Biomedical Signal Processing and Control最新文献

{"title":"Medical priors-guided feature learning network on multimodal imaging raw data for brain tumor segmentation","authors":"Yingying Feng ,&nbsp;Weiguang Wang ,&nbsp;Xuanyi Zhang ,&nbsp;Yi Jing ,&nbsp;Jingao Xu ,&nbsp;Moyu Xia ,&nbsp;Wei Cai ,&nbsp;Xia Zhang","doi":"10.1016/j.bspc.2025.107855","DOIUrl":"10.1016/j.bspc.2025.107855","url":null,"abstract":"<div><div>Mainstream brain tumor segmentation methods require skull stripping, which can inadvertently remove adjacent tumor lesions and reduce accuracy. To address this, we propose MPGNet, which directly uses raw multimodal imaging data for segmentation. Guided by medical prior information, it effectively avoids skull interference and improves accuracy. Specifically, to alleviate skull interference and misidentification, we design a relevant graph aggregation (RGA) module that enhances feature representations by leveraging the structural characteristics of the brain. Then, to reduce confusion among different regions in the prediction results, we define a prior density loss (PDL) function using brain tumor density information from multimodal imaging. Finally, to evaluate our method, we collect skull-stripped brain tumor segmentation challenge (BRATS) data, their corresponding Cancer Genome Atlas (TCGA) raw data, and actual clinical raw data annotated by experienced radiologists. Our experiments demonstrate that MPGNet is effective at preserving tumor integrity compared to other state-of-the-art brain tumor segmentation methods that require skull stripping, improving the Dice similarity coefficient by 4.27%. Additionally, when all models are trained and tested with raw data, MPGNet outperforms the best existing model by 1.05% Dice, showcasing superior performance in handling skull interference.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"107 ","pages":"Article 107855"},"PeriodicalIF":4.9,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143746597","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":"DRL-ECG-HF: Deep reinforcement learning for enhanced automated diagnosis of heart failure with imbalanced ECG data","authors":"Bochao Zhao ,&nbsp;Zhenyue Gao ,&nbsp;Xiaoli Liu ,&nbsp;Zhengbo Zhang ,&nbsp;Wendong Xiao ,&nbsp;Sen Zhang","doi":"10.1016/j.bspc.2025.107680","DOIUrl":"10.1016/j.bspc.2025.107680","url":null,"abstract":"<div><div>Heart failure (HF) is a prevalent cardiovascular condition requiring accurate and timely diagnosis for effective management. Electrocardiogram (ECG) data, as a non-invasive diagnostic resource, provides crucial temporal–spatial information essential for HF diagnosis. However, traditional automated systems struggle with the temporal–spatial complexity and class imbalance of ECG data. To address these challenges, we propose DRL-ECG-HF, a deep reinforcement learning (DRL)-based multi-instance model for enhanced HF diagnosis. By treating each ECG recording as a bag of instances and analyzing individual segments, the model captures fine-grained features related to HF. To mitigate data imbalance, we introduce a DRL strategy incorporating prioritized experience replay (PER), assigning different rewards to minority class instances. The SHapley Additive exPlanations (SHAP) technique is applied to enhance interpretability, providing clinicians insights into the model’s decision-making. The proposed method was validated on the MIMIC-IV-ECG dataset with 12-lead, 10-second ECG samples from 154,934 patients and compared against various methods, including techniques for handling imbalanced data and state-of-the-art time-series classification approaches. The DRL-ECG-HF model achieved an AUROC of 0.90, an F-measure of 0.58, and a G-mean of 0.80, significantly outperforming existing methods. Additionally, it demonstrated superior performance using 12-lead ECG data compared to single-lead, emphasizing the value of comprehensive temporal–spatial information. These results highlight the potential of DRL-ECG-HF as a reliable tool for improving HF diagnosis accuracy and interpretability, paving the way for clinical adoption.