myriad potential disturbances, we introduce the Sparse Graph Dynamic Attention Networks (SDGAT) in this research.
View Article and Find Full Text PDFSensors (Basel)
November 2024
College of Intelligent Transportation, Chongqing Vocational College of Public Transportation, Chongqing 402260, China.
Aiming at the problem that the existing human skeleton behavior recognition methods are insensitive to human local movements and show inaccurate recognition in distinguishing similar behaviors, a multi-scale spatio-temporal graph convolution method incorporating multi-granularity features is proposed for human behavior recognition. Firstly, a skeleton fine-grained partitioning strategy is proposed, which initializes the skeleton data into data streams of different granularities. An adaptive cross-scale feature fusion layer is designed using a normalized Gaussian function to perform feature fusion among different granularities, guiding the model to focus on discriminative feature representations among similar behaviors through fine-grained features.
View Article and Find Full Text PDFSensors (Basel)
November 2024
Center for Computing Research, Sandia National Labs, Albuquerque, NM 87123, USA.
Accurate self-motion estimation is critical for various navigational tasks in mobile robotics. Optic flow provides a means to estimate self-motion using a camera sensor and is particularly valuable in GPS- and radio-denied environments. The present study investigates the influence of different activation functions-ReLU, leaky ReLU, GELU, and Mish-on the accuracy, robustness, and encoding properties of convolutional neural networks (CNNs) and multi-layer perceptrons (MLPs) trained to estimate self-motion from optic flow.
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