Biomedical Signal Processing and Control最新文献

{"title":"EFEMNet: An efficient feature extraction multi-attention convolutional neural network for skin lesion segmentation","authors":"Zijie Jing ,&nbsp;Linlin Bai ,&nbsp;Dangguo Shao ,&nbsp;Lei Ma","doi":"10.1016/j.bspc.2025.108293","DOIUrl":"10.1016/j.bspc.2025.108293","url":null,"abstract":"<div><div>Melanoma is the skin tumor with the highest mortality rate, and timely diagnosis based on dermoscopic images is an essential task in melanoma prevention and treatment. However, complex dermoscopic image morphology and unclear image edges affect the accurate diagnosis of melanoma. This study presents an encoder–decoder architecture (EFEMNet) to segment dermatologic lesions. First, residual concatenation is introduced in the encoder part to enhance the feature retention capabilities. Second, Coordinate Attention is utilized to identify and localize the target region more accurately for dermoscopic images with different morphologies. Third, an efficient feature extraction module (EFEM) is designed to improve up-sampling operations and to extract and fuse features efficiently. Finally, Global Attention Module (GAM) Attention is added to the output layer to integrate the dimensions in space and channels to solve the problem of unclear edges in dermoscopy images. The suggested method is evaluated on various datasets, such as ISIC 2018 and EFEMNet, and segmented skin lesions more accurately than some state-of-the-art network models, achieving 92.52% Dice, 86.81% IoU, 96.01% Accuracy, 93.86% Recall, and 92.92% Precision. The proposed method is shown to outperform other methods across all evaluation indices, and the effectiveness of the functional module is validated through a series of ablation experiments.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"111 ","pages":"Article 108293"},"PeriodicalIF":4.9,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144632591","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":"Deep learning-based bone marrow cytology classification: A solution to class imbalance","authors":"Zahra Asgharzadeh Bonab ,&nbsp;Sina Shamekhi ,&nbsp;Mehdi Talebi","doi":"10.1016/j.bspc.2025.108247","DOIUrl":"10.1016/j.bspc.2025.108247","url":null,"abstract":"<div><div>Classification of bone marrow cells is important for diagnosing hematopoietic diseases such as leukemia and lymphoma. Traditional methods, such as complete blood count and peripheral smear analysis, focus mainly on mature blood cells. However, bone marrow analysis is critical to understanding all stages of blood cell development. Manual bone marrow analysis is time-consuming, error-prone and requires expertise. In addition, the classification of bone marrow cells is complicated due to changing cell lineages and morphological variation, leading to data imbalance. This study introduces a new Embedding-Space Re-sampling Technique (ESRT) integrated into a feature extractor model to address data imbalance and improve classification. This algorithm generates synthetic samples for minority classes in the embedding space instead of relying on image data pixel space. The <em>ESRT</em> approach improves computational efficiency and expands decision boundaries by focusing on challenging samples to classify near decision boundaries and extract key embeddings. This method enhances the model’s ability to distinguish between classes by highlighting adversarial examples from opposing classes. Using a large 21-class bone marrow cytology dataset, the proposed framework achieved an impressive classification accuracy and Matthews Correlation Coefficient (MCC) of 89.90% and 73.19%, respectively, surpassing existing methods. Also, the accuracy and F1-score of the inference on the unseen test dataset are 75.58% and 76.11%, respectively. This framework provides a solution to data imbalance with significant efficiency, increasing model and classification performance without the need for extensive preprocessing.