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A deep learning method for contactless emotion recognition from ballistocardiogram Biomed. Signal Process. Control (IF 4.9) Pub Date : 2024-09-14 Xianya Yu, Yonggang Zou, Xiuying Mou, Siying Li, Zhongrui Bai, Lidong Du, Zhenfeng Li, Peng Wang, Xianxiang Chen, Xiaoran Li, Fenghua Li, Huaiyong Li, Zhen Fang
Emotion recognition is a major research point in the field of affective computing. Existing research on the application of physiological signals to emotion recognition mainly focuses on the processing of contact signals. However, there are issues with contact signal acquisition equipment, such as limited portability and poor user compliance, which make it difficult to promote its use. To explore a
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Screening diabetic retinopathy and exudates in retinal images using dual functional convolutional neural networks Biomed. Signal Process. Control (IF 4.9) Pub Date : 2024-09-14 T. Geetha, C. Hema
Exudates are the abnormal pattern lesions in Diabetic Retinopathy (DR) images, which is the primary cause of DR in diabetic patients. Therefore, its detection process is essential for further severity estimation of DR images. Automated detection systems’ robustness can be hampered by training datasets that are not representative. In this paper, the DR image is detected and classified using the proposed
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Continuous blood pressure monitoring based on transformer encoders and stacked attention gated recurrent units Biomed. Signal Process. Control (IF 4.9) Pub Date : 2024-09-14 Zhiwen Huang, Jiajie Shao, Panyu Zhou, Baolin Liu, Jianmin Zhu, Dianjun Fang
Continuous blood pressure monitoring (CBPM) is critical to support the accurate prevention and reliable treatment of cardiovascular diseases. To achieve efficient multi-information interaction and further improve the monitoring performance, this research proposes an intelligent model based on transformer encoders and stacked attention gated recurrent units (TE-SAGRU) for CBPM. Long-term multi-source
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Exploring the potential of a new wearable sleep monitoring device for clinical application Biomed. Signal Process. Control (IF 4.9) Pub Date : 2024-09-14 Xin Li, Min Li, Mei Tian, Qi Liu, Xiaomeng Zhou, Hu Liu, Rui Li, Zhenzhong Li, Hui Dong, Lijing Jia, Yaling Liu
Sleep is critical to health and quality of life, and the gold-standard assessment method for sleep disorders--polysomnography (PSG)--has struggled to become widely used because of its inherent shortcomings, which portable sleep-monitoring devices are expected to improve. This study aims to validate a new EEG-, EOG-, and EMG-based portable sleep-monitoring device against conventional PSG to explore
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A2ST-GCM: An adaptive spatio-temporal aware graph convolutional model for predicting pathological complete response in neoadjuvant therapy Biomed. Signal Process. Control (IF 4.9) Pub Date : 2024-09-14 Wanting Yang, Jie Yuan, Juanjuan Zhao, Wei Wu, Yan Qiang
Accurate preoperative prediction of pathological complete response (pCR) following neoadjuvant immunochemotherapy is crucial for refining and customising perioperative treatment decisions. However, the challenge persists in developing reliable, interpretable, and intelligent imaging markers using early-stage computed tomography (CT). Considering the dynamic evolution of tumours during treatment can
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DAAM-Net: A dual-encoder U-Net network with adjacent auxiliary module for pituitary tumor and jaw cyst segmentation Biomed. Signal Process. Control (IF 4.9) Pub Date : 2024-09-13 Hualuo Shi, Xiaoliang Jiang, Chun Zhou, Qile Zhang, Ban Wang
In the diagnosis of various diseases, accurate segmentation of lesions in medical images is crucial. However, when faced with the challenges of blurred edges, noise, and low contrast in images of pituitary tumors and jaw cysts, existing methods do not perform well in solving these problems. To overcome these problems, we present a new approach based on U-Net architecture. Firstly, a more comprehensive
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Explainable deep learning for diabetes diagnosis with DeepNetX2 Biomed. Signal Process. Control (IF 4.9) Pub Date : 2024-09-13 Sharia Arfin Tanim, Al Rafi Aurnob, Tahmid Enam Shrestha, MD Rokon Islam Emon, M.F. Mridha, Md Saef Ullah Miah
Diabetes is a leading health global health challenge because of its high blood sugar levels and the risk of extensive damage to other internal organs. Early and accurate identification of diabetes is important because it may cause other diseases including heart diseases and nerve damage. Despite the success of using machine learning, especially deep learning in automated diabetes diagnosis. These models
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Force-Field based assisted control for upper-limb rehabilitation robots Biomed. Signal Process. Control (IF 4.9) Pub Date : 2024-09-13 Leigang Zhang, Fengfeng (Jeff) Xi, Shuai Guo, Hongliu Yu
