生物医学工程学杂志最新文献

{"title":"[A generalizable epilepsy detection network based on dual-attention mechanism].","authors":"Lei Zhang, Fengwen Zhai, Jing Jin, Xiangde Jiang, Wenwen Chang","doi":"10.7507/1001-5515.202508053","DOIUrl":"10.7507/1001-5515.202508053","url":null,"abstract":"<p><p>Existing deep learning models for epileptic electroencephalogram (EEG) signal analysis frequently overlook intrinsic pathological characteristics during feature extraction and exhibit insufficient cross-dataset generalization. To address these limitations, this study proposes an innovative dual-attention epilepsy detection network (EDDANet). The model integrates a multi-band and multi-scale dual-attention module with a dynamic kernel sampling adaptive convolutional module to classify interictal and ictal EEG signals. Extensive experiments conducted on four heterogeneous public datasets demonstrate that EDDANet consistently outperforms state-of-the-art models across key evaluation metrics, including accuracy and recall. Notably, this work is the first to achieve robust generalization across varying lead configurations, sampling rates, and electrode layouts. In conclusion, this study provides a valuable methodological framework for the design and optimization of automated epilepsy detection systems in complex scenarios, providing reference for enhancing the generalizability and clinical utility of deep learning models in real-world environments.</p>","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"43 3","pages":"530-538"},"PeriodicalIF":0.0,"publicationDate":"2026-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13311036/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148346559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Advances in research on neuroelectrophysiological characteristics of post-stroke cognitive impairment based on quantitative electroencephalography and acupuncture interventions].","authors":"Yachen Feng, Kaiqi Su, Qiongdi Ren, Rui Pan, Chunxiao Wang, Xu Zhang, Qi Li, Yaling Wang, Ming Zhang","doi":"10.7507/1001-5515.202602016","DOIUrl":"10.7507/1001-5515.202602016","url":null,"abstract":"<p><p>Post-stroke cognitive impairment (PSCI) is a common complication of stroke that severely affects patients' quality of life and social functional recovery. Quantitative electroencephalography (QEEG), as a non-invasive neuroelectrophysiological technique with high temporal resolution, can reveal PSCI-related brain dysfunction and plasticity changes from multiple levels and dimensions, including neural oscillations, brain network connectivity, and dynamic optimization of brain function. However, its feature integration and efficacy evaluation system still require further refinement. In recent years, acupuncture has attracted increasing attention in the intervention of PSCI owing to its holistic regulatory effects and favorable safety profile. This review systematically integrates the characteristics of brain rhythms, brain functional networks, and dynamic brain activity in patients with PSCI, analyzes the associations between different electroencephalography (EEG) indicators and cognitive domains, and summarizes the multidimensional changes in EEG features following acupuncture intervention in PSCI. The aim is to provide evidence for a deeper understanding of the neuroelectrophysiological mechanisms underlying PSCI, while also offering references for the objective evaluation and modernization of acupuncture interventions.</p>","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"43 3","pages":"661-668"},"PeriodicalIF":0.0,"publicationDate":"2026-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13311034/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148346574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[A two-stage model for predicting postoperative pulmonary infection in esophageal cancer patients].","authors":"Yu Wang, Lixia Yin, Bo Yao, Jiang Wu, Jiangbo Pu","doi":"10.7507/1001-5515.202601010","DOIUrl":"10.7507/1001-5515.202601010","url":null,"abstract":"<p><p>Postoperative pulmonary infection (PPI) after esophageal cancer surgery occurs frequently and severely impairs patients' prognosis. Most existing prediction models cannot realize staged classification of risk factors, which limits targeted risk identification and intervention. Based on machine learning algorithms, this study integrates preoperative baseline characteristics and perioperative indicators to construct a preoperative-perioperative two-stage risk prediction model for postoperative pulmonary infection. Clinical data of 2 200 patients undergoing esophageal cancer surgery admitted to the Cancer Hospital, Chinese Academy of Medical Sciences between October 2022 and August 2024 were retrospectively enrolled. The least absolute shrinkage and selection operator was combined with multivariate logistic regression to screen independent predictive variables for the two stages, and five machine learning models were established accordingly. Six independent predictive variables were identified. The preoperative predictors included gender, American Society of Anesthesiologists (ASA) physical status classification, and colonization of multidrug-resistant bacteria. All models yielded area under the curve values ranging from 0.71 to 0.72 with a specificity higher than 98%, which can be used for preoperative risk stratification of high-risk individuals. On the basis of preoperative variables, the perioperative stage additionally incorporated operation duration, postoperative intensive care unit (ICU) admission, and peak C-reactive protein level within 0-3 days after surgery, leading to a remarkable improvement in predictive performance with all area under the curve values greater than 0.82. The gradient boosting machine (GBM) model achieved a favorable balance between a sensitivity of 69.07% and a specificity of 82.59%, providing support for risk stratification and clinical management decision-making. Further multicenter studies are required to validate the generalization ability of the model.