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"111 ","pages":"Article 108247"},"PeriodicalIF":4.9,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144632532","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":"Chromosome image segmentation based on class imbalance skeleton semi-supervised model","authors":"Jiamei Ma,&nbsp;Rongfu Zhang,&nbsp;Xuedian Zhang","doi":"10.1016/j.bspc.2025.108297","DOIUrl":"10.1016/j.bspc.2025.108297","url":null,"abstract":"<div><div>Karyotyping, encompassing chromosome identification, segmentation, and classification, is pivotal in diagnosing cancer-related and other significant global health issues. Major challenges in karyotyping are the reliance on large volumes of annotated data, and the handling of overlapping and touching chromosomes, which often result in the neglect of smaller and less numerous chromosomes. To overcome the limitations regarding these two issues, we propose an advanced semi-supervised chromosome segmentation method—CI3SM (<strong>C</strong>lass <strong>I</strong>mbalance <strong>S</strong>keleton <strong>S</strong>emi-<strong>S</strong>upervised <strong>M</strong>odel). CI3SM enhances model consistency and accuracy on unlabeled data by leveraging cross-pseudo supervision and a dual-path skeleton data augmentation strategy. To tackle issues of low segmentation accuracy for small chromosomes and slow category training speed, CI3SM incorporates a CIMM (<strong>C</strong>lass <strong>I</strong>mbalance <strong>M</strong>itigation <strong>M</strong>odule) and a CISCM (<strong>C</strong>lass <strong>I</strong>ntelligent <strong>S</strong>peed <strong>C</strong>ontrol <strong>M</strong>odule). Experimental results demonstrate that CI3SM consistently surpasses several state-of-the-art methods in chromosome segmentation tasks, achieving substantial improvements across key performance metrics. Notably, CI3SM realizes a 0.586% enhancement in the Dice coefficient, a 0.897% reduction in Average Surface Distance (ASD), and increases of 0.818%, 0.692%, and 0.654% in the Jaccard index, recall, and F1 score, respectively. These results underscore CI3SM’s capability to deliver superior segmentation outcomes by more effectively harnessing unlabeled data and implementing refined strategies tailored to address the complexities associated with small chromosome segmentation.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"111 ","pages":"Article 108297"},"PeriodicalIF":4.9,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144632593","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":"SGT-Net: Spectral generalized transformer for skin lesion segmentation","authors":"Zeye Xu,&nbsp;Jian Ji,&nbsp;Falin Wang,&nbsp;Teng Sun,&nbsp;Junkun Li","doi":"10.1016/j.bspc.2025.108214","DOIUrl":"10.1016/j.bspc.2025.108214","url":null,"abstract":"<div><div>Skin lesion segmentation is vital for melanoma diagnosis. Existing deep-learning models face issues like poor generalization due to image noise and unique-shaped images. This paper presents the Spectral Generalized Transformer, a new network based on the classic encoder–decoder. It includes a redesigned Spectral Adaptive Block (SAB) for better feature capture and connection. SAB uses Fourier Transform to convert features, extracts key features by frequency. A Feature-generalization Decoder (FD) is developed for spectral features. With the reverse attention mechanism and multi-scale fusion, it enhances edge segmentation and model generalization. Evaluated on four public datasets, the model’s mIou on PH2, ISIC2016, ISIC2017, and ISIC2018 reached 92.28%, 87.01%, 83.61%, and 81.52% respectively. The results show it surpasses state-of-the-art methods. Code available at: <span><span>https://github.com/1914010125/Zeye-Xu</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"111 ","pages":"Article 108214"},"PeriodicalIF":4.9,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144632595","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":"Semi-supervised medical image segmentation method using multi-scale consistency adversarial learning","authors":"Yujie Feng ,&nbsp;Xue Tang ,&nbsp;Qiuyu Sun ,&nbsp;Weisheng Li ,&nbsp;Shenhai Zheng","doi":"10.1016/j.bspc.2025.108250","DOIUrl":"10.1016/j.bspc.2025.108250","url":null,"abstract":"<div><div>Medical image segmentation is an essential task in medical image analysis, playing a crucial role in improving the accuracy of disease diagnosis and the efficiency of treatment planning. Despite extensive research efforts and notable technological advancements of deep learning, its performance is often limited by the necessity for vast amounts of accurately labeled images, which are inherently costly and labor-intensive to procure. To mitigate this challenge, this study proposes a novel semi-supervised segmentation using Multi-Scale Consistency Adversarial Learning (MSCAL). By leveraging few