Robot-assisted training with an assist-as-needed (AAN) control strategy has been used in clinical investigations to effectively enhance the subject’s active effort and promote the rehabilitation of individuals with upper-limb motor impairment. In this paper, a force field-based AAN control scheme is proposed for robot-assisted upper limb rehabilitation training and then implemented in a robot-assisted
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Diagnose Alzheimer’s disease and mild cognitive impairment using deep CascadeNet and handcrafted features from EEG signals Biomed. Signal Process. Control (IF 4.9) Pub Date : 2024-09-13 Khosro Rezaee, Min Zhu
Alzheimer’s disease (AD) is the most prevalent clinically diagnosed neurodegenerative disorder. Early detection of mild cognitive impairment (MCI) is crucial for implementing effective interventions and potentially preventing further cognitive decline. Due to its efficiency, the electroencephalogram (EEG) is a promising tool for AD diagnosis. This paper proposes a computer-aided diagnostic model for
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Weighted graph convolutional network with feature mask for low back pain prediction Biomed. Signal Process. Control (IF 4.9) Pub Date : 2024-09-13 Zhixin Li, Shiyi Shen, Fanqi Shang, Zhan Huan, Jiuzhen Liang, Ying Chen
Low back pain (LBP) is one of the most common physical disabilities worldwide, imposing a heavy medical economic burden. To help medical experts make disease predictions at an early stage, this paper proposes an LBP abnormality diagnosis model based on a weighted graph convolutional network with feature mask. Unlike popular neural network architectures, we represent the biomechanical characteristics
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Emotion recognition from multichannel EEG signals based on low-rank subspace self-representation features Biomed. Signal Process. Control (IF 4.9) Pub Date : 2024-09-13 Yunyuan Gao, Yunfeng Xue, Jian Gao
In recent years, emotion recognition based on electroencephalogram (EEG) has become the research focus in human–computer interaction (HCI), but deficiencies in EEG feature extraction and noise suppression are still challenging. In this paper, a novel robust low-rank subspace self-representation (RLSR) of EEG is developed for emotion recognition. Instead of using classical time–frequency EEG feature
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Sustainable framework for automated segmentation and prediction of lung cancer in CT image using CapsNet with U-net segmentation Biomed. Signal Process. Control (IF 4.9) Pub Date : 2024-09-13 S.R. Vijayakumar, S. Aarthy, D. Deepa, P. Suresh
Due to the lung’s unregulated cell proliferation, both men and women are commonly affected by lung cancer. As a result, the chest’s inhale and exhale areas have substantial breathing difficulties. The World Health Organisation states that tobacco use and passive smoking are the leading causes of lung cancer. The mortality rate is still not entirely under control even though there are cutting-edge medical
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A novel hybrid deep learning approach with GWO–WOA optimization technique for human activity recognition Biomed. Signal Process. Control (IF 4.9) Pub Date : 2024-09-13 Divya Thakur, Shivangi Dangi, Praveen Lalwani
The effectiveness of Human Activity Recognition (HAR) models can be largely attributed to the components derived from domain expertise. The classification system swiftly and effectively categorizes human physical activity by utilizing a comprehensive collection of variables. To construct HAR models and categorize different activities, deep learning algorithms have recently seen increased application
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LungXpertAI: A deep multi-task learning model for chest CT scan analysis and COVID-19 detection Biomed. Signal Process. Control (IF 4.9) Pub Date : 2024-09-13 Shirin Kordnoori, Maliheh Sabeti, Hamidreza Mostafaei, Saeed Seyed Agha Banihashemi
Addressing the urgent need for accurate COVID-19 identification and lung infection segmentation in CT scans, our study introduces LungXpertAI, a novel Multi-Task Learning architecture. Previous studies on multi-task structures have been limited in providing detailed insights into their architectures. Addressing these details is crucial for enhancing the performance of multi-task structures. Our proposed
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Model based fitting of pattern reversal visually evoked potentials provides a reliable characterization of waveform components Biomed. Signal Process. Control (IF 4.9) Pub Date : 2024-09-13 Carlyn Patterson Gentile, Geoffrey K. Aguirre, Kenneth J. Ciuffreda, Nabin R. Joshi, Kristy B. Arbogast, Christina L. Master
To introduce a novel approach to analyzing pattern reversal visual evoked potentials (prVEPs) using a difference-of-gammas model-based fitting method. prVEP was recorded from uninjured youth ages 11–19 years during pre- or post-season sports evaluation. A difference-of-gammas model fit was used to extract the amplitude, peak time, and peak width of each of four gamma components. The within session
