IEEE Journal of Biomedical and Health Informatics最新文献

{"title":"STAND-Net: A Spiking Temporal Attention autoeNcoDer Network for Efficient EEG Artifact Removal","authors":"Ruizhi Zhang;Xiaoyu Guo;Yu Pan;Shuo Gao","doi":"10.1109/JBHI.2026.3670141","DOIUrl":"10.1109/JBHI.2026.3670141","url":null,"abstract":"Electroencephalography (EEG)-based brain–computer interface (BCI) systems hold significant promise across diverse applications; however, their performance is compromised by pervasive physiological artifacts that degrade signal fidelity. While current deep neural networks (DNNs) improve artifact rejection, their high computational cost precludes deployment in wearable BCIs systems. Here, we introduce STAND-Net (Spiking Temporal Attention autoeNcoDer Network), a neuromorphic architecture that leverages event-driven spiking neurons to achieve ultra-efficient, high-fidelity EEG artifact removal. STAND-Net combines a spike-convolution encoder-decoder with leaky integrate-and-fire neurons to model spatiotemporal EEG dynamics, a dilation-enhanced residual backbone capturing long-range dependencies, and a spike-rate attention mechanism dynamically localizing artifacts via neuronal firing patterns. The system demonstrates >3.7 dB improvement in signal-to-distortion ratio over state-of-the-art methods across diverse artifacts while consuming 97.98% less power than comparable DNNs. Crucially, downstream BCI classification accuracy increased by 6.64% using STAND-Net-processed signals. This work establishes a neuromorphic framework for low-power and high quality EEG artifact removal in wearable BCI systems.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"30 10","pages":"8492-8505"},"PeriodicalIF":7.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147354810","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":"TMN-LAKDE: Characterizing Sleep Instability via Prediction Intervals of Dynamic EEG Spectral Networks","authors":"Fuzhen Wei;Xiaoyu Zou;Jie Pan;Pengjun Zhao;Jiajing Song;Liwen Zhang","doi":"10.1109/JBHI.2026.3677739","DOIUrl":"10.1109/JBHI.2026.3677739","url":null,"abstract":"Sleep instability is a typical characteristic of insomnia, manifested as the inability of the brain to maintain a stable state, but its precise quantification is still challenging. We assume that sleep instability fundamentally reflects an increase in unpredictability in the evolution of brain network dynamics. To verify this, an interval prediction framework combining the temporal mobile network (TMN) and local adaptive kernel density estimation (LAKDE) is proposed to characterize the sleep instability. Specifically, TMN predicts future network states, while LAKDE module quantifies the uncertainty of these predictions by generating prediction intervals (PIs). Experiments on SIESTA and Sleep-EDF databases have shown that this method can construct well calibrated PIs. The key finding is that the PI normalized average width (PINAW) of subjects with sleep disorders is significantly higher than that of the healthy control group, validating that wider PIs are a mechanistic biomarker of sleep instability. In addition, this study further revealed a significant correlation between PINAW and traditional indicators such as number of sleep stage transitions, indicating that dynamic instability based on prediction uncertainty shares a common physiological basis with sleep fragmentation phenomena, establishing interval prediction as a paradigm for quantifying sleep instability.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"30 10","pages":"8557-8569"},"PeriodicalIF":7.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147511739","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":"MOSAIC: A Multi-Granularity Cross-Modal Framework for Predicting Synergistic Drug Combinations in Personalized Healthcare","authors":"Licai Zhang;Xiao Kang;Xinxing Yang;Lin Wang;Genke Yang;Jian Chu","doi":"10.1109/JBHI.2025.3605313","DOIUrl":"10.1109/JBHI.2025.3605313","url":null,"abstract":"The personalization of cancer treatment through drug combinations is critical for improving healthcare outcomes, increasing effectiveness, and reducing side effects. Computational methods have become increasingly important to prioritize synergistic drug pairs because of the vast search space of possible chemicals. However, existing approaches typically rely solely on global molecular structures, neglecting information exchange between different modality representations and interactions between molecular and fine-grained fragments, leading to limited understanding of drug synergy mechanisms for personalized treatment. To address these limitations, we propose MOSAIC (<bold><u>M</u></b>ulti-granularity cr<bold><u>OS</u></b>s-mod<bold><u>A</u></b>l method for synerg<bold><u>I</u></b>stic drug combinations predi<bold><u>C</u></b>tion), an AI-driven multi-granularity cross-modal method for personalized synergistic drug combination prediction that considers both molecular and fragment-level features. MOSAIC employs a dual-layer representation system, decomposing molecules into chemically meaningful fragments using the BRICS algorithm, facilitating information exchange between