Biomedical Signal Processing and Control最新文献_第2页

FV-DDC: A novel finger-vein recognition model with deformation detection and correction FV-DDC：带有形变检测和校正功能的新型指静脉识别模型

IF 4.9 2区医学

Biomedical Signal Processing and Control Pub Date : 2024-11-02 DOI: 10.1016/j.bspc.2024.107098

{"title":"FV-DDC: A novel finger-vein recognition model with deformation detection and correction","authors":"","doi":"10.1016/j.bspc.2024.107098","DOIUrl":"10.1016/j.bspc.2024.107098","url":null,"abstract":"<div><div>Finger vein recognition has gained widespread attention for personal identification due to its robustness and resistance to forgery. While Convolutional Neural Network (CNN)-based finger vein recognition algorithms have shown promising performance, several challenges remain. Firstly, existing methods often fail to effectively handle complex finger deformations, such as bending and rotation, which frequently occur in real-world applications. Secondly, CNN-based approaches typically require large training datasets, yet the available finger vein datasets are limited in size. To address these challenges, this paper presents a novel CNN-based finger vein recognition algorithm, FV-DDC, incorporating a lightweight finger deformation correction module, FVTN. The FVTN module autonomously learns and corrects finger deformations using matrix transformations, offering a new approach to CNN-based deformation correction. The primary advantages of FV-DDC are twofold: automatic finger deformation correction, which simplifies preprocessing, and data augmentation during deformation correction, reducing the dependency on large datasets. Extensive experiments were conducted on three publicly available datasets to validate the effectiveness of the proposed algorithm. The results show that FV-DDC achieves superior recognition performance, particularly in scenarios involving missing data and deformation interference, with recognition accuracies of 99.62% on HKPU, 99.80% on FV-USM, and 98.74% on SDUMLA.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142572443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Development of a wearable ultrasound–FES integrated rehabilitation and motor-functional reconstruction system for post-stroke patients 为脑卒中后患者开发可穿戴超声-FES 集成康复和运动功能重建系统

IF 4.9 2区医学

Biomedical Signal Processing and Control Pub Date : 2024-11-02 DOI: 10.1016/j.bspc.2024.106846

{"title":"Development of a wearable ultrasound–FES integrated rehabilitation and motor-functional reconstruction system for post-stroke patients","authors":"","doi":"10.1016/j.bspc.2024.106846","DOIUrl":"10.1016/j.bspc.2024.106846","url":null,"abstract":"<div><div>Post-stroke patients experience a significant decrease of self-care capabilities in their daily lives because of motor dysfunction. The combination of intention recognition and functional electrical stimulation (FES) is used frequently to assist in improving the self-care capabilities for post-stroke patients. However, the electrical noise from the environment and the weak bio-signal from post-stroke patients lead to low-accurate intention recognition for post-stroke patients. To overcome the issue, this paper introduces a wearable rehabilitation and motor-functional reconstruction system for post-stroke rehabilitation with a new intention recognition system. This system consists of an FES unit and a wearable musculoskeletal ultrasound system. The integration of the wearable ultrasound system allows for high-accuracy continuous intention recognition whilst the FES unit is in operation. This key feature significantly enhances the system’s robustness in FES control, augments the signal-to-noise ratio and offers precise assistance in the reconstruction of motor function, thereby improving the effectiveness of post-stroke rehabilitation. In this study, the feasibility and efficiency of the proposed system were investigated. In the clinical trial, eight post-stroke subjects were recruited. In the experiment of motor-functional reconstruction, the proposed system demonstrated enhancements of approximately 23 % and 76 % in wrist raising angle and velocity, respectively. These results demonstrated that the proposed wearable system is effective for active rehabilitation and potential candidate to reconstruct the motor function of post-stroke patients.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142572445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ROSE-Net: Leveraging remote photoplethysmography to estimate oxygen saturation using deep learning ROSE-Net：利用深度学习估算血氧饱和度

IF 4.9 2区医学

Biomedical Signal Processing and Control Pub Date : 2024-11-01 DOI: 10.1016/j.bspc.2024.107105