</p>","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"43 3","pages":"539-545"},"PeriodicalIF":0.0,"publicationDate":"2026-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13311042/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148346522","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Multi-lead electrocardiogram atrial fibrillation detection algorithm based on multi-scale patch-based attention].","authors":"Xiaojia Wang, Ruidong Yan, Mingxin Zhang, Chunfeng Yang, Yushun Gong","doi":"10.7507/1001-5515.202511015","DOIUrl":"10.7507/1001-5515.202511015","url":null,"abstract":"<p><p>Aiming at the deficiencies of insufficient cross-domain generalization and poor rhythm sensitivity in atrial fibrillation (AF) detection from multi-lead electrocardiogram (ECG) signals, this paper proposes a novel AF detection algorithm based on multi-scale patch attention fusion. The method segmented ECG signals into overlapping temporal fragments of different scales to capture local waveform details and long-range rhythm patterns respectively; it fused cross-scale feature information through a multi-scale attention mechanism to strengthen the model's ability to perceive local and global rhythm features, and introduced the self-attention mechanism of Transformer to capture long-range rhythm correlations among fragments, thus realizing in-depth mining of ECG features. The algorithm was validated on the public CinC2021 dataset and the self-constructed clinical Clin-ECG dataset. Experimental results showed that the algorithm achieved an accuracy of 94.6% and 92.7% on the two datasets, with F1 scores reaching 0.945 and 0.923, respectively. Compared with baseline models such as ECG-ResNet and CNN-BiLSTM, the proposed algorithm exhibited higher accuracy and better cross-dataset generalization ability, providing an effective method for the automatic detection of AF from multi-lead ECG signals.</p>","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"43 3","pages":"546-553"},"PeriodicalIF":0.0,"publicationDate":"2026-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13311038/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148346685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Research on the classification method of unbalanced data for the assessment of traffic accident disability level].","authors":"Hubin Yan, Shaohua Wang, Jianlin Jia, Wenhui Qin","doi":"10.7507/1001-5515.202508042","DOIUrl":"10.7507/1001-5515.202508042","url":null,"abstract":"<p><p>The evaluation of disability grades in traffic accidents is a professional forensic clinical appraisal matter, and its results directly affect the fairness of judicial compensation. In the construction of automated disability grade evaluation models, the imbalanced distribution of disability cases leads to low recognition accuracy for minority categories, becoming a key bottleneck restricting the technology's implementation. In response, this paper proposes an imbalanced data classification method based on a hybrid parameter scaling weight optimization mechanism. First, a loss weight calculation model is constructed based on category proportion, category sparsity, and category diversity. Second, the loss weight calculation model is designed by integrating the focal loss function's ability to focus on hard samples with the cross-entropy loss function's global gradient stability advantage. Then, at the early stages of training, the model proposed in this paper aligns sensitivity to imbalanced categories and constructs a low-computational-demand hybrid parameter scaling weight optimization mechanism. Experimental results show that, compared with the best-performing baseline methods, the proposed method significantly improves both accuracy and macro-F1 score on the traffic accident disability grade dataset. It can effectively enhance the classification performance of minority grade categories in imbalanced data and help improve the accuracy of automated appraisal in judicial identification of traffic accident disability grades.