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"107 ","pages":"Article 107680"},"PeriodicalIF":4.9,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143746667","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":"Optic disc and cup segmentation methods for glaucoma detection using twin- inception transformer hinge attention network with cycle consistent convolutional neural network","authors":"C. Rekha ,&nbsp;K. Jayashree","doi":"10.1016/j.bspc.2025.107844","DOIUrl":"10.1016/j.bspc.2025.107844","url":null,"abstract":"<div><div>One of the primary sources of blindness worldwide is glaucoma and can only be treated if detected early. This study’s goal is to design a comprehensive scheme for the glaucoma classification incorporating advanced approaches for extracting attributes and segmentation. To begin with, the optic disc and cup are well segmented from the retinal pictures with the Pufferfish Optimization Algorithm (POA). Due to POA, it becomes very easy to more accurately define the area of the optic disc and cup which in turn helps in glaucoma diagnosis depending on the severity. Joining the state-of-the-art neural network designs for attributes extraction and categorization, a new hybrid deep learning (DL) method is described. In the developed model, the Primary Inception Transformer, Hinge Attention Network, and Cycle-Consistent Convolutional Neural Network (Cycle-Consistent CNN) are in fusion with the Human Memory Optimization Algorithm (HMOA). The Twin-Inception Transformer captures intricate spatial interactions in retinal images by utilizing transformer processes, while the Hinge Attention Network fortifies feature learning by a dynamic attention model. In incurred to enhance the training process, HMOA replicates the human memory consolidation process to increase the trainees’ retention and reliability. This combined approach enhances the model’s capability of generalization while still preserving the highest quality of features extracted. The usefulness of the indicated architecture has been proved in experiments using the freely available glaucoma datasets. When compared with today’s benchmark techniques the presented work yields a better performance such as 99.7% accuracy, and 99.5% precision.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"107 ","pages":"Article 107844"},"PeriodicalIF":4.9,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143724429","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":"Optimized YOLOv11 model for lung nodule detection","authors":"Zichao Liu ,&nbsp;Lili Wei ,&nbsp;Tingqiang Song","doi":"10.1016/j.bspc.2025.107830","DOIUrl":"10.1016/j.bspc.2025.107830","url":null,"abstract":"<div><h3>Objectives</h3><div>This study proposes an advanced YOLOv11-based lung nodule detection algorithm that balances high accuracy with efficient computation, addressing the critical need for accurate and timely early diagnosis of lung cancer.</div></div><div><h3>Methods</h3><div>We replaced the traditional backbone with MobileNetV4, which employs reversible connections to prevent information loss and enhance feature representation, thereby improving the model’s efficiency in processing high-resolution CT scans. We developed a novel C2PSA module, C2PSA-MSDA, which integrates Multi-Scale Dilation Attention (MSDA) to capture multi-scale features more effectively. For the neck part, we introduced the new FreqFusion-BiFPN to enhance feature integration and boundary clarity, thereby reducing false positives. Additionally, we created a new C3k2 module, DyC3k2, to optimize feature fusion. We adopted Focal-inv-IoU for bounding box regression and Slide Loss for classification, which help the model focus more on high-quality predictions while still considering lower-quality ones, leading to more balanced and accurate detection.</div></div><div><h3>Results</h3><div>Extensive experiments on the LUNAR16 dataset and a proprietary dataset demonstrated significant improvements: precision increased by 4.15 %, recall by 3.23 %, mAP50 by 4.04 %, and mAP50-95 by 3.28 % compared to the baseline YOLOv11. These gains were achieved with a smaller model size (5.08 MB) and a processing speed of 135.2 frames per second (f/s). The model also performed well on the proprietary dataset, demonstrating strong generalization.</div></div><div><h3>Conclusion</h3><div>The results indicate that the improved algorithm achieves higher accuracy, real-time performance, and better generalization in lung nodule detection, highlighting its potential for clinical application in the early lung cancer diagnosis.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"107 ","pages":"Article 107830"},"PeriodicalIF":4.9,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143734971","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":"Unveiling the abusive head trauma and Shaken Baby Syndrome: A comprehensive wavelet analysis","authors":"Sebastian Glowinski ,&nbsp;Alina Głowińska","doi":"10.1016/j.bspc.2025.107862","DOIUrl":"10.1016/j.bspc.2025.107862","url":null,"abstract":"<div><h3>Background</h3><div>Abusive Head Trauma (AHT) and Shaken Baby Syndrome (SBS) represent severe forms of child abuse with devastating consequences, including profound neurological damage and, in some cases, death. Despite advances in medical imaging and clinical assessments, diagnosing these injuries remains a formidable challenge due to their intricate and multifaceted nature.