Article
Engineering, Biomedical
Qinghua Sun, Lei Wang, Jiali Li, Chunmiao Liang, Jianmin Yang, Yuguo Chen, Cong Wang
Summary: In this study, a method for detecting myocardial ischemia and identifying its etiology using multi-phase ECG dynamic features was proposed. The method involved using deterministic learning and self-adaptive VMD to model the ECG signal and extract dynamic features. The results showed promising performance in detecting myocardial ischemia and identifying its etiology.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Daniele Alves da Silva, Nayra Ferreira Lima Castelo Branco, Laiana Sepulveda de Andrade Mesquita, Hermes Manoel Galvao Castelo Branco, Guilherme de Alencar Barreto
Summary: This study aims to use machine learning techniques to predict the risk of falls in the elderly and conduct a comprehensive evaluation of relevant muscle groups. By evaluating electromyographic and dynamometric data, the study found that features extracted from myoelectric signals are more effective in predicting the risk of falls in the elderly.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
M. A. H. Akhand, Mahfuza Akter Maria, Md Abdus Samad Kamal, Tetsuya Shimamura
Summary: The study proposes an enhanced connectivity feature map for emotion recognition by introducing partial mutual information and an additional channel, which improves the performance of emotion recognition by extracting more information from brain signals.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
N. Sasikaladevi, A. Revathi
Summary: This research focuses on the early diagnosis of kidney diseases using deep learning techniques on CT scan images. The proposed model achieves superior validation accuracy and serves as a robust tool for early diagnosis of kidney diseases.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
T. M. Nithya, P. Dhivya, S. N. Sangeethaa, P. Rajesh Kanna
Summary: This paper focuses on developing a suitable model and algorithms for data augmentation, feature extraction, and classification in order to accurately identify and classify emergency vehicles based on sound. By using signal augmentation and a new feature extraction method, combined with convolutional neural networks and long short term memory models, the accuracy of vehicle sound identification and classification can be improved.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
T. S. Sasikala
Summary: This research presents a MRI-based Computer-Aided Diagnosis system for detecting Alzheimer's disease. By using a new segmentation method and a convolutional neural network model, the algorithm's performance in terms of accuracy, sensitivity, and specificity is improved.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Chenjian Sun, Min Jiang, Linlin Gao, Yu Xin, Yihong Dong
Summary: Depression is a prevalent mental health disorder, and the lack of specific biomarkers makes it difficult to diagnose definitively. Deep learning methods have shown promise in depression detection, but current approaches have limitations in recognizing depression-related cues in audio signals. To address this, researchers propose a graph neural network approach that incorporates potential connections within and between audio signals. Experimental results demonstrate the effectiveness of the proposed model on various evaluation metrics, outperforming other algorithms.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Abhishek Kesarwani, Sunanda Das, Dakshina Ranjan Kisku, Mamata Dalui
Summary: This study aims to construct a reliable anaemia detection system by combining cutting-edge computational methods with the age-old practice of assessing haemoglobin levels from palm pallor. The proposed method utilizes smartphone camera sensor and deep learning models for accurate estimation of blood haemoglobin levels.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Man-ni Chu, Ming -lei Yu, Jia-lien Hsu
Summary: This paper proposes a computational method for calculating the similarity between different languages or language varieties and validates its effectiveness through experiments. The results show that the proposed model outperforms comparative experiments in the identification task and can assist linguists in pre-classifying sound files.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Chunyan Yu, Yanju Lu, Jinhui Pang, Lu Li
Summary: In this study, a safe and effective hemostatic composite sponge was developed by combining chitosan and hydroxypropylmethylcellulose (HPMC). The sponge exhibited excellent flexibility and rapid hemostatic ability in vitro. In vivo assessments showed that the sponge had the shortest clotting time and minimal blood loss.
JOURNAL OF THE MECHANICAL BEHAVIOR OF BIOMEDICAL MATERIALS
(2024)
Article
Engineering, Biomedical
Zhongliang Yu, Lin Yu, Junjie Liu
Summary: The study proposes incorporating functionally graded tablets into nacreous composites to enhance both stiffness and damping properties. Analytical formulae and numerical experiments demonstrate the effectiveness of this design, surpassing existing homogeneous composites in performance.