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A correlation analysis between passively assessed gait initiation signal data and brain tumours progress Biomed. Signal Process. Control (IF 4.9) Pub Date : 2024-09-13 Clauirton Siebra, Katarzyna Wac
Magnetic resonance imaging (MRI) is commonly used to diagnosing and monitoring the progress of brain tumours.However, it is costly, not easily accessible, and requires frequent visits to specialised health centres. This study explores the use of passively assessed gait initiation signal data (GISD) as a potential alternative to MRI. We evaluated three hypotheses: (1) Certain features extracted from
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Dynamic MRI reconstruction via multi-directional low-rank tensor regularization Biomed. Signal Process. Control (IF 4.9) Pub Date : 2024-09-13 Shujun Liu, Maolin Lei, Jianxin Cao, Ting Yang
Compressed sensing significantly accelerates dynamic magnetic resonance imaging (MRI) by allowing the exact reconstruction of images from a small number of measurements in (, )-space. The similarity in three different directions of dynamic MRI makes it have a multi-directional low-rank prior. In this paper, we propose a tensor-based multi-dimensional low-rank regularization for dynamic MRI reconstruction
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Multi-task learning for calcaneus fracture diagnosis of X-ray images Biomed. Signal Process. Control (IF 4.9) Pub Date : 2024-09-13 Qingwen Yu, Yuansen Liu, Hongyu Li, Xinwen Liu, Xinlei Bao, Weilin Jin, Wei Xia, Zhenyu Tang, Peifu Tang, Hua Chen, Xu Wang
Fracture diagnosis is critical in clinical settings, and imaging modalities like X-ray and CT scans are crucial for bone fracture diagnosis. Although X-ray scans are more convenient and affordable, they are prone to misdiagnosis and missed diagnosis due to the limited number of poses that can be observed and the uneven distribution of grayscales. Deep learning methods show great potential in improving
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Performance comparison of machine learning algorithms for the estimation of blood pressure using photoplethysmography Biomed. Signal Process. Control (IF 4.9) Pub Date : 2024-09-13 Attilio Di Nisio, Luisa De Palma, Mattia Alessandro Ragolia, Anna Maria Lucia Lanzolla, Filippo Attivissimo
This paper deals with an in-depth performance analysis on the estimation of Systolic Blood Pressure (SBP) and Diastolic Blood Pressure (DBP) by using features from the photoplethysmography (PPG) signal enhanced using the Maximal Overlap Discrete Wavelet Transform (MODWT), to train many machine learning (ML) regression models, including eXtreme Gradient Boost (XGBoost). The impact of different features
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Design a metamaterial based applicator for hyperthermia cancer treatment Biomed. Signal Process. Control (IF 4.9) Pub Date : 2024-09-13 Nitika Sharma, Hari Shankar Singh, Rajesh Khanna, Amanpreet Kaur, Mayank Agarwal
This research describes a metamaterial-based applicator with and without water bolus for cancer treatment. The metamaterial based applicator consists of a double spiral antenna and a slotted square shape artificial magnetic conductor (SSA) structure. The antenna is designed on a low-cost FR4 substrate with dimensions of 32 × 32 × 3.27 mm and the SSA unit cell which behaves as a metamaterial, is designed
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Augmented dialectal speech recognition for AI-based neuropsychological scale assessment in Alzheimer’s disease Biomed. Signal Process. Control (IF 4.9) Pub Date : 2024-09-13 Meiwei Zhang, Qiushi Cui, Wenyuan Li, Weihua Yu, Lihua Chen, Wenjie Li, Chenzhe Zhu, Yang Lü
Alzheimer’s disease (AD) is a prevalent and widespread neurodegenerative disorder among the older adult population worldwide. Among the numerous cognitive screening methods, neuropsychological scale assessments (NSA) are the widely utilized screening tools in clinical practice. The NSA places significant emphasis on speech-related questions, and thus the role of Automatic Speech Recognition (ASR) technology
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Machine learning for ranking f-wave extraction methods in single-lead ECGs Biomed. Signal Process. Control (IF 4.9) Pub Date : 2024-09-13 Noam Ben-Moshe, Shany Biton Brimer, Kenta Tsutsui, Mahmoud Suleiman, Leif Sörnmo, Joachim A. Behar
The presence of fibrillatory waves (f-waves) is important in the diagnosis of atrial fibrillation (AF), which has motivated the development of methods for f-wave extraction. We propose a novel approach to benchmarking methods designed for single-lead ECG analysis, building on the hypothesis that better-performing AF classification using features computed from the extracted f-waves implies better-performing
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Texture graph transformer for prostate cancer classification Biomed. Signal Process. Control (IF 4.9) Pub Date : 2024-09-12 Guokai Zhang, Lin Gao, Huan Liu, Shuihua Wang, Xiaowen Xu, Binghui Zhao