</div></div><div><h3>Objective</h3><div>This research explores the application of wavelet analysis, a sophisticated signal processing method, to improve the detection and comprehension of AHT and SBS. By leveraging this technique, the study aims to enhance diagnostic accuracy and provide deeper insights into the biomechanical mechanisms underlying these injuries.</div></div><div><h3>Results</h3><div>The analysis revealed intense, rapid oscillations in the forehead and back of the head, suggesting violent shaking, while the sternum showed less pronounced oscillations, indicating gentler motion. The wavelet analysis pinpointed frequencies between 6 and 12 Hz in the head, with lower frequencies for the sternum, shedding light on the distinct ways different parts of the body respond to these forces. Simulated free-fall impacts further revealed significant rotational and linear accelerations, with sharp peaks in both the forehead and sternum. These findings are crucial for understanding the injury mechanisms. Additionally, wavelet transfer function analysis highlighted the synchronized movements and energy transfer between body parts, with frequency responses varying based on the impact surface.</div></div><div><h3>Conclusion</h3><div>This study sheds light on the intricate biomechanical dynamics of infants during episodes of shaking and impact. It underscores the need for continued research to refine our understanding of these injury mechanisms and to inform more effective prevention and intervention strategies for protecting vulnerable populations.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"107 ","pages":"Article 107862"},"PeriodicalIF":4.9,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143724430","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":"SAStainDiff: Self-supervised stain normalization by stain augmentation using denoising diffusion probabilistic models","authors":"Huaishui Yang ,&nbsp;Mengye Lyu ,&nbsp;Shiyue Yan ,&nbsp;Tianzhao Zhong ,&nbsp;Jihao Li ,&nbsp;Tong Xu ,&nbsp;Huhan Xie ,&nbsp;Shaojun Liu","doi":"10.1016/j.bspc.2025.107861","DOIUrl":"10.1016/j.bspc.2025.107861","url":null,"abstract":"<div><div>With the development of computer-aided detection/diagnosis, histopathological images become increasingly important for cancer diagnosis and prognosis. However, different stain styles in histopathological images arise from the difference in stain techniques, operator skills, and scanner specifications. These stain styles reduce the robustness of computer-aided detection/diagnosis algorithms. Existing stain normalization methods often suffer from poor generalization ability and the issue of information loss. In this paper, we propose a new self-supervised diffusion probabilistic modeling approach for stain normalization with stain augmentation training strategy and rescheduled sampling strategy, termed SAStainDiff. Specifically, we employ stain augmentation to simulate different stain styles and learn any stain distribution through diffusion models in a self-supervised manner while preserving the histopathological structure. We employ rescheduled sampling strategy that selects fewer sampling step sizes and a different initial sampling point. This reduces the inference time, which is comparable to mainstream methods, while keeping the performance. We conduct experiments on mutual stain normalization between breast cancer images scanned by two different scanners. Additionally, we explore the application of stain normalization in lymphoma classification and colon gland segmentation. Experimental results demonstrate that our method exhibits excellent generalization capabilities and adapts well to different tissue textures and stain styles without retraining, achieving satisfactory performance in terms of both speed and quality. Our proposed SAStainDiff method can improve the accuracy of disease diagnosis and subsequent analysis, ultimately benefiting clinical practice and advancing medical research. The code and sample data are publicly available on <span><span>GitHub</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"107 ","pages":"Article 107861"},"PeriodicalIF":4.9,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143724766","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":"Exploring dynamic hubs for face perception in the brain: A graph theoretical measure approach","authors":"Shefali Gupta,&nbsp;Tapan Kumar