Article
Biophysics
Luke T. Mattar, Arash B. Mahboobin, Adam J. Popchak, William J. Anderst, Volker Musahl, James J. Irrgang, Richard E. Debski
Summary: Exercise therapy fails in about 25.0% of cases for individuals with rotator cuff tears, and one reason for this failure may be the inability to strengthen and balance the muscle forces that keep the humeral head in the correct position. This study developed computational musculoskeletal models to compare the net muscle force before and after exercise therapy between successfully and unsuccessfully treated patients. The study found that unsuccessfully treated patients had less inferiorly oriented net muscle forces, which may increase the risk of impingement.
JOURNAL OF BIOMECHANICS
(2024)
Article
Engineering, Biomedical
Santtu Soderholm, Joonas Lahtinen, Carsten H. Wolters, Sampsa Pursiainen
Summary: The problem of reconstructing brain activity from electric potential measurements is challenging due to the ill-posed inverse problem and inaccuracies in the utilized forward solution methods. This study investigates the impact of source location restriction on the accuracy of two inverse methods, sLORETA and Dipole Scan, in different signal-to-noise ratios. The findings demonstrate that peeling can significantly enhance the regularity of reconstructed distributions when appropriate depth and low noise level are selected. The applied inverse algorithm and observed brain compartment also influence the accuracy.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Deshan Ma, Li Li, Wenbin Shi, Mengwei Li, Jian Zhang, Yong Fan, Yu Kang, Xiu Zhang, Pengming Yu, Qing Zhang, Zhengbo Zhang, Chien-Hung Yeh
Summary: Autonomic nervous system dysfunction is a significant characteristic of congestive heart failure patients. This study introduced HRV and MMCA-derived parameters to quantify ANS function in CHF patients and compared their clinical efficacy. The results showed that most parameters improved after treatment in SRHF patients, while only a few showed significant differences in ARHF patients. PNS function and ANS balance were recovered in all CHF patients after treatment. These metrics can be used for prognosis and therapeutic efficacy monitoring.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Qing Pan, Yunhang Li, Nili Tian
Summary: Medical image fusion is an important tool in medical diagnosis. This paper proposes a medical image fusion method based on saliency measurement improvement and local structural similarity correction. The method decomposes the source images using multi-level decomposition and nonsubsampled shearlet transformation, and then improves the saliency measurement of the base layer fusion rule using the source image mask. Furthermore, a correction method based on structural similarity index measure is used to deal with information distortion. Experimental results show that the proposed method has excellent subjective visual effects and outperforms other state-of-the-art methods in objective indexes.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Deepika Shekhawat, Deevesh Chaudhary, Ashutosh Kumar, Anju Kalwar, Neha Mishra, Dimpal Sharma
Summary: Diagnosing fetal cardiac abnormalities by fetal electrocardiogram (FECG) is challenging but crucial. Fetal ECG monitoring provides accurate information on the fetal state. However, the noisy and artifact-ridden ECG signals make ECG extraction difficult. Misdiagnosis of fetal arrhythmias can lead to inappropriate treatment and further complications. This paper proposes a Binarized spiking neural network optimized with Momentum search algorithm for Fetal Arrhythmia Detection and Classification. The proposed technique accurately classifies the output as normal or arrhythmia and outperforms existing approaches.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Jiayang Xu, Wenxia Qian, Liangliang Hu, Guangyuan Liao, Yin Tian
Summary: This study proposes a novel feature called asPLV based on multi-channel EEG emotional features, which shows superior classification performance and generalization in recognizing different emotions. The method also introduces a novel brain network metric to elucidate the collaboration and information exchange among emotion-related brain regions, and provides new insights for the development of an emotional brain-computer interface (BCI).
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Chuanmin Wu, Jiafeng Qiu, Gang Shen
Summary: Heart rate variability (HRV) is a reliable measure of physical and mental fitness. This study proposes a self-supervised learning approach to address the challenge of undesirable artifacts in BCG signals, and demonstrates high accuracy in heartbeat identification and interbeat interval measurements through evaluations.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Cell & Tissue Engineering
Jinghui Xu, Zhenxiao Ren, Tianzuo Niu, Siyuan Li
Summary: This study discovers that MSCs-EVs attenuate inflammation and oxidative stress in spinal cord injury (SCI) by delivering miR-26b-5p to target the KDM6A/NOX4 axis, facilitating recovery.