annotated images, this method constructs a comprehensive data augmentation perturbation space, incorporating both image-level strong–weak perturbations alongside multi-scale feature perturbations. Furthermore, strong–weak consistency regularization and multi-scale adversarial learning strategies across diverse scales of the segmentation network are implemented. Furthermore, the method utilizes adaptive weighted pyramid consistency loss to encourage consistent predictions across scales, and emphasizes consistency in high-confidence regions through the confidence maps outputted by the discriminator. Finally, the advantages of our proposed model are rigorously evaluated on the ACDC and BraTS2019 datasets, where it is systematically compared against ten state-of-the-art semi-supervised methods. Experimental results demonstrate the model’s superiority across DSC, HD95, ASD metrics and <span><math><mi>p</mi></math></span>-value. Notably, with only 3 annotated samples, this method achieves at least 4.2%, 2.2%, and 1.6% gains in segmentation accuracy for the right ventricle, myocardium, and left ventricle, respectively. Ablation studies further corroborate this innovative framework enables the acquisition of richer feature representations and bolstering model robustness for semi-supervised medical image segmentation tasks.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"111 ","pages":"Article 108250"},"PeriodicalIF":4.9,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144632594","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":"Assessment of quality of electrocardiograms, seismocardiograms, and gyrocardiograms based on features derived from symmetric projection attractor reconstruction in healthy subjects","authors":"Szymon Sieciński ,&nbsp;Muhammad Tausif Irshad ,&nbsp;Md Abid Hasan ,&nbsp;Rafał Doniec ,&nbsp;Paweł Kostka ,&nbsp;Ewaryst Tkacz ,&nbsp;Marcin Grzegorzek","doi":"10.1016/j.bspc.2025.108170","DOIUrl":"10.1016/j.bspc.2025.108170","url":null,"abstract":"<div><div>Signal quality assessment is essential for biomedical signal processing, analysis, and interpretation. Various methods exist, including averaged numerical values, thresholding, time- or frequency-domain analysis, and nonlinear approaches. The aim of this study was to evaluate the quality of electrocardiographic (ECG) signals, seismocardiographic signals (SCG), and gyrocardiograms (GCG) based on symmetric projection attractor reconstruction (SPAR) with Takens delay coordinates with fit five classifiers: random forest, gradient boosting, random forest XGB, and support vector machines (SVM) with various number of decision tree-based estimators (100–10,000) and various kernels (linear, radial base function, and polynomial), respectively. The analysis was carried out on a public dataset “Mechanocardiograms with ECG reference” containing 29 concurrent ECG, SCG, and GCG signal recordings. The highest values without SMOTE were observed for ECG signals, SVM with fourth order polynomial kernel (accuracy of 0.6897, PPV of 0.6019, sensitivity of 0.5306, and F1 score of 0.4952), and after applying SMOTE were observed for Gradient Boosting in ECG signal (200 estimators, accuracy 0.7500, PPV of 0.7747, sensitivity of 0.7500, and F2 score of 0.7747 respectively). These findings suggest that the SPAR-based approach is a promising method to accurately assess the quality of cardiovascular signals, including seismocardiograms and gyrocardiograms.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"111 ","pages":"Article 108170"},"PeriodicalIF":4.9,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144614748","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":"Electrocardiographic alternans as an additional criterion for cardioverter defibrillator implantation in primary prevention of sudden cardiac death","authors":"Ilaria Marcantoni ,&nbsp;Erica Iammarino ,&nbsp;Agnese Sbrollini ,&nbsp;Micaela Morettini ,&nbsp;Cees A. Swenne ,&nbsp;Laura Burattini","doi":"10.1016/j.bspc.2025.108322","DOIUrl":"10.1016/j.bspc.2025.108322","url":null,"abstract":"<div><div>The current Guidelines recommend implantable cardioverter defibrillator (ICD) for primary prevention of sudden cardiac death (SCD) when left ventricular ejection fraction (LVEF) is reduced. Nevertheless, LVEF lacks sensitivity and specificity as a risk index, meaning that additional risk indexes are needed. Electrocardiographic alternans (ECGA) is the every-other-beat morphology oscillation in either ECG wave: P-wave/QRS-complex/T-wave alternans (PWA/QRSA/TWA, respectively). This study aims to investigate ECGA as an additional criterion to decide for ICD implantation for primary prevention of SCD.