Gandhi","doi":"10.1016/j.bspc.2025.107863","DOIUrl":"10.1016/j.bspc.2025.107863","url":null,"abstract":"<div><div>The human brain operates as a highly complex system, characterized by extensive communication among various sub-networks while perceiving a face. The challenge lies in identifying the distinct active modules responsible while executing the task of face perception within the human brain. Here, we have attempted to investigate the dynamics of hubs in face perception networks using graph measure analysis. EEG data was acquired from 15 healthy subjects while presenting the face-object paradigm to participants. Hub-related measures (transitivity, modularity, characteristic path length, global efficiency) and centrality measures (betweenness, closeness, eigenvector centrality, participation coefficient) are evaluated over time after stimulus onset. These measures are also evaluated across different EEG frequency bands and over the time length of stimuli at each frequency band. Our findings revealed that the processing of face perception in the brain unfolds, exhibiting information processing in both intra-module and inter-modules. Moreover, we identified community networks dedicated to face processing in the brain over time and in different frequency bands, illustrating the evolving nature of these communities following stimulus onset. This comprehensive exploration delves into the brain network dynamics of face perception in the human brain and sheds light on their relevance in understanding neurological disorders and cognitive functions.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"107 ","pages":"Article 107863"},"PeriodicalIF":4.9,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143724431","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":"Switch fusion for continuous emotion estimation from multiple physiological signals","authors":"Ngoc Tu Vu ,&nbsp;Van Thong Huynh ,&nbsp;Seung-Won Kim ,&nbsp;Ji-eun Shin ,&nbsp;Hyung-Jeong Yang ,&nbsp;Soo-Hyung Kim","doi":"10.1016/j.bspc.2025.107831","DOIUrl":"10.1016/j.bspc.2025.107831","url":null,"abstract":"<div><div>Physiological signals represent a robust foundation for affective computing, primarily due to their resistance to conscious manipulation by subjects. With the proliferation of applications such as safe driving, mental health treatment, and wearable wellness technologies, emotion recognition based on physiological signals has garnered substantial attention. However, the increasing variety of signals captured by diverse sensors poses a challenge for models to integrate these inputs and accurately predict emotional states efficiently. Determining an optimized fusion strategy becomes increasingly complex as the number of signals grows. To address this, we propose switch fusion, a dynamic allocation fusion algorithm designed to dynamically enable models to learn optimal fusion strategies of multiple modalities. Leveraging the mixture of experts’ frameworks, our approach employs a gating mechanism to route modalities to specialized experts, utilizing these experts as fusion encoder modules. Furthermore, we demonstrate the effectiveness of time series-based models in processing physiological signals for continuous emotion estimation to enhance computational efficiency. Experiments conducted on the continuously annotated signals of emotion dataset highlight the effectiveness of switch fusion, achieving root mean square errors of 1.064 and 1.089 for arousal and valence scores, respectively, surpassing state-of-the-art methods in 3 out of 4 experimental scenarios. This study underscores the critical role of dynamic fusion strategies in continuous emotion estimation from diverse physiological signals, effectively addressing the challenges posed by the increasing complexity of sensor inputs.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"107 ","pages":"Article 107831"},"PeriodicalIF":4.9,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143724765","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":"MFIFN: A multimodal feature interaction fusion network-based model for Alzheimer’s disease classification","authors":"Yibo Huang ,&nbsp;Jie Liu ,&nbsp;Zhiyong Li ,&nbsp;Qiuyu Zhang","doi":"10.1016/j.bspc.2025.107857","DOIUrl":"10.1016/j.bspc.2025.107857","url":null,"abstract":"<div><div>The classification of Alzheimer’s disease (AD) and the identification of abnormal connections in brain networks have important research implications. Existing classification methods are mainly based on fMRI and sMRI features of brain regions, ignoring the multimodal fusion information of the brain, which has some limitations. Therefore, in