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 signal...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(MAE)and an increase of 79.95%in signal-to-noise ratio(SNR).Regarding HR estimation accuracy,FM-FCN achieves a decrease of 35.85%in MAE,29.65%in error standard deviation,and 32.88%decrease in 95%limits of agreement width,meeting clinical standards for HR accuracy requirements.The results highlight its potential in improving the accuracy and reliability of vital sign measurement through high-quality BVP signal extraction.The codes and datasets are available online at https://github.com/zhaoqi106/FM-FCN.展开更多
文摘作为计算机视觉的基础任务,单幅图像超分辨率(Single Image Super-Resolution,SISR)长期以来一直是一个备受关注的研究课题。近期的研究表明,Transformer的成功不仅归功于其自注意力(Self-Attention,SA)机制,还体现在其宏观框架和先进组件的整体设计上。空间池化、位移、多层感知机(Multi-Layer Perception,MLP)、傅里叶变换和常数矩阵等方法,具有与SA机制相似的空间信息编码能力,能够替代并实现与其相当的效果。基于这一发现,本文的目标是利用Transformer中优越的宏观架构与高效的空间信息编码技术结合,改进复杂度较高的SA机制,以提升SISR性能。具体而言,本文重新审视了空间卷积的设计,旨在通过卷积调制技术实现更高效的空间特征编码,并通过动态调制方法表达特征。提出的高效空间信息编码(Efficient Spatial Information Encoding,ESIE)层,采用大核卷积和Hadamard积的方式,模仿查询与键之间的点积操作,并实现与SA机制中值表示再校准类似的效果。因此,ESIE层不仅能够捕捉长程依赖和自适应行为,还能够保持线性计算复杂度。另一方面,针对传统前馈网络(Feed-Forward Network,FFN)在处理空间信息时的次优表现,本文在提出的高效通道信息编码(Efficient Channel Information Encoding,ECIE)层中引入了空间感知和动态自适应机制。该方法有助于增强特征的多样性,并有效地调节层间的信息流动。实验结果表明,本文提出的高效空间-通道信息编码网络(Efficient Spatial-Channel Information Encoding,ESCIEN)在定量和定性评估上均优于现有模型。
基金supported by Ministry of Science and Technology of the People’s Republic of China(STI2030-Major Projects 2021ZD0201900)National Natural Science Foundation of China(grant mo.12090052)+2 种基金Natural Science Foundation of Liaoning Province(grant no.2023-MS-288)Fundamental Research Funds for the Central Universities(grant no.20720230017)Basic Public Welfare Research Program of Zhejiang Province(grant no.LGF20F030005).
文摘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(MAE)and an increase of 79.95%in signal-to-noise ratio(SNR).Regarding HR estimation accuracy,FM-FCN achieves a decrease of 35.85%in MAE,29.65%in error standard deviation,and 32.88%decrease in 95%limits of agreement width,meeting clinical standards for HR accuracy requirements.The results highlight its potential in improving the accuracy and reliability of vital sign measurement through high-quality BVP signal extraction.The codes and datasets are available online at https://github.com/zhaoqi106/FM-FCN.
