Article
Engineering, Multidisciplinary
Hassan M. Aljohani
Summary: The paper presents a wavelet shrinkage model with noise variance estimated using exponential distribution, involving an epsilon-contamination prior distribution for robust priors in Bayesian analysis. The proposed method computes posterior mean, applies wavelet methods for signal reconstruction, and compares different approaches through extensive simulation and application on a real-life data set. The main focus is on comparing two different likelihoods - normal distribution and double exponential distribution.
ALEXANDRIA ENGINEERING JOURNAL
(2022)
Article
Automation & Control Systems
Ashish Kumar, Harshit Tomar, Virender Kumar Mehla, Rama Komaragiri, Manjeet Kumar
Summary: This paper studies various denoising techniques for removing noise from ECG signals, and proposes a denoising technique based on stationary wavelet transform, which outperforms other methods by preserving more ECG signal components.
Article
Engineering, Biomedical
Mahesh Chandra, Pankaj Goel, Ankita Anand, Asutosh Kar
Summary: The improved high-speed adaptive filter-based denoising architectures proposed in this paper outperform existing adaptive filter architectures and wavelet-based architectures, offering design flexibility and efficiency in denoising ECG signals in noisy environments for low-cost high-performance applications in the medical field. These architectures also require significantly less hardware compared to state-of-the-art wavelet-based architectures.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2021)
Article
Engineering, Biomedical
Yanrui Jin, Chengjin Qin, Jinlei Liu, Yunqing Liu, Zhiyuan Li, Chengliang Liu
Summary: This study proposes a novel method for denoising ECG signals based on deep wavelet convolutional neural network. The method utilizes convolution layers for automatic feature extraction and employs discrete wavelet transform to convert the signal into high-frequency and low-frequency components, compressing the input data while preserving effective information. Compared with existing methods, this method achieves better denoising performance under real noise conditions.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Changfang Chen, Minglei Shu, Shuwang Zhou, Zhaoyang Liu, Ruixia Liu
Summary: In this paper, a group-sparse signal denoising approach is proposed, which incorporates non-convex regularization and sparsity characteristics in the wavelet domain to estimate the electrocardiogram (ECG) signals with noise. A parameterized non-convex penalty function is introduced to strongly promote wavelet sparsity, and the strict convexity of the total cost function is guaranteed by identifying the interval for the parameter. By minimizing a certain single objective function, all the wavelet coefficients are estimated to retain the details of ECG signals and maintain the insignificant coefficients. The effectiveness of the proposed wavelet-domain group-sparse method (WDGS) for ECG signal enhancement is evaluated using real collected ECG signals and the MIT-BIH arrhythmia database through qualitative and quantitative analysis, demonstrating its ability to effectively suppress undesired noise and preserve the important morphology of ECG signals.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2023)
Article
Engineering, Biomedical
Navdeep Prashar, Meenakshi Sood, Shruti Jain
Summary: This paper evaluates the impact of denoising performance on ECG signals using dual-tree complex wavelet transform and proposes a new estimator for more effective results. The experimental results show that the universal modified threshold level-dependent threshold value selection with non-negative Garrote threshold function performs better.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2021)
Article
Engineering, Chemical
Xu Luo, Lihong Wang, Shufeng Cao, Qiuhan Xiao, Hongjuan Yang, Jianguo Zhao
Summary: In this paper, the enhanced metal magnetic memory testing method was used to minimize the negative influence of interrupting signals and improve the detection accuracy. The denoising effects of various methods on the detection signal and gradient signal were compared. The results showed that the enhanced method significantly increased the signal-to-noise ratio, and wavelet threshold, EMD and its improved methods were more applicable for denoising.
Article
Environmental Sciences
Bingzhe Dai, Jie Li, Jiahao Zhou, Yingting Zeng, Wenhao Hou, Junchao Zhang, Yao Wang, Qilin Zhang
Summary: In this paper, a modified empirical wavelet transform (MEWT) method was proposed to process natural lightning signal data. The experimental results demonstrated the adaptive processing capability of the method for lightning signals with different frequencies as well as the noise reduction effect for VLF lightning signals.
Article
Engineering, Multidisciplinary
Jianchun Guo, Zetian Si, Jiawei Xiang
Summary: This paper proposes a compound fault detection method using wavelet scattering transform and an improved soft threshold denoising algorithm to extract compound faults in bearings. Simulations and experiments have shown that this method can effectively detect compound faults in bearings.
