Biomedical Signal Processing and Control最新文献

{"title":"Study on tower crane drivers’ fatigue detection based on conditional empirical mode decomposition and multi-scale attention convolutional neural network","authors":"Daping Chen,&nbsp;Fuwang Wang","doi":"10.1016/j.bspc.2025.107662","DOIUrl":"10.1016/j.bspc.2025.107662","url":null,"abstract":"<div><div>Tower crane drivers’ fatigue may cause safety hazards and serious work accidents. Therefore, the detection of fatigue is critically important. In the research field of driving fatigue of tower crane drivers, the detection method of electroencephalogram (EEG) signals based on drivers is one of the most commonly used methods. However, noise in the real building environment often disrupts this type of detection method, leading to low classification accuracy. To solve this problem, this study proposes a driving fatigue detection model based on conditional empirical mode decomposition and multi-scale attention convolutional neural network (CEMD-MACNN). Conditional empirical mode decomposition (CEMD) overcomes the problem that traditional empirical mode decomposition (EMD) ignores important information or does not sufficiently remove the noise component when analyzing the signal. A multi-scale attention convolutional neural network (MACNN) uses channel attention to adaptively select channels containing fatigue features when extracting features at different scales, thus improving the model’s noise immunity and suppressing the influence of noise. In this study, the driving fatigue detection experiment of tower crane drivers was carried out. The Emotiv device was used to collect the EEG signal of 10 subjects in 7 driving stages, and the EEG signals were divided into awake state and fatigue state using the Karolinska sleepiness scale (KSS). The results showed that the CEMD-MACNN methods achieved an average classification accuracy of 98.70% across 10 subjects. Compared with other traditional methods, CEMD-MACNN has better anti-noise performance and higher classification accuracy.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"105 ","pages":"Article 107662"},"PeriodicalIF":4.9,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143394733","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}

{"title":"Preoperative path planning of craniotomy surgical robot based on improved MDP-LQR-RRT* algorithm","authors":"Zhenzhong Liu,&nbsp;Mingyang Li,&nbsp;Runfeng Zhang,&nbsp;Guobin Zhang,&nbsp;Shilei Han,&nbsp;Kelong Chen","doi":"10.1016/j.bspc.2025.107647","DOIUrl":"10.1016/j.bspc.2025.107647","url":null,"abstract":"<div><div>A craniotomy is significant in treating brain diseases and has gained immense attention. The study of preoperative path planning has always been a major research issue in studying craniotomy surgical robots (CSR). Reasonable path planning can effectively prevent various brain tissue injuries and subsequent damage to the brain. Thus, to promote the efficiency of preoperative path planning and improve the safety of surgery, this study proposes a novel preoperative path planning algorithm based on improved MDP-LQR-RRT*. First, the craniotomy approach was analyzed based on expert experience, and the architecture of CSR was introduced. Subsequently, a technique for dividing the bone window area was developed to determine the shape and location of the intracranial tumor before path planning. Then, we proposed the workflow of the MDP-LQR-RRT*, which introduces a workflow that uses the LQR controller to generate path points and utilizes the Markov decision process model to refine the path. Afterward, the effectiveness and accuracy of the proposed method were verified by comparing it with other benchmark methods under two-dimensional and three-dimensional scenes. Finally, experimental verification of the skull model was carried out. The results showed that the method could realize a balanced performance compared with other methods, which provides a foundation for the clinical application of surgery while significantly improving the safety of the procedure.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"105 ","pages":"Article 107647"},"PeriodicalIF":4.9,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143403002","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}

{"title":"Spatiotemporal feature disentanglement for quality surveillance of left ventricular echocardiographic video using ST-R(2 + 1)D-ConvNeXt","authors":"Chin-Chieh Hsu ,&nbsp;You-Wei Wang ,&nbsp;Lung-Chun Lin ,&nbsp;Ruey-Feng Chang","doi":"10.1016/j.bspc.2025.107671","DOIUrl":"10.1016/j.bspc.2025.107671","url":null,"abstract":"<div><div>The left ventricle (LV), as the primary chamber responsible for systemic circulation, plays a crucial role in cardiac function assessment. Echocardiography which particularly focuses on LV, is vital for cardiac disease diagnosis. However, the diagnostic accuracy heavily depends on image quality, which requires systematic assessment. In this study, we propose a two-stage deep learning approach for echocardiographic quality surveillance using a dataset of 514 annotated videos. The first stage employs EchoNet, to extract LV volumes of interest. The second stage introduces ST-R(2 + 1)D-ConvNeXt, a novel ConvNeXt-based model designed to disentangle spatiotemporal features and leverage echocardiographic hallmarks within the apical-four-chamber (A4C) dynamic echocardiogram data. The proposed approach achieves an accuracy of 82.63 %, an Area Under the Curve (AUC) of 0.89, a sensitivity of 84.10 %, and a specificity of 81.08 % in classifying echocardiographic videos into high and low quality. Furthermore, through explainable AI techniques, our model identifies specific quality issues such as missing cardiac walls, distorted or poorly positioned chambers, and other anomalies, providing interpretable feedback for clinical applications.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"105 ","pages":"Article 107671"},"PeriodicalIF":4.9,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143394735","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}