graph and SMILES representations through a bidirectional cross-attention mechanism, and ensuring semantic consistency of different modal representations of the same molecular fragment through a contrastive learning framework. Additionally, we designed a bilinear attention network to capture interactions between fragments of different drugs and dynamically integrate multi-granularity feature relationships through a multi-head attention mechanism. Through extensive experiments on multiple real-world datasets, MOSAIC demonstrates superior performance over state-of-the-art methods. Literature validation confirms its predicted novel drug combinations align with existing clinical evidence, while visualization analyses elucidate its capability to pinpoint key molecular fragments critical for drug synergy, providing valuable insights for personalized treatment planning and remote patient monitoring.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"30 10","pages":"8166-8177"},"PeriodicalIF":7.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145000407","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":"Deconver: A Deconvolutional Network for Medical Image Segmentation","authors":"Pooya Ashtari;Shahryar Noei;Fateme Nateghi Haredasht;Jonathan H. Chen;Giuseppe Jurman;Aleksandra Pižurica;Sabine Van Huffel","doi":"10.1109/JBHI.2025.3623381","DOIUrl":"10.1109/JBHI.2025.3623381","url":null,"abstract":"While convolutional neural networks (CNNs) and vision transformers (ViTs) have advanced medical image segmentation, they face inherent limitations such as local receptive fields in CNNs and high computational complexity in ViTs. This paper introduces Deconver, a novel network that integrates traditional deconvolution techniques from image restoration as a core learnable component within a U-shaped architecture. Deconver replaces computationally expensive attention mechanisms with efficient nonnegative deconvolution (NDC) operations, enabling the restoration of high-frequency details while suppressing artifacts. Key innovations include a backpropagation-friendly NDC layer based on a provably monotonic update rule and a parameter-efficient design. Evaluated across five datasets (ISLES’22, Spleen, BraTS’23, GlaS, and FIVES) covering both 2D and 3D segmentation tasks, Deconver achieves state-of-the-art performance in Dice scores and Hausdorff distance while reducing computational costs (FLOPs) by up to 90% compared to leading baselines. By bridging traditional image restoration with deep learning, this work offers a practical solution for high-precision segmentation in resource-constrained clinical workflows.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"30 10","pages":"8356-8368"},"PeriodicalIF":7.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11265864","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145596301","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":"Multi-Feature Fusion and Compressed Bi-LSTM for Memory-Efficient Heartbeat Classification on Wearable Devices","authors":"Reza Nikandish;Jiayu He;Benyamin Haghi","doi":"10.1109/JBHI.2026.3669719","DOIUrl":"10.1109/JBHI.2026.3669719","url":null,"abstract":"In this paper, we present a memory-efficient ECG-based heartbeat classification for wearable devices enabled by multi-feature fusion and compressed bidirectional long short-term memory (Bi-LSTM). A multi-feature fusion technique based on <italic>time intervals</i> and <italic>under-the-curve areas</i> is proposed to extract the main characteristic points of the ECG waveform with high accuracy and robustness against noise and artifacts. A Bi-LSTM network is developed to process the input sequence in both forward and backward directions, resulting in higher accuracy and a 28% smaller network size compared with a conventional LSTM. Multiple neural networks with varying sizes, including tiny (84 k), small (150 k), medium (478 k), and large (1.25 M) models, are developed to achieve high accuracy across all classes. The overall accuracy is 96.4% for the large model and 94.6% for the tiny model, while the F1 score across all classes exceeds 89.1% and 85.1%, respectively. The proposed models, compressed using post-training quantization techniques, achieve state-of-the-art performance. The compressed large model with 8-bit integer quantization (INT8) achieves an accuracy of 88.4% with 1.3 MB of memory, while the compressed tiny model with dynamic range quantization achieves 94.6% accuracy with only 139 kB of memory.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"30 10","pages":"8897-8906"},"PeriodicalIF":7.