Prostate cancer classification plays a pivotal role in the diagnosis and treatment of this disease. In this paper, we present a novel approach called the texture graph transformer, which combines texture analysis techniques, graph-based representations, and multi-head attention to enhance the accuracy of prostate cancer classification. Our texture graph transformer adeptly captures intricate texture
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Developing a real-time hand exoskeleton system that controlled by a hand gesture recognition system via wireless sensors Biomed. Signal Process. Control (IF 4.9) Pub Date : 2024-09-12 Yunus Hazar, Ömer Faruk Ertuğrul
Numerous studies have been conducted using wearable sensors on gesture recognition and classification methods for the control of robotic arms, prostheses, and exoskeleton systems. The primary goal of this study is to classify 32 different hand gestures in real-time using electromyography (EMG) signals. EMG signals obtained by measuring the electrical activity of the muscles were simulated wirelessly
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Pigment network detection and classification in dermoscopic images using directional imaging algorithms and convolutional neural networks Biomed. Signal Process. Control (IF 4.9) Pub Date : 2024-09-12 M.A. Rasel, Sameem Abdul Kareem, Unaizah Obaidellah
Early diagnosis of melanoma, which can save thousands of lives, relies heavily on the analysis of dermoscopic images. One crucial diagnostic criterion is the identification of unusual pigment network (PN). However, distinguishing between regular (typical) and irregular (atypical) PN is challenging. This study aims to automate the PN detection process using a directional imaging algorithm and classify
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Improved Cascade-RCNN for automatic detection of coronary artery plaque in multi-angle fusion CPR images Biomed. Signal Process. Control (IF 4.9) Pub Date : 2024-09-12 Xuan Nie, Bosong Chai, Kun Zhang, Chen Liu, Zhongxian Li, Rennian Huang, Qianru Wei, Minggang Huang, Weimin Huang
Coronary heart disease is a disease that seriously endangers human health and life which is caused by plaque formation due to coronary artery atherosclerosis or spasm. The detection of coronary plaques through medical imaging is a non-destructive and fast diagnostic method, which holds significant medical and clinical value for the diagnosis of coronary heart disease. In this paper, We proposed an
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CFNet: Automatic multi-modal brain tumor segmentation through hierarchical coarse-to-fine fusion and feature communication Biomed. Signal Process. Control (IF 4.9) Pub Date : 2024-09-12 Yaru Cheng, Yuanjie Zheng, Junxia Wang
Automatic segmentation of brain tumors employing images from multi-modalities is important for preoperative diagnosis and prognostic assessment. The rich complementary information contained within multi-modal images allows for improved brain tumor segmentation performance when models are trained on multi-modal data. However, accurate segmentation of small lesion regions from medical images remains
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Unleashing the power of Manta Rays Foraging Optimizer: A novel approach for hyper-parameter optimization in skin cancer classification Biomed. Signal Process. Control (IF 4.9) Pub Date : 2024-09-12 Shamsuddeen Adamu, Hitham Alhussian, Norshakirah Aziz, Said Jadid Abdulkadir, Ayed Alwadin, Mujaheed Abdullahi, Aliyu Garba
Optimizing hyperparameters is crucial for improving the performance of deep learning (DL) models, especially in complex applications like skin cancer classification from dermoscopic images. This study introduces a novel hyperparameter optimization strategy using the Manta Rays Foraging Optimizer (MRFO). A model tailored for skin cancer classification is created by fine-tuning a Convolutional Neural
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Edge morphology attention mechanism and optimal geometric matching connection model for vascular segmentation Biomed. Signal Process. Control (IF 4.9) Pub Date : 2024-09-12 Yuntao Zhu, Yuxuan Qiao, Qun Zhou, Xiaoping Yang
Over the past decades, medical image segmentation methods have been intensively developed, but there are still a number of unsolved challenging problems in vascular image segmentation, including broken vessels, insufficient vessel branches, and missing small vessels. In order to optimize the topology and accuracy of segmented vessels, we propose a novel Edge Morphology Attention Network (EMA-Net) for
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AFC-Unet: Attention-fused full-scale CNN-transformer unet for medical image segmentation Biomed. Signal Process. Control (IF 4.9) Pub Date : 2024-09-12 Wenjie Meng, Shujun Liu, Huajun Wang
In the field of medical image segmentation, although U-Net has achieved significant achievements, it still exposes some inherent disadvantages when dealing with complex anatomical structures and small targets, such as inaccurate target localization, blurry edges, and insufficient integration of contextual information. To address these challenges, this study proposes the Attention-Fused Full-Scale CNN-Transformer
