Article
Engineering, Biomedical
Yifeng Ji, Dan Chen, Yiping Zuo, Tengfei Gao, Yunbo Tang
Summary: This study develops an object detection framework for localizing sleep apnea and hypopnea (SAH) events. The framework utilizes dual-modal feature learning and hierarchical feature maps to accurately identify the position of SAH segments with varied durations. Experimental results show that the framework performs the best in detecting SAH events.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Justin Amadeus Albert, Bert Arnrich
Summary: This paper proposes a computer vision method to continuously monitor fatigue during resistance training by predicting external and internal parameters. The method utilizes human pose estimation and analyzes kinematic data to accurately predict external load and perceived exertion, providing important feedback for coaches or athletes.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Siba Prasad Mishra, Pankaj Warule, Suman Deb
Summary: Automated speech emotion recognition has gained popularity due to its wide range of applications. Researchers have been using different methods to improve emotion recognition performance. This study proposes a method based on multi-resolution variational mode decomposition to extract features for emotion classification, achieving better accuracy compared to existing methods when combined with a deep neural network classifier.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Jignyasa Sanghavi, Manish Kurhekar
Summary: This research investigates various segmentation and classification techniques for accurately segmenting the Optic Disk and classifying normal and glaucomatous eyes. The proposed method shows potential clinical application and benefits healthcare facilities with limited resources.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Hongli Cheng, Shizhong Yuan, Weimin Li, Xiao Yu, Fangyu Liu, Xiao Liu, Tsigabu Teame Bezabih
Summary: This study proposes a collaborative learning framework called LSTM-TSGAIN for accurate prediction of the progression of Alzheimer's disease. The model improves prediction performance through the use of generative adversarial imputation, collaborative training, and adjusting input lengths, and experiments demonstrate its superiority over existing methods.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Cell & Tissue Engineering
Yasuhiro Homma, Sayuri Uchino, Masashi Nagao, Takanori Wakayama, Shin Fukusato, Tomonori Baba, Taiji Watari, Koju Hayashi, Yoshitomo Saita, Muneaki Ishijima
Summary: This study investigates the safety and feasibility of locoregional PRP injection for iliopsoas impingement after THA, and finds that it is both safe and feasible.
REGENERATIVE THERAPY
(2024)
Article
Engineering, Biomedical
Dibaloke Chanda, Md. Saif Hassan Onim, Hussain Nyeem, Tareque Bashar Ovi, Sauda Suara Naba
Summary: Skin cancer is a significant health concern, and existing computer vision methods struggle with the variability in skin lesion features. We propose an ensemble approach involving three customized DCNNs to achieve a better bias-variance trade-off.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Xing Wu, Zhi Li, Chenjie Tao, Xianhua Han, Yen-Wei Chen, Junfeng Yao, Jian Zhang, Qun Sun, Weimin Li, Yue Liu, Yike Guo
Summary: Data Efficient Augmentation (DEA) is proposed as a plug-and-use method for efficient medical image segmentation. DEA enhances data efficiency and has good generalization capabilities across different segmentation methods, improving segmentation performance on multiple datasets.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Dazhao Zhou, Shang Sun, Ming Li, Xueping Liu, Shengli Mi
Summary: This research proposes a GAN-based super-resolution reconstruction model which significantly enhances the quality of eyelid tissue images by introducing a new attention mechanism and high-order degradation simulation. Compared to existing models, the proposed model demonstrates the strongest generalization ability and robustness, reducing reconstruction time and improving image quality for low and high-noise images.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Yingbin Liu, Yang Zhang, Shuai Yu, Yanbin Guo, Yong Li, Xiao-Jian Han, Yuan-di Zhao, Shibiao Chen, Guoping Wang
Summary: This study proposes an efficient adaptive variational mode decomposition algorithm for the extraction of adventitious lung sounds from original lung sounds. The algorithm, which constructs a frequency-constrained model and employs a strategy of gradually extracting each mode, demonstrates superiority in both decomposition quality and computational efficiency. The characteristic frequency distribution of adventitious lung sounds obtained from the experiment results provides important references for clinicians and demonstrates the potential application of the proposed algorithm in the diagnosis of pulmonary diseases.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Yuchen Pan, Yuanyuan Shang, Wei Wang, Zhuhong Shao, Zhuojin Han, Tie Liu, Guodong Guo, Hui Ding