JOURNAL OF THE MECHANICAL BEHAVIOR OF BIOMEDICAL MATERIALS
(2024)
Article
Engineering, Biomedical
Priya Ranganathan, Vijayakumari Sugumaran, Bargavi Purushothaman, Ajay Rakkesh Rajendran, Balakumar Subramanian
Summary: The study aims to design and fabricate an ultra-easier multi-functional biomedical polymeric scaffold loaded with unique equimolar Ca:P phasic bioactive glass material. The results showed that the G:BG (1:2) ratio is the more appropriate composition for enhanced bio-mineralization and higher surface area. The scaffold can induce mitogenesis in osteoblast cells for hard tissue regeneration and rapid collagen secretion in fibroblast cells for soft tissue regeneration.
JOURNAL OF THE MECHANICAL BEHAVIOR OF BIOMEDICAL MATERIALS
(2024)
Article
Engineering, Biomedical
Manan Patel, Harsh Bhatt, Manushi Munshi, Shivani Pandya, Swati Jain, Priyank Thakkar, Sangwon Yoon
Summary: Electroencephalogram (EEG) signals have been effectively used to measure and analyze neurological data and brain-related ailments. Artificial Intelligence (AI) algorithms, specifically the proposed CNN-FEBAC framework, show promising results in studying the EEG signals of autistic patients and predicting their response to stimuli with 91% accuracy.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
P. Nancy, M. Parameswari, J. Sathya Priya
Summary: Stroke is the third leading cause of mortality worldwide, and early detection is crucial to avoid health risks. Existing research on disease detection using machine learning techniques has limitations, so a new stroke detection system is proposed. The experimental results show that the proposed method achieves a high accuracy rate in stroke detection.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Weijun Gong, Yurong Qian, Weihang Zhou, Hongyong Leng
Summary: The recognition of dynamic facial expressions is challenging due to various factors, and obtaining discriminative expression features has been difficult. Traditional deep learning networks lack understanding of global and temporal expressions. This study proposes an enhanced spatial-temporal learning network to improve dynamic facial expression recognition.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Muhammad Adeel Azam, Claudio Sampieri, Alessandro Ioppi, Muhammad Ashir Azam, Chiara Baldini, Shunlei Li, Sara Moccia, Giorgio Peretti, Leonardo S. Mattos
Summary: This paper presents SegMENT-Plus, a deep learning segmentation convolutional network specifically developed for accurate delineation of laryngeal squamous cell carcinoma (LSCC). The network achieves high performance and generalization capability, surpassing other state-of-the-art architectures in segmenting LSCC margins in endoscopic images.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Tuuli Uudeberg, Juri Belikov, Laura Paeske, Hiie Hinrikus, Innar Liiv, Maie Bachmann
Summary: This article introduces a novel feature extraction method, the in-phase matrix profile (pMP), specifically adapted for electroencephalographic (EEG) signals, for detecting major depressive disorder (MDD). The results show that pMP outperforms Higuchi's fractal dimension (HFD) in detecting MDD, making it a promising method for future studies and potential clinical use for diagnosing MDD.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
N. Venkatesan, S. Pasupathy, B. Gobinathan
Summary: This research proposes a hybrid framework for diagnosing lung cancer using CT images. The framework combines DLBP and hybrid WPHT for feature extraction, and optimizes the classifier performance through feature selection and parameter tuning algorithms. Experimental results show that the proposed method outperforms other methods in several metrics.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Adem Polat
Summary: The primary goal of this study was to reduce computation time and improve efficiency of 3D image reconstruction in bioimaging applications. The proposed mining-ART technique outperformed traditional ART method in terms of speed without sacrificing image quality. Integration of compressed sensing-based 3DTV into mining-ART was also explored.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Anita Gade, V. Vijaya Baskar, John Panneerselvam
Summary: Analysis of exhaled breath is an increasingly used diagnostic technique in medicine. This study introduces a new NICBGM-based model that utilizes various features and weight optimization for accurate data interpretation and result optimization.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