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An extended variational autoencoder for cross-subject electromyograph gesture recognition Biomed. Signal Process. Control (IF 4.9) Pub Date : 2024-09-12 Zhen Zhang, Yuewei Ming, Quming Shen, Yanyu Wang, Yuhui Zhang
Surface electromyographic hand gesture recognition has gained significant attention in recent years, especially within the field of human–computer interfaces. However, cross-subject tasks remain challenging due to inherent individual differences. To address this, a novel approach for hand gesture recognition is proposed that leverages a subject-generalized variational autoencoder. This approach involves
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Asym-UNet: An asymmetric U-shape Network for breast lesions ultrasound images segmentation Biomed. Signal Process. Control (IF 4.9) Pub Date : 2024-09-12 Jia Liu, Jun Shao, Sen Xu, Zhiyong Tang, Weiquan Liu, Zeshuai Li, Tao Wang, Xuesheng Bian
Precise segmentation of breast ultrasound images is essential for early breast cancer screening. However, the segmentation process is challenging due to the diverse morphology of tumors, blurred boundaries, and the similar grey intensity distribution between lesions and normal tissues. To overcome these obstacles, we propose the Asym-UNet, an asymmetric U-shaped network tailored for segmenting breast
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White blood cell classification network using MobileNetv2 with multiscale feature extraction module and attention mechanism Biomed. Signal Process. Control (IF 4.9) Pub Date : 2024-09-12 Yujie Zou, Lianghong Wu, Cili Zuo, Liang Chen, Bowen Zhou, Hongqiang Zhang
White blood cells play a crucial role in the human immune system. The accurate classification of white blood cells can help doctors diagnose various diseases for patients. To enhance the classification accuracy of white blood cells micro-vision images, an efficient lightweight deep learning network called ICAFF-MobileNetv2 is proposed in this paper. Firstly, the pruning operations are applied to the
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Postural regulation and signal segmentation using clustering with TV regularization approach Biomed. Signal Process. Control (IF 4.9) Pub Date : 2024-09-12 Imen Trabelsi, Romain Hérault, Héloise Baillet, Régis Thouvarecq, Ludovic Seifert, Gilles Gasso
This paper investigates a clustering algorithm with Total Variation (TV) constraint for postural regulation from postural coordination signals. The problem addressed aims to automatically segment postural coordination signals into behavioral patterns according to the patients’ performances. Starting from the assumption that the strategies of postural regulation by a patient will be almost constant
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Spatio-Temporal correspondence attention network for vessel segmentation in X-ray coronary angiography Biomed. Signal Process. Control (IF 4.9) Pub Date : 2024-09-12 Yunlong Gao, Danni Ai, Yuanyuan Wang, Kaibin Cao, Hong Song, Jingfan Fan, Deqiang Xiao, Tianwei Zhang, Yining Wang, Jian Yang
The segmentation of contrast-filled vessels from X-ray coronary angiography (XCA) image sequences is an important step in the diagnosis and treatment of coronary artery disease. However, accurate and complete extraction of blood vessels is particularly challenging due to the poor quality of XCA images and the complex structure of blood vessels. The existing coronary artery segmentation methods mainly
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Revolutionizing diabetic retinopathy diagnosis through advanced deep learning techniques: Harnessing the power of GAN model with transfer learning and the DiaGAN-CNN model Biomed. Signal Process. Control (IF 4.9) Pub Date : 2024-09-12 Mohamed R. Shoaib, Heba M. Emara, Ahmed S. Mubarak, Osama A. Omer, Fathi E. Abd El-Samie, Hamada Esmaiel
Diabetic Retinopathy (DR) presents a substantial risk to vision, underscoring the critical necessity for prompt identification and timely intervention to avert visual decline. Conventional diagnostic approaches, dependent on human interpretation of retinal images, encounter difficulties in achieving high precision and high speed. In this study, we present an innovative methodology that surpasses conventional
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Modified deep inductive transfer learning diagnostic systems for diabetic retinopathy severity levels classification Biomed. Signal Process. Control (IF 4.9) Pub Date : 2024-09-11 Richa Vij, Sakshi Arora
Diabetic Retinopathy (DR), a retinal illness that degenerates the retina and causes blindness, can be effectively treated with early detection and examination. Although expensive and unpleasant, manual retinography is the gold standard for DR diagnosis. Many Deep Learning (DL)-based algorithms have shown promise as deep learning (DR) diagnostic tools, performing similarly to human picture evaluation
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Wavelength selection for real-time detection of human stress based on StO2 Biomed. Signal Process. Control (IF 4.9) Pub Date : 2024-09-11 Xinyu Liu, Xiao Xiao, Ju Zhou, Hanpu Wang, Yingjuan Jia, Tong Chen