Article
Computer Science, Artificial Intelligence
Siyuan Wang, Junjie Lv, Zhuonan He, Dong Liang, Yang Chen, Minghui Zhang, Qiegen Liu
Summary: Compressive sensing, utilizing undecimated wavelet transform and an iterative image reconstruction algorithm, aims to efficiently reconstruct MR images from a few under-sampled data in k-space. Experimental comparisons on different sampling trajectories and ratios validate the great potential of the proposed algorithm.
Article
Computer Science, Artificial Intelligence
Jun-Jie Huang, Pier Luigi Dragotti
Summary: The proposed WINNet method combines the advantages of wavelet-based methods and learning methods for image denoising, containing LINNs, sparse coding denoising, noise estimation networks, etc. By implementing nonlinear redundant transforms, sparse coding, and adaptively adjusting soft thresholds, WINNet method demonstrates strong generalization ability across different noise levels.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2022)
Article
Computer Science, Software Engineering
Xiaoxu Hu, Qingwen Yu, Hongpeng Yu
Summary: Cardio-cerebrovascular disease is one of the malignant diseases with the highest morbidity and mortality in the world. This paper proposes a method of denoising ECG signals by combining variational mode decomposition with wavelet soft-threshold, and the experimental results show that this method has good denoising effect.
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
(2022)
Review
Engineering, Biomedical
Syarifah Noor Syakiylla Sayed Daud, Rubita Sudirman
Summary: This review article comprehensively describes the application of the wavelet method in denoising the EEG signal based on recent research. It provides an overview of the basic theory and characteristics of EEG and the wavelet transform method, describes commonly applied wavelet-based methods for EEG dataset denoising, reviews a considerable number of the latest published EEG research works with wavelet applications, discusses challenges in current EEG-based wavelet method research, and recommends alternative solutions to mitigate the issues.
ANNALS OF BIOMEDICAL ENGINEERING
(2022)
Article
Automation & Control Systems
Inigo Monedero
Summary: This study presents a novel expert system for automatic ECG diagnosis of 13 different diseases using standard 12-lead ECGs. The system's rules replicate a specialist's diagnostic process but with the speed of an automatic system, achieving a reliability of 80.8% when validated by a specialist with over 20 years of experience. This system is considered a useful support tool for diagnosis due to its complexity and the number of diagnoses it covers.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Li Gao, Yi Gan, Juncheng Shi
Summary: This paper proposes an intelligent denoising method of ECG signals based on wavelet adaptive threshold and mathematical morphology, which can effectively remove high-frequency noise and low-frequency noise, and improve the denoising effect.
APPLIED INTELLIGENCE
(2022)
Article
Computer Science, Information Systems
Devanjali Relan, Lucia Ballerini, Emanuele Trucco, Tom MacGillivray
MULTIMEDIA TOOLS AND APPLICATIONS
(2019)
Article
Hematology
Abirami Veluchamy, Lucia Ballerini, Veronique Vitart, Katharina E. Schraut, Mirna Kirin, Harry Campbell, Peter K. Joshi, Devanjali Relan, Sarah Harris, Ellie Brown, Suraj S. Vaidya, Baljean Dhillon, Kaixin Zhou, Ewan R. Pearson, Caroline Hayward, Ozren Polasek, Ian J. Deary, Thomas MacGillivray, James F. Wilson, Emanuele Trucco, Colin N. A. Palmer, Alexander S. F. Doney
ARTERIOSCLEROSIS THROMBOSIS AND VASCULAR BIOLOGY
(2019)
Article
Mathematics, Applied
Devanjali Relan, Deepti Jain, Vrinda Mittal
JOURNAL OF DISCRETE MATHEMATICAL SCIENCES & CRYPTOGRAPHY
(2019)
Article
Engineering, Biomedical
D. Relan, R. Relan
BIOMEDICAL ENGINEERING LETTERS
(2018)
Meeting Abstract
Endocrinology & Metabolism
T. M. Li, C. Palmer, E. Pearson, T. MacGillivray, E. Trucco, L. Ballerini, R. Wang, D. Relan, G. J. Mckay, A. Doney
Proceedings Paper
Engineering, Biomedical
D. Relan, T. MacGillivray, L. Ballerini, E. Trucco
2013 35TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)
(2013)
Proceedings Paper
Mathematical & Computational Biology
E. Trucco, L. Ballerini, D. Relan, A. Giachetti, T. MacGillivray, K. Zutis, C. Lupascu, D. Tegolo, E. Pellegrini, G. Robertson, P. J. Wilson, A. Doney, B. Dhillon
2013 ISSNIP BIOSIGNALS AND BIOROBOTICS CONFERENCE (BRC)
(2013)