{"title":"Periodicity constrained and block accelerated thin plate spline approach for cardiac motion estimation","authors":"Yunfeng Yang ,&nbsp;Lihui Zhu ,&nbsp;Zekuan Yang ,&nbsp;Yuqi Zhu ,&nbsp;Qiyin Huang ,&nbsp;Pengcheng Shi ,&nbsp;Qiang Lin ,&nbsp;Xiaohu Zhao ,&nbsp;Zhenghui Hu","doi":"10.1016/j.bspc.2025.107655","DOIUrl":"10.1016/j.bspc.2025.107655","url":null,"abstract":"<div><div>In this paper, we propose a periodicity constrained and block accelerated Thin Plate Spline (TPS) approach for cardiac motion estimation from periodic medical image sequences. The TPS transformation is confined to specific sub-blocks to cover the motion range of the matching points during the cardiac cycle, which captured sufficient motion information while preserving computational efficiency. A periodic constraint is introduced to ensure motional consistency throughout the entire cardiac motion. The feasibility of the proposed approach was validated using the Lenna test image, further validation was conducted using MRI datasets from the Cardiac Motion Analysis Challenge (CMAC), demonstrating accurate motion estimation capability with an endpoint error (<span><math><mrow><mi>E</mi><mi>E</mi></mrow></math></span>) of less than 1 pixel and an angular error (<span><math><mrow><mi>A</mi><mi>E</mi></mrow></math></span>) of less than 5 degrees. Finally, this approach was applied to real cardiac MRI data, and the motion estimation results were shown to be consistent with the assessment of medical experts. Experimental validation demonstrates that the proposed approach provides enhanced computational flexibility in motion estimation, while expert input ensures an optimal balance between computational efficiency and precision.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"104 ","pages":"Article 107655"},"PeriodicalIF":4.9,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143394632","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}

{"title":"MECF-Net: A prostate cancer lymph node metastasis classification method based on 18F-PSMA-1007 and 18F-FDG dual-tracer PET/CT image feature optimization","authors":"Junchen Hao ,&nbsp;Yan Cui ,&nbsp;Huiyan Jiang ,&nbsp;Guoyu Tong ,&nbsp;Xuena Li","doi":"10.1016/j.bspc.2025.107651","DOIUrl":"10.1016/j.bspc.2025.107651","url":null,"abstract":"<div><div>Prostate cancer is one of the most common cancers in men, and the presence of lymph node metastasis is critical for determining treatment options and assessing prognosis. ¹⁸F-FDG is a widely used tracer in PET imaging for tumor diagnosis, while ¹⁸F-PSMA-1007 typically exhibits high specificity in prostate cancer cells. Therefore, we propose a novel model for classifying prostate cancer lymph node metastasis, MECF-Net, which integrates ¹⁸F-PSMA-1007 and ¹⁸F-FDG PET/CT images. Specifically, to enhance feature perception at different channel levels and multi-scale spatial dimensions, we propose the Multi-Scale Feature Extraction (MSFE) branch, which combines Squeeze-and-Excitation Attention with a newly designed Multi-Scale Spatial Enhanced Attention (MSEA). The MSEA extracts spatial feature information of tumors at various scales by employing global average pooling and max pooling aggregation operations at multiple scales. Furthermore, we introduce a Local-Global Feature Complementary Fusion (LGFCF) branch, which constructs a series of complementary fusion blocks as basic units. These blocks consist of concatenated multi-scale grouped convolutions and point-wise convolutions, enabling the complementary extraction of intra-group local spatial features and inter-channel global features. Finally, at the end of MECF-Net, we design a Multi-Feature Adaptive Fusion (MF-AF) module, based on a dynamic weight allocation mechanism, to fuse the features extracted from different branch sub-networks. Our experimental results on a private dual-tracer ¹⁸F-PSMA-1007/¹⁸F-FDG PET/CT dataset and a public single-tracer ¹⁸F-FDG PET/CT dataset demonstrate the effectiveness of MECF-Net, which achieved 0.9269 and 0.885 in ACC, respectively, 0.9156 and 0.8838 in AUC, respectively, demonstrating superior performance compared to state-of-the-art networks, as well as generalizability on the single-tracer dataset.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"105 ","pages":"Article 107651"},"PeriodicalIF":4.9,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143394681","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}