{"title":"ROSE-Net: Leveraging remote photoplethysmography to estimate oxygen saturation using deep learning","authors":"","doi":"10.1016/j.bspc.2024.107105","DOIUrl":"10.1016/j.bspc.2024.107105","url":null,"abstract":"<div><div>A method for accurately estimating physiological signals from video streams at a minimal cost holds immense value, particularly in pre-clinical health monitoring applications. This technique is particularly indispensable in scenarios where traditional sensors, such as finger photoplethysmography (PPG), are not viable, such as in cases involving burn victims, premature infants, or individuals with sensitive skin. Remote photoplethysmography (rPPG) is a process of estimating PPG signals using video streams instead of traditional sensors. rPPG has thus been seen as a promising alternative to traditional PPG. As an alternative to using PPG for estimating oxygen saturation (SpO2), we propose ROSE-Net. ROSE-Net, trained on clinical PPG, was tested on an external rPPG dataset, PURE. The model achieved a mean absolute error (MAE) of 1.20 and a root mean square error (RMSE) of 1.86 on clinical PPG. When tested on rPPG, it exhibited an MAE of 1.95 and an RMSE of 2.46 in PURE, an MAE of 0.77, and an RMSE of 0.96 in ARPOS. These results demonstrate the model’s ability to estimate SpO2 levels within acceptable margins when applied to rPPG data. Consequently, rPPG presents a viable approach for estimating SpO2 levels, paving the way for non-contact health tracking applications.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142572448","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MindCeive: Perceiving human imagination using CNN-GRU and GANs MindCeive：利用 CNN-GRU 和 GAN 感知人类的想象力

IF 4.9 2区医学

Biomedical Signal Processing and Control Pub Date : 2024-11-01 DOI: 10.1016/j.bspc.2024.107110

{"title":"MindCeive: Perceiving human imagination using CNN-GRU and GANs","authors":"","doi":"10.1016/j.bspc.2024.107110","DOIUrl":"10.1016/j.bspc.2024.107110","url":null,"abstract":"<div><div>Neuroscience has made astonishing advancements in understanding the human brain with the help of Brain-Computer Interface. Recent contributions in the field of Artificial Intelligence by different researchers made it possible to perceive human imagination by decoding brain signals. Generating visual stimuli perceived by humans will help in analyzing how the human brain works and behaves to different perceptual experiences. Different techniques like Electroencephalography, Magnetoencephalography, functional Magnetic Resonance Imaging, etc. are used to capture brain signals. Electroencephalography signals are non-invasive, low cost, and also have high temporal resolution, therefore they are preferred. Machine learning models are used to extract important features from these signals. These extracted features are then used by Generative Adversarial Network to generate images representing human imagination. This work uses Electroencephalography signals to generate realistic images. The task of extracting important features from Electroencephalography signals is achieved using Convolutional Neural Network and Gated Recurrent Unit based feature extractor. The proposed feature extractor accomplishes better classification accuracy than existing models. By using these extracted features in combination with proposed novel architecture of Generative Adversarial Network, realistic images of objects imagined by humans are generated. The proposed MindCeive approach outperforms previous works by showing improvement in various performance metrics such as Classification Accuracy, Inception Score, and Class Diversity Score.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142573359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Coarse-to-Fine bone age regression by using multi-scale self-attention mechanism 利用多尺度自我关注机制进行粗-细骨龄回归

IF 4.9 2区医学

Biomedical Signal Processing and Control Pub Date : 2024-11-01 DOI: 10.1016/j.bspc.2024.107029

{"title":"Coarse-to-Fine bone age regression by using multi-scale self-attention mechanism","authors":"","doi":"10.1016/j.bspc.2024.107029","DOIUrl":"10.1016/j.bspc.2024.107029","url":null,"abstract":"<div><div>Pediatric bone age assessment (BAA) is a widely-used clinical technique employed to investigate various growth, genetic, and endocrine disorders in children. In this article, we propose a novel network architecture called BoGFF-Net that integrates multi-scale hand bone feature maps across different levels, and introduce an adaptive triplet loss (ATL) function that can distinguish sample pairs in regression tasks. Our network incorporates self-attention mechanisms to adaptively learn the most important regions of hand bone images, which deviates from the conventional approach of extracting specific regions in the field of bone age assessment. Additionally, we observe heterogeneous characteristics of hand bone development among different age ranges in adolescents. Therefore, we introduce a two-stage coarse-to-fine framework that can accommodate greater differences in bone modalities across diverse age groups. Quantitative and qualitative results from extensive experiments on two public bone age datasets highlight the performance and effectiveness of our model. Specifically, our model achieves competitive performance with a 3.91 mean absolute error (MAE) on the RSNA test dataset, compared to the latest model proposed by Yang et al. 2023, and a 7.07 MAE on the DHA dataset, setting a new state-of-the-art benchmark. The data and code are available at: <span><span>BoGFF-Net</span><svg><path></path></svg></span></div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142573360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Medical image fusion via decoupled representation and component-wise regularization learning 通过解耦表示和分量正则化学习实现医学图像融合

