生物医学信号处理和控制旨在提供一个跨学科的国际论坛，用于交换临床医学和生物科学中信号和图像的测量和分析研究的信息。重点是在临床诊断、患者监测和管理中使用方法和设备的实用、应用主导研究方面的贡献。生物医学信号处理和控制反映了这些方法在临床上使用和开发的主要领域工程和临床科学的接口。该期刊的范围被定义为包括相关的评论论文、技术说明、简短的通信和信件。教程论文和特刊也将出版。

The journal "Biomedical Signal Processing and Control" aims to provide an interdisciplinary international forum for the exchange of information on the measurement and analysis of signals and images in clinical medicine and biosciences. It focuses on practical and application-driven research contributions regarding methods and devices used in clinical diagnosis, patient monitoring, and management. "Biomedical Signal Processing and Control" reflects the main areas where these methods are applied and developed, particularly at the interface of engineering and clinical sciences. The scope of the journal includes relevant review papers, technical notes, brief communications, and letters, as well as tutorial papers and special issues.

《Biomedical Signal Processing and Control》期刊旨在提供一个跨学科的国际论坛，促进临床医学和生物科学领域中信号和图像测量与分析研究的信息交流。期刊重点关注在临床诊断、患者监测和管理中应用的方法和设备的实用性和应用导向的研究贡献。《Biomedical Signal Processing and Control》反映了这些方法在临床应用和开发中的主要领域，尤其是工程与临床科学的交汇点。期刊的范围包括相关的评论论文、技术说明、简短通信和信件，同时也会出版教程论文和特刊。

Biomedical Signal Processing and Control

Fengyi Xia

Yanjun Peng

Jiao Wang

Xue Chen

A 2.5D multi-path fusion network framework with focusing on z-axis 3D joint for medical image segmentation

一种关注z轴3D关节的2.5D多路径融合网络框架用于医学图像分割

V.P. Subramanyam Rallabandi

Krishnamoorthy Seetharaman

Deep learning-based classification of healthy aging controls, mild cognitive impairment and Alzheimer’s disease using fusion of MRI-PET imaging

Auscultation is an ordinary way to diagnose cardiovascular and respiratory diseases. The heart-lung sound (HLS) from the stethoscope is a mixture of heart sound (HS) and lung sound (LS). The first and most crucial step is to differentiate between HS and LS from the mixture signal. This paper proposes an unsupervised single-channel blind source separation (BSS) algorithm based on nonnegative matrix factorization (NMF) and deep learning (DL). Firstly, HS signals are extracted by the multi-constrained NMF (MCNMF) decomposition algorithm and K-means & SVM (KS) clustering method proposed in this paper, and different types of HS signals are classified by a convolutional neural network (CNN). Then, the HLS is separated by the embedding space centroid network (ECNet) proposed in our study. The method outperforms the known state-of-the-art techniques in all evaluation metrics SNR, SDR, SIR, SAR, and correlation coefficient. Our algorithm has two advantages: 1) this study is the first to combine NMF and DL for the HLS separation. MCNMF with selected constraints can have a more substantial sparsity effect that improves the decomposition accuracy. KS clustering algorithm can distinguish the HS component from the LS component in two clustering results. By mapping classified HLS into an embedding space, the ECNet demonstrates excellent separation results. 2) HS extraction and classification first, then HLS separation. Because our method exploits more detailed information from HS types in HLS before BSS, the ECNet can outperform other HLS separation methods.

Weibo Wang

Shubo Wang

Dimei Qin

Yu Fang

Yongkang Zheng

Heart-lung sound separation by nonnegative matrix factorization and deep learning

基于非负矩阵分解和深度学习的心肺音分离

Nana Jia

Tong Jia

Li Zhao

Bowen Ma

Zheyi Zhu

Multi-modal global- and local- feature interaction with attention-based mechanism for diagnosis of Alzheimer’s disease

基于注意力机制的多模态全局与局部特征交互用于阿尔茨海默病诊断

Ziwei Hu

Yanglin Deng

Junlin Lan

Tao Wang

Zixin Han

Yuxiu Huang

Hejun Zhang

Jianchao Wang

Musheng Cheng

Haiyan Jiang

Ren-Guey Lee

Ming Du

Tong Tong

Qinquan Gao

Gang Chen

A multi-task deep learning framework for perineural invasion recognition in gastric cancer whole slide images

用于胃癌全切片图像中周围神经侵犯识别的多任务深度学习框架

Xue Yuan

Maozhou Chen

Peng Ding

Anan Gan

Keren Shi

Anming Gong

Lei Zhao

Tianwen Li

Yunfa Fu

Yuqi Cheng

Intermediary-guided windowed attention Aggregation network for fine-grained characterization of Major Depressive Disorder fMRI

中介引导的窗口注意力聚合网络用于重度抑郁症fMRI的细粒度特征表征

Chongbo Yin

Jian Qin

Yan Shi

Yineng Zheng

Xingming Guo

Detection of severe coronary artery disease based on clinical phonocardiogram and large kernel convolution interaction network

基于临床心音图和大核卷积交互网络的严重冠状动脉疾病检测

Pengfei Hou

Xiaowei Li

Jing Zhu

Bin Hu

A lightweight convolutional transformer neural network for EEG-based depression recognition

基于脑电图的抑郁症识别的轻量级卷积变换器神经网络

Prithwijit Mukherjee

Anisha Halder Roy

A deep learning-based comprehensive robotic system for lower limb rehabilitation

基于深度学习的下肢康复综合机器人系统

Daniele Borzelli

Martina Morano

Sandro Fioretti

Francesco Di Nardo

The pooled scalogram: A wavelet-based approach to detect the co-activation of several muscles in the time-frequency domain

池化尺度图：一种基于小波的方法，用于在时频域中检测多块肌肉的协同激活

Abbreviation	Biomed. Signal. Process. Control.
Journal Impact	4.91
Quartiles(Global)	ENGINEERING, BIOMEDICAL(Q1)

ISSN	1746-8094, 1746-8108
h-index	108

Publication Information	Publisher: Elsevier B.V.，Publishing cycle: Quarterly，Journal Type: journal，Open Access Journals: No
Basic data	Year of publication: 2006，Proportion of original research papers: 99.21%，Self Citation Rate:12.20%， Gold OA Rate: 8.48%
Average review cycle	网友分享经验：平均3.0个月
Average recruitment ratio	网友分享经验：容易

Biomedical Signal Processing and Control

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