Hyperspectral imaging (HSI) has been used to detect human stress by extracting tissue oxygen saturation (StO2). However, extracting StO2 from raw HSI data requires a large number of wavelengths, which prevents real-time human stress detection. In this study, we present a wavelength selection framework for real-time detection of human stress. Leveraging the simplicity of linear prediction (LP) and the
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High-frequency SSVEP-BCI system for detecting intermodulation frequency components using task-discriminant component analysis Biomed. Signal Process. Control (IF 4.9) Pub Date : 2024-09-11 Hongyan Cui, Meng Li, Xiaodong Ma, Xiaogang Chen
Recently, steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) has significantly progressed and is moving from the laboratory to practical application. However, the system performance and comfort of SSVEP-BCIs still need to be improved. In this study, five flicker frequencies (i.e., 30–34 Hz with an interval of 1 Hz) and eight scaling frequencies (i.e., 0.4–1.8 Hz with
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Evidence based management of medical devices: A follow-up experiment Biomed. Signal Process. Control (IF 4.9) Pub Date : 2024-09-11 Fabio Crapanzano, Alessio Luschi, Francesca Satta, Lorenzo Sani, Ernesto Iadanza
Ensuring the optimal operation and longevity of medical devices is essential for maintaining high safety standards in healthcare. This study presents a significant advancement in the field by enhancing an existing evidence-based maintenance (EBM) framework, which is crucial for the effective management of medical equipment. Building upon previous methodologies, this research introduces a novel, comprehensive
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Skip-AttSeqNet: Leveraging skip connection and attention-driven Seq2seq model to enhance eye movement event detection in Parkinson’s disease Biomed. Signal Process. Control (IF 4.9) Pub Date : 2024-09-11 Xin Wang, Lizhou Fan, Haiyun Li, Xiaochan Bi, Wenjing Jiang, Xin Ma
To address the limitations of traditional algorithms in detecting eye movement events, particularly in Parkinson’s disease (PD) patients, this study introduces . It presents an innovative approach combining skip-connected, one-dimensional convolutional neural networks with an attention-enhanced, bidirectional long short-term memory network. This hybrid architecture significantly advances smooth pursuit
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Tissue segmentation for traumatic brain injury based on multimodal MRI image fusion-semantic segmentation Biomed. Signal Process. Control (IF 4.9) Pub Date : 2024-09-11 Yao Xu, Zhongmin Chen, Xiaohui Wang, Shanghai Jiang, Fuping Wang, Hong Lu
Accurate segmentation of traumatic brain injury (TBI) has great significance for physicians to diagnose and assess a patient’s condition. The utilization of multimodal information plays a critical role in TBI segmentation. However, most of the existing methods mainly focus on direct extraction and selection of deep semantic features, whereas in this paper, we use image fusion as an auxiliary task for
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An intelligent bone age assessment model incorporating multilayer superimposed texture enhancement and the China-05 attention mechanism Biomed. Signal Process. Control (IF 4.9) Pub Date : 2024-09-11 Guo Zisheng, Wang Xinhua, Yang Lin, Yang Xuyun, Qi Yongsheng, Zhao Zeling
In the process of intelligent bone age assessment for Chinese ethnicity, generalized convolutional network models are not well targeted in extracting specific features in medical skeletal images and lack specificity in training and predicting skeletal developmental features in different ethnicities. This study aims to propose a hybrid improved deep residual network model, ZH05-DL-ResNet50, focusing
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UMF-Net: A UNet-based multi-branch feature fusion network for colon polyp segmentation Biomed. Signal Process. Control (IF 4.9) Pub Date : 2024-09-11 Yulong Wan, Dongming Zhou, Changcheng Wang
The early diagnosis of colorectal cancer heavily relies on colonoscopy, but clinical examinations often face challenges in detecting polyps due to various influencing factors. Polyp segmentation models can be categorized into two architectures: those based on convolutional neural networks (CNNs), which specialize in local modeling, and those based on Transformers, which excel at capturing global context
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Thin vessel segmentation in fundus images using attention UNet and modified Frangi filtering Biomed. Signal Process. Control (IF 4.9) Pub Date : 2024-09-11 Anumeha Varma, Monika Agrawal
Thin vessel segmentation is an active research problem, with an emphasis on finding a universal approach for different types of fundus datasets. Enhancement of the thin vessels is the first and foremost task for proper segmentation, which is proposed to be done with the total variation (TV) decomposition method with layer-selective enhancement and illumination correction. The vessel segmentation task
