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Spiking-PhysFormer: Camera-based remote photoplethysmography with parallel spike-driven transformer.
Neural Netw
January 2025
Tsinghua University, Beijing, China. Electronic address:
Artificial neural networks (ANNs) can help camera-based remote photoplethysmography (rPPG) in measuring cardiac activity and physiological signals from facial videos, such as pulse wave, heart rate and respiration rate with better accuracy. However, most existing ANN-based methods require substantial computing resources, which poses challenges for effective deployment on mobile devices. Spiking neural networks (SNNs), on the other hand, hold immense potential for energy-efficient deep learning owing to their binary and event-driven architecture.
View Article and Find Full Text PDFMulitDeepsurv: survival analysis of gastric cancer based on deep learning multimodal fusion models.
Biomed Opt Express
January 2025
Computer Engineering Department, Taiyuan Institute of Technology, Taiyuan 030008, China.
Gastric cancer is a leading cause of cancer-related deaths globally. As mortality rates continue to rise, predicting cancer survival using multimodal data-including histopathological images, genomic data, and clinical information-has become increasingly crucial. However, extracting effective predictive features from this complex data has posed challenges for survival analysis due to the high dimensionality and heterogeneity of histopathology images and genomic data.
View Article and Find Full Text PDFAtmospheric turbulence is one of the key factors that affect the stability and performance of satellite-to-ground laser communication (SGLC). Predicting turbulence could provide a decisive strategy for the SGLC system to ensure communication performance and is thus of great significance. In this Letter, we proposed a hybrid multi-step prediction method for atmospheric turbulence.
View Article and Find Full Text PDFLeveraging dynamics informed neural networks for predictive modeling of COVID-19 spread: a hybrid SEIRV-DNNs approach.
Sci Rep
January 2025
Institute of Mathematical Sciences, Faculty of Science, Universiti Malaya, 50603, Kuala Lumpur, Malaysia.
A dynamics informed neural networks (DINNs) incorporating the susceptible-exposed-infectious-recovered-vaccinated (SEIRV) model was developed to enhance the understanding of the temporal evolution dynamics of infectious diseases. This work integrates differential equations with deep neural networks to predict time-varying parameters in the SEIRV model. Experimental results based on reported data from China between January 1, and December 1, 2022, demonstrate that the proposed dynamics informed neural networks (DINNs) method can accurately learn the dynamics and predict future states.
View Article and Find Full Text PDFNeurophysiological data augmentation for EEG-fNIRS multimodal features based on a denoising diffusion probabilistic model.
Comput Methods Programs Biomed
January 2025
College of Medical Instruments, Shanghai University of Medicine and Health Sciences, Shanghai, 201318, PR China; Shanghai Yangpu Mental Health Center, Shanghai, 200093, PR China. Electronic address:
Background And Objective: The hybrid brain computer interfaces (BCI) combining electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) have attracted extensive attention for overcoming the decoding limitations of the single-modality BCI. With the deepening application of deep learning approaches in BCI systems, its significant performance improvement has become apparent. However, the scarcity of brain signal data limits the performance of deep learning models.
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