</div><div>ECGs were acquired during a bicycle-ergometer test in a heart-failure population having ICDs for primary prevention. During follow-up, patients were classified into cases, if device therapy was administered, and controls, if no device therapy occurred. Resting and exercise ECGs were analyzed using the enhanced adaptive matched filter method (EAMFM) to identify ECGA.</div><div>Unlike the exercise condition, the resting condition showed a statistically significant difference in PWA and QRSA between cases and controls. Thus, to classify them, rest-related ECGA features were used to feed a support vector machine (SVM), validated by a leave-one-out cross-validation algorithm. SVM yielded a sensitivity, specificity, and F1 score of 98.49%, 83.33%, and 95.61%, respectively. These results suggest that EAMFM-derived ECGA may act as a further useful feature to stratify the arrhythmia risk, overcoming the insufficient sensitivity and specificity of LVEF only. Thus, the main contribution of this study is the proposal of an additional ECGA-based criterion for identifying patients who may benefit from primary prevention ICD implantation paving the way for a conceivable revision of the current Guidelines.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"110 ","pages":"Article 108322"},"PeriodicalIF":4.9,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144632012","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":"Tuberculosis detection on chest X-rays using two-dimensional multiscale symbolic dynamic entropy","authors":"Andreia S. Gaudêncio ,&nbsp;Miguel Carvalho ,&nbsp;Pedro G. Vaz ,&nbsp;João M. Cardoso ,&nbsp;Anne Humeau-Heurtier","doi":"10.1016/j.bspc.2025.108346","DOIUrl":"10.1016/j.bspc.2025.108346","url":null,"abstract":"<div><div>Several radiological patterns associated with pulmonary tuberculosis (TB) have been identified on chest X-rays (CXR) used for screening purposes. As a result, several automatic computational tools have emerged for this purpose. We propose a new algorithm, two-dimensional multiscale symbolic dynamic entropy (MSDE<span><math><msub><mrow></mrow><mrow><mn>2</mn><mi>D</mi></mrow></msub></math></span>), to develop a computational tool sensitive to these subtle patterns variations and noise robustness for evaluating CXR images from healthy and TB-diagnosed individuals. The one-dimensional SDE algorithm was previously shown to be more efficient in detecting amplitude variations and in computational calculations (compared to other entropy algorithms). Additionally, we also extracted first-order statistical parameters like standard deviation (SD), and mean of positive pixels (MPP), among others. These MSDE<span><math><msub><mrow></mrow><mrow><mn>2</mn><mi>D</mi></mrow></msub></math></span> and first-order texture features were used to detect TB in each lung individually. The MSDE<span><math><msub><mrow></mrow><mrow><mn>2</mn><mi>D</mi></mrow></msub></math></span> was validated using a synthetic dataset and optimized for the best set of parameters. We verified that, for both lungs, the MSDE<span><math><msub><mrow></mrow><mrow><mn>2</mn><mi>D</mi></mrow></msub></math></span> values were significantly different between healthy and TB CXR images (<span><math><mrow><mi>P</mi><mo>&lt;</mo><mn>0</mn><mo>.</mo><mn>05</mn></mrow></math></span>), and the effect size was <span><math><mo>|</mo></math></span>d<span><math><mo>|</mo></math></span> <span><math><mo>&gt;</mo></math></span>0.23. From the first-order parameters, only the mean, SD, entropy, and MPP were statistically different between both groups for the left lung (<span><math><mrow><mi>P</mi><mo>&lt;</mo><mn>0</mn><mo>.</mo><mn>05</mn></mrow></math></span>; <span><math><mo>|</mo></math></span>d<span><math><mo>|</mo></math></span> <span><math><mo>&gt;</mo></math></span>0.22). For the right lung, all first-order features significantly differentiated TB patients (<span><math><mrow><mi>P</mi><mo>&lt;</mo><mn>0</mn><mo>.</mo><mn>05</mn></mrow></math></span>; <span><math><mo>|</mo></math></span>d<span><math><mo>|</mo></math></span> <span><math><mo>&gt;</mo></math></span>0.28). Finally, we show that a multi-layer perceptron obtained 86.4 and 85.2% accuracy in detecting TB in the left and right lungs, respectively. The highest sensitivity values achieved in this study were 71.4% and 81.8% for the left and right lungs, respectively.