this paper, we utilize the time course (TC) of fMRI decomposition with the complementary information between the two, as well as the interactive fusion with sMRI, to learn the multifaceted representational information of the brain, and propose a multimodal feature interaction fusion network (MFIFN) framework that fuses the brain connectivity and activity information. The framework aims to improve the accuracy of brain disease classification through temporal consistency and the combined use of fMRI and sMRI data. A CNN-AM module was designed to process the TC data to extract the time dependence, with a three-layer GRU providing the interpretability of the model. The TC data were processed by PCA downscaling, and the complementarity with the fMRI data was obtained by cross-using the HAN module to obtain the complementary information of both. The GCN uses the information for feature propagation and learning, and the final decision is obtained by the fully connected layer. The effectiveness of MFIFN was verified on the ADNI dataset, achieving a high classification accuracy of AD and NC (99.3%). The results show that the method proposed in this paper is effective in identifying different brain networks in AD patients, which provides biological interpretability for AD diagnosis.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"107 ","pages":"Article 107857"},"PeriodicalIF":4.9,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143714127","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":"Early detection of Parkinson’s disease based on beta dynamic features and beta-gamma coupling from non-invasive resting state EEG: Influence of the eyes","authors":"G. Gimenez-Aparisi ,&nbsp;E. Guijarro-Estelles ,&nbsp;A. Chornet-Lurbe ,&nbsp;M. Diaz-Roman ,&nbsp;Dongmei Hao ,&nbsp;Guangfei Li ,&nbsp;Y. Ye-Lin","doi":"10.1016/j.bspc.2025.107868","DOIUrl":"10.1016/j.bspc.2025.107868","url":null,"abstract":"<div><div>Resting state electroencephalography (EEG) has been shown to provide relevant information for detecting neuropathological changes of the brain’s electrical activity in neurodegenerative patients. Studies conducted on local field potential recordings have shown that exaggerated beta oscillations and abnormally high beta-gamma phase amplitude coupling (PAC) are hallmark Parkinson’s disease (PD) signatures. Extracting beta bursts from non-invasive magnetoencephalography has also been found to be feasible, as it provides a better signal-to-noise ratio than electroencephalography and is less affected by volume conduction.</div><div>It is still unclear whether beta burst dynamic features and beta-gamma PAC from resting state EEG can be used to assess the progress of PD. In the present study, it has been proposed to assess the potential utility of beta burst dynamic and the beta-gamma PAC to discriminate PD patients from healthy subjects, as well as their relationship with clinical symptoms. Resting state EEG data have been analysed in both eyes closed (EC) and open (EO) and reactivity-to-eyes opening (REO) of a public database consisting of 20 healthy and 13 Parkinson patients. Beta burst events from EEG spectrograms were extracted to determine their dynamic features, i.e. burst duration, rate, peak frequency, spectral bandwidth and power as well as the normalized beta-gamma PAC. Permutation test while controlling the family-wise error rate was used to assess statistical significance. The results indicate that REO is more sensitive than EC and EO alone, and also that the higher variability of burst duration is linked to PD, while the lower burst rate is negatively correlated with clinical symptoms. PD patients had a higher periodicity of duration in the left frontal area, and a higher periodicity of peak frequency, spectral bandwidth and power of the bursts in the left central area than healthy subjects, together with a significant positive correlation with clinical symptoms.</div><div>Beta-gamma PAC not only found abnormalities in the central regions but also in the frontal, fronto-central, parietal and occipital regions, suggesting impaired motor, working memory and visuospatial skills. It was also possible to extract beta burst dynamic features and the beta-gamma PAC from resting state EEG and that these provided reliable PD progress biomarkers. These advances are expected to help clinicians design patient-personalised therapies and improve their quality of life.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"107 ","pages":"Article 107868"},"PeriodicalIF":4.9,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143714204","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}