{"title":"Control of end-tidal carbon dioxide during phrenic nerve stimulation with mechanical ventilation","authors":"Arnhold Lohse ,&nbsp;Felix Röhren ,&nbsp;Philip von Platen ,&nbsp;Carl-Friedrich Benner ,&nbsp;Dmitrij Ziles ,&nbsp;Marius Hühn ,&nbsp;Matthias Manfred Deininger ,&nbsp;Thomas Breuer ,&nbsp;Steffen Leonhardt ,&nbsp;Marian Walter","doi":"10.1016/j.bspc.2025.107649","DOIUrl":"10.1016/j.bspc.2025.107649","url":null,"abstract":"<div><div>Mechanical ventilation maintains the gas exchange of patients in the intensive care unit which is life-saving, but prolonged ventilation results in diaphragm atrophy. Phrenic nerve stimulation can keep the diaphragm active so that atrophy might be avoided. To use phrenic nerve stimulation in a clinical setting, it is important to implement a closed-loop control system that automatically adjusts stimulation parameters to achieve the desired ventilation. This study presents the development of a robust cascaded control system for end-tidal carbon dioxide using phrenic nerve stimulation. The control system was validated in simulations with 100 virtual patients, in which the conditions of the phrenic nerve stimulation and the patient’s condition changed, as well as in animal trials using pigs. The control system proved to be robust to end-tidal carbon dioxide perturbations, such as changing stimulation efficiency, varying patient conditions, and disconnection, in both simulations and animal trials. Regarding reference tracking, the control system achieved a settling time of 5.5<!--> <!-->min–14<!--> <!-->min in simulations and of 7.3<!--> <!-->min–38.8<!--> <!-->min in animal trials. The proposed control system can be used for further development of feedback-controlled phrenic nerve stimulation in the intensive care unit.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"105 ","pages":"Article 107649"},"PeriodicalIF":4.9,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143394679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Metrics for comparison of image dataset and segmentation methods for fractal analysis of retinal vasculature","authors":"Asmae Igalla El-Youssfi,&nbsp;José Manuel López-Alonso","doi":"10.1016/j.bspc.2025.107650","DOIUrl":"10.1016/j.bspc.2025.107650","url":null,"abstract":"<div><div>Fractal analysis of images of the retinal vasculature is an instrument that has proven to be of great value both for the characterization of various pathologies and for the study of the vasculature in healthy retinas. To quantify this parameter, it is necessary to consider the treatment of the fractal object and the analysis conditions to ensure the validity of the results. Fractal and multifractal analysis of the retinal vasculature depends on several factors, including the fractal methods applied, the segmentation algorithm and calculation used, and especially the quality of the retinal image which directly influences the accuracy of the segmentation. These factors can influence the calculation and analysis of the fractal or multifractal dimensions. In the present work, different metrics have been developed to quantify the differences introduced by different segmentation methods and image datasets. Using the developed metrics, it has been possible to determine and quantify the influence of the factors studied effectively. The results indicate that the developed metrics allow to quantify these differences, as well as provide criteria on which are the best methods and protocols, which is relevant when using fractal and multifractal methods as an aid in retinal characterization and in the diagnosis of different anomalies.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"105 ","pages":"Article 107650"},"PeriodicalIF":4.9,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143394731","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}