</p>","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"43 3","pages":"607-614"},"PeriodicalIF":0.0,"publicationDate":"2026-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13311051/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148346795","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[White matter microstructural disturbance and its relationship with cognitive function in schizophrenia with comorbidity of metabolic syndrome].","authors":"Jingyu Zhou, Kai Yu, Taisheng Han, Huan Huang, Mingjun Duan, María Luisa Bringas Vega, Cheng Luo, Dezhong Yao","doi":"10.7507/1001-5515.202603015","DOIUrl":"10.7507/1001-5515.202603015","url":null,"abstract":"<p><p>The mechanisms underlying the impact of metabolic syndrome on cognitive dysfunction in patients with schizophrenia remain unclear. The present study employed a two-factor factorial design to investigate the effects of metabolic syndrome on white matter microstructure in schizophrenia and its association with cognitive function. A total of 187 participants were included and classified into four groups based on the diagnoses of schizophrenia and metabolic syndrome: schizophrenia patients with metabolic syndrome (SZ-wMS), schizophrenia patients without metabolic syndrome, healthy controls with metabolic syndrome, and healthy controls without metabolic syndrome. Diffusion tensor imaging data were acquired. Using diffusion tensor model, fractional anisotropy (FA) was calculated to characterize the microstructural integrity of white matter. Peripheral metabolic indices and multiple domains of cognitive function were also assessed. The SZ-wMS group showed further reduced FA in the right sagittal stratum. Within the two-factor analytical framework, an interaction effect between metabolic syndrome and schizophrenia on FA in the right sagittal stratum was identified. Correlation analyses revealed that reduced FA in the right sagittal stratum was associated with impaired language function in patients with schizophrenia. Moreover, mediation analysis indicated that body mass index might indirectly affect language function by influencing FA in the right sagittal stratum. In summary, reduced integrity of white matter fibers in sagittal stratum may represent a potential neural mechanism underlying the comorbidity of schizophrenia and metabolic syndrome and may be associated with language dysfunction in schizophrenia.</p>","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"43 3","pages":"441-449"},"PeriodicalIF":0.0,"publicationDate":"2026-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13311043/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148346041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Novel framework based on multi-stage random sampling and differentiable rendering for automated three-dimensional orientation estimation of skull base foramina].","authors":"Yonghui Li, Han Zhang, Shuai Geng, Dongliang Tan, Weili Shi, Yu Miao, Zhengang Jiang","doi":"10.7507/1001-5515.202512001","DOIUrl":"10.7507/1001-5515.202512001","url":null,"abstract":"<p><p>The foramen ovale and foramen rotundum of the skull base serve as critical anatomical approaches for percutaneous puncture of the trigeminal ganglion. Their three-dimensional orientation is crucial for designing surgical pathways and avoiding adjacent vital structures. However, this orientation exhibits significant individual variability, and its assessment currently lacks automated quantitative methods. To address this issue, the present study formulates the puncture foramina orientation estimation as a geometric optimization problem and proposes a coarse-to-fine solution framework: first, a coarse estimate is obtained through adaptive multi-stage random sampling to rapidly cover the feasible region, followed by a refined estimate using optimization techniques based on differentiable rendering. Experiments on 64 foraminal structures from 16 subjects demonstrated that the average angular error of the automatically estimated results was 2.21 °, which is smaller than the inter-observer variability among physicians 4.81 °. The method outperforms manual judgment in terms of accuracy, providing an objective anatomical reference for personalized puncture path planning and demonstrating clear potential for clinical translation.</p>","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"43 3","pages":"580-588"},"PeriodicalIF":0.0,"publicationDate":"2026-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13311044/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148346642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Gait recognition based on feature-level fusion of motion posture and surface electromyography].","authors":"Chao Ma, Mu Li, Jie Yao","doi":"10.7507/1001-5515.202509001","DOIUrl":"10.7507/1001-5515.202509001","url":null,"abstract":"<p><p>To address the problems of misidentification of similar gaits, excessive feature dimensionality, and computational complexity in gait recognition, this paper proposes a gait recognition method based on feature-level fusion. After validating the complementarity between motion posture signals and surface electromyography (sEMG) signals, parameters in the time, frequency, and time-frequency domains of the two types of signals were extracted. Based on the energy distribution of acceleration, angular velocity, and angle signals from motion posture signals, feature-level fusion was performed. A dual constraint strategy combining Gain-based discriminability filtering and energy-ratio stability filtering was adopted to reduce feature dimensions, yielding the most discriminative feature subset, upon which the XGBoost model was applied for gait recognition. Experimental results showed that the proposed method improved the average recognition accuracy by 8.6% over the baseline model that used only motion posture signals, reaching 95.8%. Specifically, the accuracies for forward, backward, and turning gaits reached 89.8%, 95.2%, and 97.3%, respectively, effectively reducing the misidentification rates for these three similar gaits. Furthermore, the feature-level fusion strategy effectively improved computational efficiency, and the energy distribution-based feature selection strategy reduced the impact of background noise on feature parameter perturbations, thereby enhancing model stability. This method provides strong technical support and engineering application value for gait feature parameter identification and real-time intelligent gait recognition control of exoskeletons.