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Enhancing complex upper-limb motor imagery discrimination through an incremental training strategy Biomed. Signal Process. Control (IF 4.9) Pub Date : 2024-09-11 C.D. Guerrero-Mendez, Cristian F. Blanco-Diaz, H. Rivera-Flor, C. Badue, A. Ferreira De Souza, D. Delisle-Rodriguez, T.F. Bastos-Filho
Motor Imagery (MI)-based Brain–Computer Interface (BCI) systems are a great technological advance for the recovery of lost movements in people with severe motor impairments. Different Artificial Intelligence (AI) techniques with supervised methods have been explored for MI task discrimination, especially static movements from left and right hands. Due to different factors affecting MI-based Electroencephalography
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Research on multi-sequence MR image synthesis CT algorithm based on unsupervised method Biomed. Signal Process. Control (IF 4.9) Pub Date : 2024-09-11 Liwei Deng, Henan Sun, Sijuan Huang, Xin Yang, Jing Wang
Using multi-sequence MRI to synthesize CT, combining different MRI sequences, and processing synthetic CT (sCT) based on unsupervised algorithms, to conduct more in-depth and comprehensive research on MR-based radiotherapy planning. First, we compare and analyze the effects of single-sequence MRI synthesis to CT to determine the quality of the synthesis and find a set of optimal sequence combinations
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Lung-YOLO: Multiscale feature fusion attention and cross-layer aggregation for lung nodule detection Biomed. Signal Process. Control (IF 4.9) Pub Date : 2024-09-11 Chaosheng Tang, Feifei Zhou, Junding Sun, Yudong Zhang
Lung cancer is a significant public health problem worldwide, and its mortality and morbidity rates are among the highest of cancers. At the same time, early diagnosis of nodules can significantly improve the survival rate of patients. Therefore, this paper proposes the Lung-YOLO algorithm for lung CT image detection based on YOLOv6. First, to enable the network to detect nodules of different sizes
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Ontological modeling with recursive recurrent neural network and crayfish optimization for reliable breast cancer prediction Biomed. Signal Process. Control (IF 4.9) Pub Date : 2024-09-11 V. Rajeswari, K. Sakthi Priya
Breast cancer is predominantly a female illness, even though it affects men less frequently than women. Breast cancer prediction provides significant challenges in the medical field due to the complex and heterogeneous nature of disease. Traditional methods for breast cancer prediction, such as statistical analysis and conventional machine learning algorithms, often fall short in delivering high accuracy
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Enhancing motor imagery decoding in brain–computer interfaces using Riemann tangent space mapping and cross frequency coupling Biomed. Signal Process. Control (IF 4.9) Pub Date : 2024-09-11 Xiong Xiong, Li Su, Jinjie Guo, Tianyuan Song, Ying Wang, Jinguo Huang, Guixia Kang
Motor Imagery (MI) is a key paradigm in Brain-Computer Interfaces (BCI) aimed at decoding motor intentions from EEG signals. However, accuracy remains challenging due to data limitations, noise, and non-stationarity. This paper introduces DFBRTS, a novel method leveraging Riemannian geometry and Cross-Frequency Coupling to improve MI-EEG decoding. DFBRTS filters EEG signals using a Dichotomous Filter
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Multi-GlaucNet: A multi-task model for optic disc segmentation, blood vessel segmentation and glaucoma detection Biomed. Signal Process. Control (IF 4.9) Pub Date : 2024-09-10 Haoren Xiong, Fei Long, Mohammad S. Alam, Jun Sang
Glaucoma is a common and severe ocular disease that often leads to vision loss. The information on the optic disc (OD) and blood vessels in fundus images can significantly aid in glaucoma detection. In addition, the use of deep learning models for glaucoma detection is a highly effective approach. We propose a multi-task deep learning model called Multi-GlaucNet that can simultaneously segment the
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A dual-domain framework for multimodal medical image registration: Optimizing phase consistency with LPC-GIMI Biomed. Signal Process. Control (IF 4.9) Pub Date : 2024-09-10 Shanshan Chen, Kangjian He, Dan Xu, Hongzhen Shi, Rong Zeng
Efficient and accurate image registration is crucial for advancing medical image processing. However, inherent limitations in medical imaging, such as low image quality, missing data, and inconsistent visual features, are widespread and significantly affect registration accuracy. To address the challenges of registering unlabeled datasets with large deformation fields and inconsistent imaging regions
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MAST-UNet: More adaptive semantic texture for segmenting pulmonary nodules Biomed. Signal Process. Control (IF 4.9) Pub Date : 2024-09-10 Xuemei Shi, Zifan Zhang