IF 4.9 2区医学

Biomedical Signal Processing and Control Pub Date : 2024-11-01 DOI: 10.1016/j.bspc.2024.106859

{"title":"Medical image fusion via decoupled representation and component-wise regularization learning","authors":"","doi":"10.1016/j.bspc.2024.106859","DOIUrl":"10.1016/j.bspc.2024.106859","url":null,"abstract":"<div><div>Medical image fusion plays an important role in the precise diagnosis, treatment planning, and follow-up studies of various diseases. While tremendous improvements in medical image fusion based on convolution sparse coding have been achieved, existing methods are still limited by the intractable redundancy information interaction between source medical images. In this paper, we propose an easy yet effective representation and regularization learning method based on decomposed components scheme with high competitive performance. We construct more compact information interactions by decoupled representation learning, which simultaneously mitigates the problem of redundancy in fusion component entanglement. And then two different regularization operators are adaptively exploited to depict two different components separately, which describe the structural-inspired difference based on the decoupled principle. Furthermore, we combine the alternating direction method of multipliers (ADMM) algorithm and the conjugate gradient (CG) method to optimize our proposed model. Our experiments demonstrate that our proposed method has significant improvements in efficiency and fusion performance against the state-of-the-art methods.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142572444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

PhysKANNet: A KAN-based model for multiscale feature extraction and contextual fusion in remote physiological measurement PhysKANNet：基于 KAN 的远程生理测量多尺度特征提取和上下文融合模型

IF 4.9 2区医学

Biomedical Signal Processing and Control Pub Date : 2024-11-01 DOI: 10.1016/j.bspc.2024.107111

{"title":"PhysKANNet: A KAN-based model for multiscale feature extraction and contextual fusion in remote physiological measurement","authors":"","doi":"10.1016/j.bspc.2024.107111","DOIUrl":"10.1016/j.bspc.2024.107111","url":null,"abstract":"<div><div>Physiological indicator reflects the health status of the human body, and remote photoplethysmography (rPPG) is a highly promising technology for contactless measurement of these indicators through facial video. However, current deep learning methods mainly rely on traditional neural networks with limited spatiotemporal receptive fields, overlooking the importance of multi-scale features and noise resistance in rPPG signal modeling. This results in challenges when addressing subtle color changes and noise interference. To overcome these limitations, we leverage the advantages of the Kolmogorov-Arnold Network (KAN) in handling sparse data and propose PhysKANNet, a novel KAN-based encoder–decoder architecture that integrates multi-scale feature extraction and contextual information fusion to enhance rPPG signal extraction. We introduce three new plug-and-play modules for PhysKANNet: the rPPG-Aware Convolutional Attention Block, which extracts features at different scales through a multi-branch structure and enhances multi-scale representation using KAN’s nonlinear modeling capabilities; the Multi-Dimensional Feature Fusion Block, which combines high-dimensional features from the encoder with low-dimensional features from the decoder; and the rPPG Edge Sampling Block, which fuses edge and semantic information to further optimize signal extraction accuracy. We employ unsupervised learning for training PhysKANNet and conducted comprehensive experiments on multiple benchmark datasets. The results show that PhysKANNet significantly improves feature learning from unlabeled data, achieving excellent performance across various testing scenarios.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142572447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Liver disease classification using histogram-based gradient boosting classification tree with feature selection algorithm 利用基于直方图的梯度提升分类树和特征选择算法进行肝病分类

IF 4.9 2区医学

Biomedical Signal Processing and Control Pub Date : 2024-10-31 DOI: 10.1016/j.bspc.2024.107102