</p>","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"43 3","pages":"598-606"},"PeriodicalIF":0.0,"publicationDate":"2026-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13311057/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148346604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Research on deep learning-based pathological image classification of pilonidal sinus].","authors":"Qi Wang, Siqi Ben, Xiuyan Li, Qun Ma, Shunli Qi, Shifei Liu, Aidong Liu","doi":"10.7507/1001-5515.202512031","DOIUrl":"10.7507/1001-5515.202512031","url":null,"abstract":"<p><p>Pilonidal sinus disease (PSD) is a suppurative inflammatory condition characterized by a sinus tract in the sacrococcygeal gluteal cleft and surrounding skin. Surgical resection is an important treatment for PSD; however, postoperative recurrence occurs in some patients, severely affecting their quality of life. Due to the lack of objective clinical indicators for predicting recurrence, developing an artificial intelligence-based prediction model is an effective approach. In this study, postoperative hematoxylin and eosin (H&E) stained pathological sections from PSD patients were used. The horizontal and vertical distance network (HoVer-Net) deep learning model for automated nuclei segmentation and classification was employed to extract nuclear features from lesion regions. These features were then integrated with multidimensional clinical characteristics to select key features. And then a machine learning classifier was incorporated to construct a recursive feature elimination-balanced random forest (RFE-BalancedRF) ensemble model to predict PSD recurrence. Experimental results demonstrate that the proposed PSD recurrence prediction model achieves an area under the curve of receiver operating characteristic (ROC_AUC) of 0.751 ± 0.048 and an accuracy (Acc) of 0.715 ± 0.066. Furthermore, a correlation exists between multimodal features and the risk of PSD recurrence, confirming the effectiveness of the proposed model in predicting PSD recurrence. This study innovatively integrates radiomics features with clinical characteristics from the dual perspectives of pathological image analysis and multimodal feature fusion to construct a PSD recurrence risk prediction model, potentially providing a new pathway with considerable clinical translation potential for artificial intelligence assisted medicine.</p>","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"43 3","pages":"562-570"},"PeriodicalIF":0.0,"publicationDate":"2026-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13311041/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148346764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Finite-element simulation and experimental investigation of nanosecond transcranial pulsed electric field propagation and distribution in a three-dimensional brain model].","authors":"Yue Chen, Chenguo Yao, Fanping Yan, Pangxin Xiao, Xiaoyu Lu","doi":"10.7507/1001-5515.202512068","DOIUrl":"10.7507/1001-5515.202512068","url":null,"abstract":"<p><p>Nanosecond pulsed electric field (nsPEF) exposure can disrupt and disaggregate amyloid-β, indicating its potential to improve symptoms of Alzheimer's disease. However, the propagation and distribution patterns of nsPEF within brain tissue remain insufficiently understood, making related simulation analysis necessary. In this study, a high-resolution three-dimensional human head model incorporating the scalp, skull, cerebrospinal fluid, gray matter, white matter, and hippocampus was constructed. Based on the spectral characteristics of nsPEF, the dielectric properties of human tissues at different frequency ranges were assigned, and a transient finite-element model of nsPEF exposure in the human brain was established. The simulation analysis identified two optimal electrode-pair positions and characterized the spatial distributions of intracranial electric field strength as well as current density. It further elucidated the dependence of the hippocampal electric field response and current density on pulse parameters. In addition, a physical human brain model was constructed to experimentally validate the finite-element simulation results. The results showed that transcranial nsPEF can reach deep brain regions with extremely narrow pulse widths, and pulsed electric fields with kilovolt-level amplitudes and nanosecond-scale pulse widths can generate electric field strengths of approximately 10 ³ V/m in the hippocampus. In summary, this work provides a theoretical basis and experimental support for optimizing the electrode configuration and stimulation parameters of transcranial nsPEF, thereby laying a foundation for future research on its application in non-invasive physical interventions for Alzheimer's disease.</p>","PeriodicalId":39324,"journal":{"name":"生物医学工程学杂志","volume":"43 3","pages":"504-512"},"PeriodicalIF":0.0,"publicationDate":"2026-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13311064/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148346584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}