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147467968","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":"Postoperative Recovery Assessment for Parkinson's Patients via Light-Weighted Topological Pose Estimation","authors":"Zeping Ma;Zhiyao Qin;Botao Jiang;Guosong Zhu;Zhen Qin;Ji Geng;Mohammed J. F. Alenazi;Saru Kumari","doi":"10.1109/JBHI.2025.3559493","DOIUrl":"10.1109/JBHI.2025.3559493","url":null,"abstract":"The UPDRS III scale plays a critical role in diagnosing the progression of Parkinson's disease. Current methods often involve doctors guiding patients through specific actions on the scale, recording their performance, and assigning scores. However, this approach has several drawbacks, including the lengthy time required for doctor-patient communication, the high costs of patients traveling to hospitals for follow-up visits, and the reliance on subjective judgments from doctors, which lack standardized criteria. With advancements in artificial intelligence, many traditional processes have been partially automated. To help patients reduce diagnosis time, lower medical costs, and provide more accurate and objective evaluation results, this paper proposes a Transformer-based pose estimation model for assessing UPDRS III scale actions. By integrating skeleton-based evaluations from the network with a series of post-processing operations, the model enables patients to perform self-assessments of their post-treatment recovery at home, saving doctors significant time. This work introduces a cascaded graph self-attention module, SGAM (Spatial-Graphical Attention Module), to enhance the network's understanding of human topology. Additionally, it proposes a lightweight convolutional block, Chi-block, which employs a novel approach leveraging the attribute invariance of filters to interpret model performance and guide compression. This approach reduces computational costs and model parameters while preserving accuracy. The proposed method demonstrates robust performance on human pose estimation (HPE) datasets and showcases impressive lightweight performance on benchmark datasets such as ImageNet-1 K and CIFAR-10. These results demonstrate the potential of artificial intelligence in enabling automated remote diagnosis and treatment for Parkinson's patients.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"30 10","pages":"8254-8264"},"PeriodicalIF":7.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143990808","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":"DPGNet: A Boundary-Aware Medical Image Segmentation Framework via Uncertainty Perception","authors":"Huafeng Wang;Yong Qi;Wanquan Liu;Kexin Guo;Weifeng Lv;Zhengrong Liang","doi":"10.1109/JBHI.2025.3601025","DOIUrl":"10.1109/JBHI.2025.3601025","url":null,"abstract":"Addressing the critical challenge of precise boundary delineation in medical image segmentation, we introduce DPGNet, an adaptive deep learning model engineered to emulate expert perception of intricate anatomical edges. Our key innovations drive its superior performance and clinical utility, encompassing: 1) a three-stage progressive refinement strategy that establishes global context, performs hierarchical feature enhancement, and precisely delineates local boundaries; 2) a novel Edge Difference Attention (EDA) module that implicitly learns and quantifies boundary uncertainties without requiring explicit ground truth supervision; and 3) a lightweight, transformer-based architecture ensuring an exceptional balance between performance and computational efficiency. Extensive experiments across diverse and challenging medical image datasets demonstrate DPGNet’s consistent superiority over state-of-the-art methods, notably achieving this with significantly lower computational overhead (25.51 M parameters). Its exceptional boundary refinement is rigorously validated through comprehensive metrics (Boundary-IoU, HD95) and confirmed by rigorous clinical expert evaluations. Crucially, DPGNet generates an explicit uncertainty boundary map, providing clinicians with actionable insights to identify ambiguous regions, thereby enhancing diagnostic precision and facilitating more accurate clinical segmentation outcomes.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"30 10","pages":"8302-8314"},"PeriodicalIF":7.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144952109","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":"Contactless Acquisition and Decoupling of Respiratory and Cardiac Signals Using a 122 GHz FMCW Radar","authors":"Laura Miro;Ruochen Wu;Albert Aguasca;Antoni Broquetas;Cosme Garcia;Oriol Estrada;Montse Najar","doi":"10.1109/JBHI.2026.3673391","DOIUrl":"10.1109/JBHI.2026.3673391","url":null,"abstract":"This paper presents a contactless method for the simultaneous monitoring of heart rate (HR) and respiratory rate (RR) using a Frequency Modulated Continuous Wave (FMCW) radar operating at 122.5 GHz. To address the challenges posed by spectral overlap and interference between cardiac and respiratory components, we introduce a signal separation and estimation framework based on Maximum Likelihood Estimation (MLE) combined with an orthogonal projection technique. The approach models the thoracic displacement captured by the radar as the superposition of periodic sources and applies spectral projection to isolate the fundamental frequencies and their harmonics. Theoretical performance is assessed through the derivation of the Cramér–Rao Lower Bound (CRLB) and extensive Monte Carlo simulations under varying SNR and sample size conditions. Experimental validation was conducted on 23 healthy volunteers, each providing two 20 s recordings, using a clinical-grade reference monitor. The method achieves high estimation accuracy, with a mean absolute error of 0.69 bpm for HR and 0.33 bpm for RR. The algorithm is capable of reconstructing the cardiac pulse waveform while preserving key morphological features such as the systolic peak and dicrotic notch. Furthermore, the use of the projection technique further enhanced estimation accuracy under nonphysiological conditions, including both elevated and reduced rates. These findings demonstrate the potential of the proposed method as a reliable tool for contactless monitoring of vital signs and support its future integration into biomedical applications.