REGENERATIVE THERAPY
(2024)
Article
Engineering, Biomedical
Mamadou Dia, Ghazaleh Khodabandelou, Aznul Qalid Md Sabri, Alice Othmani
Summary: Affect recognition is an important area of research for machine learning researchers, with a focus on assessing and classifying emotions in people with mental disorders. This paper proposes a supervised learning method for classifying Autism Spectrum Disorder (ASD) and evaluating affect levels in autistic children. The proposed approach is evaluated using YouTube videos and images, showing promising results for continuous affect recognition in children with ASD.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Huiling Cai, Qingcheng Lin, Hanwei Liu, Xuefeng Li, Hui Xiao
Summary: Light has both visual and non-visual effects on the human body, and illuminance and correlated colour temperature (CCT) have significant impacts on human mood, alertness, and comfort. The study found that both illuminance and CCT significantly influenced subjective perception. Using continuous physiological data, subjective feelings under the influence of the light environment were evaluated. The research results are important for creating human-centred indoor environments and personalized user experience research.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
H. Rivera-Flor, C. D. Guerrero-Mendez, K. A. Hernandez-Ossa, D. Delisle-Rodriguez, R. Mello, T. F. Bastos-Filho
Summary: This study proposes a cloud SSVEP-based BCI system to evaluate its performance in commanding a virtual electric-powered wheelchair through user experience in a training simulator. The study compares traditional wheelchair command modes using standardized questionnaires and finds high levels of motivation, enjoyment, and usability for the cloud SSVEP-based BCI, despite its higher mental work and discomfort compared to other input interfaces.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Cho- Moon, Eun Bin Kim, Yoo Sang Baek, Onesok Lee
Summary: This study proposes a deep learning-based evaluation method that identifies severity characteristics and predicts changes in severity of psoriasis during treatment. The method demonstrates good prediction performance and verifies the possibility of tracking dynamic changes in the disease and personalized treatment.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Limei Ma, Yang Nie, Qian Feng, Jianshu Cao, Shaoya Guan
Summary: This paper explores the application of deep learning methods in vascular image registration, compares the performance of different CNN models, and discusses the optimization of network structures. The experiments demonstrate that these networks are suitable for vascular image registration, with Alex-reg achieving the best performance.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Chengyang Gao, Qiule Sun, Wen Zhu, Lizhi Zhang, Jianxin Zhang, Bin Liu, Junxing Zhang
Summary: Computer-aided diagnosis based on deep learning improves the efficiency of pathologists. This study explores the effectiveness of Transformers in classifying breast cancer tissues in WSIs and proposes a hybrid multiple instance learning method called HTransMIL. The method selects informative instances and strengthens their correlation to achieve accurate classification, while visualization analysis helps understand the weakly supervised classification model.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Guilin Zhan, Kai Qian, Wenyang Chen, Dandan Xue, Mengdi Li, Jun Zhang, Yonghang Tai
Summary: This study presents a novel deep learning network model for accurately segmenting COVID19 lesions in chest CT images. By adopting a fusion strategy and a hybrid semi-supervised algorithm, the model achieved a significant improvement in accuracy, surpassing state-of-the-art methods.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
A. Priya, V. Vasudevan
Summary: A hybrid model combining AlexNet and GRU neural networks is proposed to identify and characterize brain cancers using MRI data. The model achieves high accuracy and performance in classifying and diagnosing brain cancers. This research has the potential to improve patient outcomes and create a more favorable healthcare environment in medical imaging and brain tumor detection.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
P. Saravanaprasad, S. Anbu Karuppusamy
Summary: This paper investigates the landscape of clinical image analysis in computer-aided diagnosis, with a specific focus on the architecture of Convolutional Neural Network (CNN). By using CT scan images of lung tumors, the proposed CNN architecture demonstrates remarkable performance in lung tumor diagnosis.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Yi Xia, Hua Sun, Baifu Zhang, Yangyang Xu, Qiang Ye
Summary: This study proposes a deep-learning-based framework for predicting freezing of gait (FoG) in patients with Parkinson's disease. The framework utilizes self-supervised contrastive learning to learn latent gait representations and achieves accurate and robust FoG prediction.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Mingyue Zhang, Chengruo Huang, Zumrat Druzhinin
Summary: This research presents a new approach for diagnosing ACL tears using a Modified Golden Search Algorithm optimized Convolutional Neural Network (CNN). The method is able to extract significant features from knee MRI images and demonstrates superior accuracy compared to other methods.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Raffaele Dubbioso, Myriam Spisto, Laura Verde, Valentina Virginia Iuzzolino, Gianmaria Senerchia, Giuseppe De Pietro, Ivanoe De Falco, Giovanna Sannino
Summary: Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease that affects motor neurons and impairs communication. By using innovative machine learning techniques, this study successfully discovered markers and patterns to promptly detect and classify the severity of speech difficulties.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