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"111 ","pages":"Article 108346"},"PeriodicalIF":4.9,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144614750","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":"Effective muscle synergies extraction pipeline to characterise the Box and Block Test movement","authors":"Emma Colamarino ,&nbsp;Jlenia Toppi ,&nbsp;Floriana Pichiorri ,&nbsp;Valeria de Seta ,&nbsp;Giulia Savina ,&nbsp;Ilaria Mileti ,&nbsp;Eduardo Palermo ,&nbsp;Donatella Mattia ,&nbsp;Febo Cincotti","doi":"10.1016/j.bspc.2025.108252","DOIUrl":"10.1016/j.bspc.2025.108252","url":null,"abstract":"<div><div>The Box and Block test (BBT) is a clinical test for the evaluation of the unilateral manual dexterity. To the best of our knowledge, no study has yet explored it through the muscle synergy approach. Since each analysis step impacts on the muscle synergies influencing the results interpretation, this study aims at i) optimising the procedure of muscular synergies extraction in BBT and ii) applying it to characterise BBT in healthy individuals. Electromyographic (EMG) data (8 muscles per arm) were recorded from 16 participants during the BBT performed with each upper limb, separately. Muscle synergies were extracted by means of the Non-Negative Matrix Factorization method and the impact of following two parameters was estimated: (i) cut-off frequency of low-pass filter designed to compute the envelope of the EMG signals (ii) type of normalisation to be applied to the EMG envelopes. Results show that the procedure of muscular synergies extraction requires to be optimised with particular attention to the cut-off frequency of low-pass filter designed to compute the envelope of the EMG signals and the type of normalisation to be applied to the EMG envelopes. From the physiological perspective, three synergies seem to be enough to capture main BBT movement mechanisms. The evaluation of the impact of processing steps on the muscle synergies and the characterization of the BBT will pave the way for the definition of a standard procedure to be applied also in a pathological context.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"110 ","pages":"Article 108252"},"PeriodicalIF":4.9,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144623669","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":"Cross-subject seizure detection with vision transformer and unsupervised domain adaptation","authors":"Hailing Feng,&nbsp;Shuai Wang,&nbsp;Hongbin Lv,&nbsp;Chenxi Nie,&nbsp;Wenqian Feng,&nbsp;Hao Peng,&nbsp;Yanna Zhao","doi":"10.1016/j.bspc.2025.108341","DOIUrl":"10.1016/j.bspc.2025.108341","url":null,"abstract":"<div><div>Automatic seizure detection is of critical importance for clinical epilepsy treatment. Due to the variability of Electroencephalography (EEG) patterns across different individuals, most existing seizure detection methods fails to generalize across patients. To tackle this issue, this paper proposes a cross-subject seizure detection combines Vision Transformer (ViT) and unsupervised domain adaptation (UDA). Specifically, to enhance the generalization ability of the ViT backbone across different subjects, an adversarial network is introduced on the class token to disentangle global transferable features. Meanwhile, the multi-head attention mechanism is replaced by the transfer adaptation module (TAM) to disentangle transferable features at the patch level. Additionally, to retain the discriminative features related to epileptic seizures, a discriminative clustering module (DCM) is introduced to constrain the model. Our experiments on the CHB-MIT dataset demonstrate that the proposed method achieves strong performance in both evaluation paradigms: in epoch-based analysis it attains 89.20% accuracy, 91.05% sensitivity, and 94.54% specificity, while in event-based evaluation it maintains 89.23% sensitivity with a low false detection rate of 0.42/h. The results verify the feasibility of this method in cross-subject seizure detection.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"111 ","pages":"Article 108341"},"PeriodicalIF":4.9,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144632592","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}