Summary: In this paper, the authors propose the MFDS-VAN, a deep supervised voiceprint adversarial network, for audio-based depression recognition. The MFDS-VAN integrates acoustic features and audio waveform to predict depression score. Experimental results show that the MFDS-VAN significantly enhances robustness and performance in speech-based depression recognition, achieving competitive results compared to recent audio-based methodologies.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Moajjem Hossain Chowdhury, Muhammad E. H. Chowdhury, Mamun Bin Ibne Reaz, Sawal Hamid Md Ali, Seyed Mehdi Rakhtala, M. Murugappan, Sakib Mahmud, Nazmul Islam Shuzan, Ahmad Ashrif A. Bakar, Mohd Ibrahim Bin Shapiai Abd Razak, Muhammad Salman Khan, Amith Khandakar
Summary: A method to accurately estimate physiological signals from video streams at a minimal cost is highly valuable, especially in pre-clinical health monitoring. The proposed LGI-rPPG-Net model can generate highly correlated rPPG signals that can be used as a substitute for finger PPG when in-contact collection is not feasible.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Konstantinos Ntagiantas, Eduardo Pignatelli, Nicholas S. Peters, Chris D. Cantwell, Rasheda A. Chowdhury, Anil A. Bharath
Summary: The study aims to infer the spatial distribution of tissue conductivity for effective treatment of atrial fibrillation (AF) through concurrently acquired contact electrograms (EGMs). By generating a simulated dataset and training a deep neural network, the study successfully estimates the location of scars and quantifies tissue conductivity with a high accuracy of 91%.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Cell & Tissue Engineering
Shant Nepal, Jinyan Si, Shohei Ishikawa, Masaki Nishikawa, Yasuyuki Sakai, Aya M. Akimoto, Hiroyuki Okada, Shinsuke Ohba, Ung-il Chung, Takamasa Sakai, Hironori Hojo
Summary: In this study, we evaluated the potential of oligo-tetra-PEG gel (Oligo gel) as a growth factor-releasing scaffold. Compared to tetra-PEG hydrogel (Tetra gel), Oligo gel underwent a higher degree of gel-gel phase separation, demonstrating better protein release and osteogenic potential, and achieving superior regeneration in a mouse calvarial defect model. Therefore, Oligo gel is considered a promising therapeutic biomaterial.
REGENERATIVE THERAPY
(2024)
Article
Engineering, Biomedical
Patricia Mesa-Gresa, Jose-Antonio Gil-Gomez, Jose Antonio Lozano-Quilis, Konstanze Schoeps, Inmaculada Montoya-Castilla
Summary: This study examines the electrophysiological correlates of emotional response in adolescents and young adults using electroencephalography measures. The findings indicate differences in cortical neural activity based on the valence and arousal of the images, highlighting the significant differences in emotional responses between adolescents and young adults.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Cell & Tissue Engineering
Taichi Tenkumo, Benedikt Kruse, Kathrin Kostka, Viktoriya Sokolova, Toru Ogawa, Nobuhiro Yoda, Oleg Prymak, Osamu Suzuki, Keiichi Sasaki, Matthias Epple
Summary: This study evaluated the bone-healing effects of a triple-functionalized CaP paste in the rat femoral head. The results showed that the paste significantly accelerated bone healing after 21 days and increased the levels of bone formation-related markers at the protein and gene levels.
REGENERATIVE THERAPY
(2024)
Article
Engineering, Biomedical
Gabriel Chaves de Melo, Gabriela Castellano, Arturo Forner-Cordero
Summary: A Brain-Computer Interface (BCI) translates brain activities into computer commands through decoding brain signals, with electro-encephalography being the most widely adopted technique for signal recording. However, the high intra-subject variability of EEG signals poses a challenge for BCI development. This study aims to improve a pseudo-online movement detection system by using motor imagery EEG signals, proposing a strategy to minimize the effects of poor spatial resolution and active reference electrode by finding the best combinations of electrode pairs. The average accuracy across 15 subjects was 95%.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Yue Tu, Shukuan Lin, Jianzhong Qiao, Yilin Zhuang, Zhiqi Wang, Dai Wang
Summary: This paper proposes a Consistent Manifold Projection Generative Adversarial Network (CMPGAN) for FDG-PET generation and a Multilevel Multimodal Fusion Diagnosis Network (MMFDN) for diagnosing AD. CMPGAN utilizes consistent manifold projection and a distribution distance metric to overcome the incompleteness of FDG-PET. MMFDN constructs multiscale and voxel-level feature extraction networks for different levels of multimodal fusion. Experimental results show that our proposed method outperforms state-of-the-art methods in FDG-PET generation and AD diagnosis.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Lei Cao, Wenrong Wang, Yilin Dong, Chunjiang Fan
Summary: This paper proposes a multimodal tensor fusion network based on the self-attention mechanism for classroom fatigue recognition. The experimental results demonstrate that the network has better performance in multimodal feature fusion.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Hany El-Ghaish, Emadeldeen Eldele
Summary: This paper introduces ECGTransForm, a deep learning framework tailored for ECG arrhythmia classification. The framework comprehensively captures temporal dependencies and spatial features, and addresses the class imbalance challenge. Experimental results demonstrate the superiority of ECGTransForm in arrhythmia diagnosis, offering meaningful feature extraction.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