Lung cancer is one of the most common cancers globally, and early detection of lung cancer is crucial for patient treatment and survival. Accurate segmentation of pulmonary nodules is essential for the early diagnosis of lung cancer. However, pulmonary nodules are relatively small, and their characteristic information is unclear, making precise segmentation challenging. Based on the characteristics
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Structure-aware single-source generalization with pixel-level disentanglement for joint optic disc and cup segmentation Biomed. Signal Process. Control (IF 4.9) Pub Date : 2024-09-10 Jia-Xuan Jiang, Yuee Li, Zhong Wang
Deploying deep segmentation models in new medical centers poses a significant challenge due to statistical disparities between source and unknown domains. Recent advancements in domain generalization (DG) have shown improved generalization performance by leveraging disentanglement techniques on domain-specific and domain-invariant features. However, existing DG methods face challenges in achieving
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A neurophysiological model based on resting state EEG functional connectivity features for assessing semantic long-term memory performance Biomed. Signal Process. Control (IF 4.9) Pub Date : 2024-09-10 Hafeez Ullah Amin, Amr Ahmed, Mohd Zuki Yusoff, Mohamad Naufal Mohamad Saad, Aamir Saeed Malik
Existing methods for assessing long-term memory (LTM) rely predominantly on psychometric tests or clinical expert observations. In this study, we propose an objective method for evaluating semantic LTM ability using resting-state electroencephalography (EEG) functional connectivity. Data from 68 participants were analysed, deriving functional connectivity from the phase information of EEG theta (4–8 Hz)
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Improving burn diagnosis in medical image retrieval from grafting burn samples using B-coefficients and the CLAHE algorithm Biomed. Signal Process. Control (IF 4.9) Pub Date : 2024-09-09 Pramod K.B. Rangaiah, B.P. Pradeep kumar, Robin Augustine
This study focuses on the vital difficulty of burn assessment in medical image retrieval from grafted burn specimens particularly in resource-constrained contexts where speedy and precise diagnoses are required. Our solution combines sophisticated machine learning techniques, namely an Artificial Neural Network (ANN), with the Contrast Limited Adaptive Histogram Equalisation (CLAHE) algorithm in an
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Integration of multiscale fusion of residual neural network with 2-D gramian angular fields for lower limb movement recognition based on multi-channel sEMG signals Biomed. Signal Process. Control (IF 4.9) Pub Date : 2024-09-09 Hao Zhou, Ruliang Feng, Yinghu Peng, Dingxun Jin, Xiaohui Li, Dahua Shou, Guanglin Li, Lin Wang
The human lower limb movements recognition (LLMR) plays a pivotal role in active lower limb exoskeleton robots. Employing surface electromyography (sEMG) signals for LLMR allows for the convenient, rapid and stable capture of signal variations, facilitating efficient identification of lower limb motion patterns. However, current sEMG-based LLMR methods face challenges such as incomplete feature extraction
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Improving EEG signal-based emotion recognition using a hybrid GWO-XGBoost feature selection method Biomed. Signal Process. Control (IF 4.9) Pub Date : 2024-09-09 Hanie Asemi, Nacer Farajzadeh
Emotion plays a crucial role in daily life, influencing cognitive functions such as language comprehension, decision-making, attention, and concentration. With the growing integration of computer systems into our everyday activities, it is essential to understand and detect emotional states accurately. Emotion detection through EEG signals allows direct assessment of the human’s internal state and
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DEMA: Deep EEG-first multi-physiological affect model for emotion recognition Biomed. Signal Process. Control (IF 4.9) Pub Date : 2024-09-07 Qiaomei Li, Donghui Jin, Jun Huang, Qingshan Zhong, Linyan Xu, Jiali Lin, Dazhi Jiang
In the field of electroencephalogram (EEG) emotion recognition, existing studies often focus solely on EEG-specific features, neglecting valuable emotional cues present in other physiological signals. To address this gap, we introduce the Deep EEG-first Multi-Physiological Affect (DEMA) model. DEMA leverages a Deep Multi-View Convolutional Neural Network (DMCNN) to extract comprehensive features from
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Multi-channel MCG signals filtering method based on multivariate variational mode decomposition Biomed. Signal Process. Control (IF 4.9) Pub Date : 2024-09-07 Kun Yang, Tiedong Xu, Deng Pan, Zhidan Zhang, Hai Wang, Xiangyan Kong
With the help of an extremely sensitive magnetic sensors array, magnetocardiography (MCG) can record magnetic field signals produced by the cardiac electrical activity. However, MCG is a very weak magnetic field signal, and tends to be overwhelmed by unsuppressed ambient magnetic noise. To address this issue, this work proposed a multi-channel MCG signals synchronously denoising method based on multivariate