{"title":"Liver disease classification using histogram-based gradient boosting classification tree with feature selection algorithm","authors":"","doi":"10.1016/j.bspc.2024.107102","DOIUrl":"10.1016/j.bspc.2024.107102","url":null,"abstract":"<div><div>Healthcare is the key for everyone to run daily life, and health diagnosing techniques should be accessible easily. Indeed, the early identification of liver disease will be supportive for physicians to make decisions. Utilizing feature selection and classification approaches, this work aims to predict liver disorders through machine learning. The Histogram-based Gradient Boosting Classification Tree with a recursive feature selection algorithm (HGBoost) is proposed in this paper. The recursive feature selection approach and the Gradient Boosting are used to forecast liver disease. Using data from Indian liver patient records, the proposed HGBoost method has been assessed. Assessing the accuracy, confusion matrix, and area under curve involves implementing and comparing a variety of classification techniques, including MLP, Gboost, Adaboost, and proposed HGBoost algorithms. With the help of the recursive feature selection technique, the proposed HGBoost has surpassed other current algorithms. In comparison to the MLP, RF, Gboost, Adaboost, and proposed HGBoost algorithms, the enhanced accuracy is between 4 and 9% and between 1 and 7 % of the MSE error.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142561152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Video-based heart rate estimation with spectrogram signal quality ranking and fusion 基于视频的心率估计与频谱图信号质量排序和融合

IF 4.9 2区医学

Biomedical Signal Processing and Control Pub Date : 2024-10-30 DOI: 10.1016/j.bspc.2024.107094

{"title":"Video-based heart rate estimation with spectrogram signal quality ranking and fusion","authors":"","doi":"10.1016/j.bspc.2024.107094","DOIUrl":"10.1016/j.bspc.2024.107094","url":null,"abstract":"<div><div>Remote photoplethysmography (rPPG) enables non-contact measurement of heart rate (HR). However, the stability of rPPG extraction is a bottleneck limiting its application. To address this issue, a signal quality ranking and fusion (SQRF) approach based on HR continuity in the time–frequency domain is introduced. Firstly, the facial region is divided into multiple regions of interest (ROIs), and the raw blood volume pulse (BVP) signal is extracted from each ROI separately using a conventional rPPG method such as the plane orthogonal to skin (POS) method. Then, wavelet synchrosqueezed transform (WSST) is employed to convert the raw pulse signals into spectrograms, which are further ranked according to the HR instantaneous continuity. The selected spectrograms with high-quality HR continuity are then fused using a weighted average to predict the final HR. The proposed SQRF algorithm is verified on three public datasets DDPM, UBFC-Phys and PURE with real scenarios. The obtained mean absolute error (MAE) was reduced by 58.7%, 47.5%, and 16.0% respectively, compared to the original single-ROI method. The results prove that SORF with spectrogram-based HR continuity can consistently boost the stability of POS.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142551984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CoLM: Contrastive learning and multiple instance learning network for lung cancer classification of surgical options based on frozen pathological images CoLM：基于冰冻病理图像的肺癌手术方案分类对比学习和多实例学习网络

IF 4.9 2区医学

Biomedical Signal Processing and Control Pub Date : 2024-10-30 DOI: 10.1016/j.bspc.2024.107097

{"title":"CoLM: Contrastive learning and multiple instance learning network for lung cancer classification of surgical options based on frozen pathological images","authors":"","doi":"10.1016/j.bspc.2024.107097","DOIUrl":"10.1016/j.bspc.2024.107097","url":null,"abstract":"<div><div>Histopathological images are regarded as the gold standard in cancer diagnosis. Formalin-fixed paraffin-embedded (FFPE) tissues are routinely collected and archived for pathological examination. However, the time-consuming procedures of tissue fixation and embedding render FFPE tissues unsuitable for intraoperative diagnosis, where immediate results are crucial during surgical procedures. In contrast, obtaining a fresh frozen section (FS) takes a very short time. FS samples are widely utilized for intraoperative diagnosis, whereas the diagnostic accuracy of FS is currently limited by the presence of potential histological artifacts. In this paper, we propose a contrastive learning image translation and multiple instance learning network (CoLM) for lung cancer classification. CoLM efficiently translates FS images into FFPE-style images and facilitates whole slide image classification. The entire framework encompasses two crucial stages. In the first stage, we employ a contrastive learning translation network with a dual-attention module (CL-DAM) for image translation. In the second stage, we utilize a hybrid transformer multi-instance learning-based network (HTM) to address the challenge posed by weak labels. We conduct experiments on lung cancer datasets to validate the performance of our proposed approach. The results demonstrate that our method achieve superior classification performance over other state-of-the-art methods, effectively mitigating the impact of blurred FS images. The proposed framework not only elevates the precision of intraoperative diagnosis when employing FS but also provides valuable reference for pathologists through the application of synthetic images.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":null,"pages":null},"PeriodicalIF":4.9,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142553061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0