ArticleResearch (Washington, D.C.)2024
FM-FCN: A Neural Network with Filtering Modules for Accurate Vital Signs Extraction.
Article in Research (Washington, D.C.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 1 of them a synthesis that pooled it.
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Who cites it
34 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Predicting piRNA-Disease Associations Based on Dual-View Learning and Multi-head Self-Attention Mechanism Fusion.Interdisciplinary sciences, computational life sciences · 2026Article
- AMPred-MFG: Investigating the Mutagenicity of Compounds Using Motif-Based Graph Combined with Molecular Fingerprints and Graph Attention Mechanism.Interdisciplinary sciences, computational life sciences · 2026Article
- Noninvasive Risk Stratification Based on Renal Tubular Injury Phenotypes: A Deep Learning Study for Predicting Vesicoureteral Reflux in Children.Journal of cellular and molecular medicine · 2026Article
- ADFC-ATP: Attention-Guided Dual-View Fusion and Contrastive Pretraining for Robust Aquatic Toxicity Prediction.Journal of cellular and molecular medicine · 2026Article
- Internet-delivered cognitive behavioral therapy for insomnia: The future of insomnia treatment with large language models.Sleep medicine: X · 2025Review
- DSMR: Dual-Stream Networks with Refinement Module for Unsupervised Multi-modal Image Registration.Interdisciplinary sciences, computational life sciences · 2025Article
- A Multi-Task Self-Supervised Strategy for Predicting Molecular Properties and FGFR1 Inhibitors.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Clinical Application of Monitoring Vital Signs in Dogs Through Ballistocardiography (BCG).Veterinary sciences · 2025Article
- Multi-Scale Convolutional Attention and Structural Re-Parameterized Residual-Based 3D U-Net for Liver and Liver Tumor Segmentation from CT.Sensors (Basel, Switzerland) · 2025Article
- Investigation of cell development and tissue structure network based on natural Language processing of scRNA-seq data.Journal of translational medicine · 2025Article
- A Machine Learning Model for Diagnosing Opportunistic Infections in HIV Patients: Broad Applicability Across Infection Types.Journal of cellular and molecular medicine · 2025Article
- Investigating the relevance of nucleotide metabolism in the prognosis of glioblastoma through bioinformatics models.Scientific reports · 2025Article
- Multi-task aquatic toxicity prediction model based on multi-level features fusion.Journal of advanced research · 2025Article
- Cuproptosis-related gene signatures define the immune microenvironment in diabetic nephropathy.PloS one · 2025Article
- MDNN-DTA: a multimodal deep neural network for drug-target affinity prediction.Frontiers in genetics · 2025Article
- Article
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Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Neural networks excel at capturing local spatial patterns through convolutional modules, but they may struggle to identify and effectively utilize the morphological and amplitude periodic nature of physiological signals. In this work, we propose a novel network named filtering module fully convolutional network (FM-FCN), which fuses traditional filtering techniques with neural networks to amplify physiological signals and suppress noise. First, instead of using a fully connected layer, we use an FCN to preserve the time-dimensional correlation information of physiological signals, enabling multiple cycles of signals in the network and providing a basis for signal processing. Second, we introduce the FM as a network module that adapts to eliminate unwanted interference, leveraging the structure of the filter. This approach builds a bridge between deep learning and signal processing methodologies. Finally, we evaluate the performance of FM-FCN using remote photoplethysmography. Experimental results demonstrate that FM-FCN outperforms the second-ranked method in terms of both blood volume pulse (BVP) signal and heart rate (HR) accuracy. It substantially improves the quality of BVP waveform reconstruction, with a decrease of 20.23% in mean absolute error (
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.