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"30 10","pages":"8917-8929"},"PeriodicalIF":7.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11432837","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147443474","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":"Automated Pediatric Delirium Recognition via Deep Learning-Powered Video Analysis","authors":"Jiarong Chen;Suqin Xia;Wenqi Shi;Yemin Gong;Yali Huang;Lixiang Gu;Xiaoyu Lin;Haibao Chen;Guoxing Wang;Cheng Chen;Liebin Zhao;Wenyi Luo","doi":"10.1109/JBHI.2025.3604448","DOIUrl":"10.1109/JBHI.2025.3604448","url":null,"abstract":"Delirium is an acute, fluctuating state of consciousness disturbance characterized by cognitive alterations and perceptual disturbances. Pediatric delirium has a notably higher incidence rate than adult delirium, and it is time-consuming and labor-intensive for clinicians to analyze, requiring effective recognition approaches. Deep learning has shown potential for the extraction of robust representations and improvement of patient outcomes. In this study, 129 video samples labeled by professional clinicians were collected from multiple hospitals, including 74 non-delirium and 55 delirium labeled samples. An 18-layer deep spatiotemporal convolutional neural network is employed, in which two-dimensional and one-dimensional convolutional filters are applied to individual video frames to extract frame-level and inter-frame-level features, respectively. The entire architecture is pretrained on a large-scale video analysis dataset, and a three-layer fully connected classification head is integrated for the delirium recognition task. The proposed model was fine-tuned with a training dataset and evaluated on a testing dataset, exploring various models and strategies. The proposed algorithm demonstrated robust classification performance, achieving an accuracy of 0.8718, precision of 0.8711, recall of 0.8730, and F1-score of 0.8715, with approximately 31.54 million model parameters. These metric results validate the clinical applicability and technical reliability of the model under various training and testing strategies. In addition, the developed delirium classification model is deployed a hospital system to enable intelligent video diagnosis. The independent test accuracy for 100 newly collected samples is 0.8800. Therefore, the proposed algorithm enables new methods for pediatric delirium recognition and cures.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"30 10","pages":"8178-8189"},"PeriodicalIF":7.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144952173","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":"Waveformer: Dual-Branch Adaptive Network With Wavelet-Guided Cross-Context Decoding for Colorectal Polyp Segmentation","authors":"Liming Liang;Ting Kang;Yulin Li;Jiaming Liang;Xiaoqi Sheng","doi":"10.1109/JBHI.2026.3678353","DOIUrl":"10.1109/JBHI.2026.3678353","url":null,"abstract":"With the advancement of deep learning, polyp segmentation in endoscopic images has achieved remarkable progress. However, clinical polyps often exhibit variable morphology, blurred boundaries, and low contrast with the intestinal mucosa, hindering accurate lesion localization and edge delineation. Moreover, complex conditions of low light, luminal distortion, and mucosal folds further exacerbate the problem with identification, resulting in frequent misdetections and omissions in computer-aided diagnosis. Accordingly, we propose Waveformer, a local-global co-modeling segmentation network, to improve segmentation accuracy. Concretely, the encoder employs parallel CNN-Transformer branches to synergistically extract detailed and global features, thereby enhancing the completeness and discriminative power of the representation. The decoder integrates a wavelet-based frequency decomposition unit (WFDU), a camouflage identification module (CIM), and an information fusion layer (IFL). These modules collaboratively enhance edge responses and semantic aggregation across scales, significantly boosting the framework’s capability in boundary modeling and lesion discernment. Extensive experiments on CVC-ClinicDB and Kvasir-SEG datasets achieve Dice Similarity Coefficients (DSC) of 95.60% and 94.11%, outperforming fourteen state-of-the-art (SOTA) methods. Cross-dataset evaluations further verify its strong generalization ability, with DSC scores of 81.0% and 79.2%, respectively.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"30 10","pages":"8870-8883"},"PeriodicalIF":7.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147528563","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}