{"title":"Airway segmentation using Uncertainty-based Double Attention Detail Supplement Network","authors":"Dexu Wang ,&nbsp;Ziyan Huang ,&nbsp;Jingyang Zhang ,&nbsp;Wei Wu ,&nbsp;Zhikai Yang ,&nbsp;Lixu Gu","doi":"10.1016/j.bspc.2025.107648","DOIUrl":"10.1016/j.bspc.2025.107648","url":null,"abstract":"<div><div>Automatic pulmonary airway segmentation from thoracic computed tomography (CT) is an essential step for the diagnosis and interventional surgical treatment of pulmonary disease. While deep learning algorithms have shown promising results in segmenting the main and larger bronchi, segmentation of the distal small bronchi remains challenging due to their limited size and divergent spatial distribution. The study aims to address the challenges associated with segmenting the pulmonary airway, particularly focusing on the distal small bronchi. Specifically, we aim to improve the accuracy and completeness of airway segmentation by developing a novel deep-learning model. To achieve this purpose, we propose an Uncertainty-based Double Attention Detail Supplement Network (UDADS-Net) to identify and supply these missing details of the airway. We introduce the Uncertainty-based Double Attention Module (UDA), which utilizes the uncertainty-based attention module to obtain the regions with high uncertainty and utilizes another attention module to identify the missing details. Moreover, we also propose the Adaptive Multi-scale Module (AMS) to optimize the process of extracting details. Evaluation of our method on the ATM’2022 airway segmentation dataset demonstrates its effectiveness, especially for segmenting distal small bronchi. Our method significantly reduces missing and fragmented parts, leading to more accurate and complete airway segmentation, and achieving higher evaluation metrics compared to the state-of-the-art (SOTA) methods.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"105 ","pages":"Article 107648"},"PeriodicalIF":4.9,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143394734","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}

{"title":"Predicting athletic injuries with deep Learning: Evaluating CNNs and RNNs for enhanced performance and Safety","authors":"Mohammad Mohsen Sadr ,&nbsp;Mohsen Khani ,&nbsp;Saeb Morady Tootkaleh","doi":"10.1016/j.bspc.2025.107692","DOIUrl":"10.1016/j.bspc.2025.107692","url":null,"abstract":"<div><div>Identifying and predicting sports injuries is crucial for managing athletes’ performance and health. Recent advancements in deep learning have emerged as powerful tools for analyzing complex data and detecting injury patterns. This study investigates the effectiveness of deep learning algorithms, particularly Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs), in identifying and predicting injury patterns in athletes. Biometric data and motion videos from training sessions were collected and analyzed, focusing on RNN architectures, including Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU). The models were trained on diverse datasets and evaluated using performance metrics such as accuracy, precision, recall, and F1-score. The results indicate that the LSTM model achieved the highest accuracy at 91.5%, outperforming both the GRU model (90.8%) and the CNN model (89.2%). The precision and recall rates for the LSTM model were 89.7% and 88.3%, respectively, solidifying its superiority in the precise identification of potential injury patterns compared to CNNs. These findings highlight the capability of deep learning algorithms, particularly RNNs, in effectively predicting and managing sports injuries. This research emphasizes the importance of leveraging deep learning techniques for injury prevention and suggests future studies should focus on enhancing model accuracy through diverse and comprehensive datasets.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"105 ","pages":"Article 107692"},"PeriodicalIF":4.9,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143395604","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}

{"title":"LRCTNet: A lightweight rectal cancer T-staging network based on knowledge distillation via a pretrained swin transformer","authors":"Jia Yan ,&nbsp;Peng Liu ,&nbsp;Tingwei Xiong ,&nbsp;Mingye Han ,&nbsp;Qingzhu Jia ,&nbsp;Yixing Gao","doi":"10.1016/j.bspc.2025.107696","DOIUrl":"10.1016/j.bspc.2025.107696","url":null,"abstract":"<div><div>Rectal cancer, a prevalent malignant neoplasm within the digestive system, significantly jeopardizes patient health and quality of life. Accurate preoperative T-staging is critical for developing effective treatment strategies. In areas with limited medical resources, computed tomography (CT) has become the norm because of its popularity and economy and is an important method for the initial diagnosis of disease. Despite major advancements in computer vision in recent years, large-scale models have high demands on hardware and datasets, making them difficult to use and deploy in resource-limited environments. To address this challenge, we designed two lightweight modules, LightFire and ResLightFire, and developed a lightweight rectal cancer T-staging network (LRCTNet). On this basis, we leveraged the swin transformer, transfer learning and knowledge distillation techniques to optimize the classification performance of the LRCTNet. The experimental results revealed that LRCTNet achieved a classification accuracy of 95.79%, precision of 93.91%, recall of 93.48%, F1 score of 93.70%, and Matthews correlation coefficient (MCC) of 94.38% while containing only 0.407 million parameters, which were much higher than those of lightweight models such as SqueezeNet, MobileNet, and EfficientNet. These results indicate that the model achieves a low misclassification rate and a low rate of missed detections, ensuring balanced performance in classification. The lightweight design of LRCTNet enables efficient deployment in resource-constrained environments without sacrificing accuracy, making it a valuable tool for rectal cancer diagnosis.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"105 ","pages":"Article 107696"},"PeriodicalIF":4.9,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143387520","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}