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Journal articles on the topic 'WAVELET ENHANCED ICA'

Author: Grafiati

Published: 11 September 2023

Last updated: 31 July 2025

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1

Chiu, Chuang-Chien, Bui Huy Hai, Shoou-Jeng Yeh, and Ken Ying-Kai Liao. "RECOVERING EEG SIGNALS: MUSCLE ARTIFACT SUPPRESSION USING WAVELET-ENHANCED, INDEPENDENT COMPONENT ANALYSIS INTEGRATED WITH ADAPTIVE FILTER." Biomedical Engineering: Applications, Basis and Communications 26, no. 05 (2014): 1450063. http://dx.doi.org/10.4015/s101623721450063x.

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Independent component analysis (ICA) has been proven to be a powerful tool for removing artifacts from electroencephalogram (EEG) recordings in the form of blind source separation (BSS). Independent components (ICs) come from undesired sources that are mixed with the useful signal, and the assessment of such ICs allows them to be detected. But the unwanted ICs also can contain some useful information. To overcome this problem, wavelet-enhanced ICA (wICA) can be used, and this method applies a wavelet threshold for each wavelet coefficient to suppress abnormal deformation in each wavelet coeffi

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Ezilarasan, M. R., S. Vishnupriya, Thulasi Arumugam, B. Sakthi Karthi Durai, and S. Manimekalai. "Implementation of Realtime Image Fusion for Biomedical Applications Using ICA And Discrete Wavelet Transform." International Journal of Electrical and Electronics Research 12, no. 3 (2024): 836–41. http://dx.doi.org/10.37391/ijeer.120314.

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Image fusion is an extensively used technique in various areas like computer visualization, enhanced diagnostic imaging, radio therapy, automatic object recognition, image analysis, and remote sensing. The main aim of image fusion is to combine several input images into one image containing more information than the individual images. This type of image fusion results in a new image that is easier for computers and humans to see, making it possible for additional image processing operations like object detection, segmentation, and feature extraction. This paper examines the potential applicati

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Issa, Mohamed F., and Zoltan Juhasz. "Improved EOG Artifact Removal Using Wavelet Enhanced Independent Component Analysis." Brain Sciences 9, no. 12 (2019): 355. http://dx.doi.org/10.3390/brainsci9120355.

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Electroencephalography (EEG) signals are frequently contaminated with unwanted electrooculographic (EOG) artifacts. Blinks and eye movements generate large amplitude peaks that corrupt EEG measurements. Independent component analysis (ICA) has been used extensively in manual and automatic methods to remove artifacts. By decomposing the signals into neural and artifactual components and artifact components can be eliminated before signal reconstruction. Unfortunately, removing entire components may result in losing important neural information present in the component and eventually may distort

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Zhang, Wei, Zhongqiang Luo, Xingzhong Xiong, and Kai Deng. "An Enhanced Impulsive Noise Suppression Method Based on Wavelet Denoising and ICA for Power Line Communication." Infocommunications journal 13, no. 2 (2021): 25–31. http://dx.doi.org/10.36244/icj.2021.2.4.

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Aiming at the problem of noise suppression in power lines, traditional noise suppression methods need to know prior knowledge and other defects. In this paper, blind source separation methods that do not need prior knowledge are selected. In the case of low signal-to-noise ratio, the basic independent component analysis algorithm has poor denoising effect. Therefore, this paper proposes a joint independent component analysis algorithm based on Wavelet denoising and Power independent component analysis (WD-PowerICA). In this work, firstly, the pseudo observation signal is constructed by weighte

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Raju, Burra. "Brain Tumor Detection Using MRI." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem48988.

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ABSTRACT - Detecting and classifying tumors in brain MR images is crucial for early diagnosis and effective treatment. Early detection improves long-term survival rates. This paper presents a new approach for accurate brain tumor segmentation and classification. First, the MR images are enhanced using an anisotropic diffusion filter. Then, an active contour model is applied to detect the tumor, providing smooth and precise contours. For classification, features from the tumor images are extracted using 2-D Daubechies wavelet transform (DWT), and the feature dimensions are reduced with Independ

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Leng, Junfa, Penghui Shi, Shuangxi Jing, and Chenxu Luo. "An Enhanced CICA Method and Its Application to Multistage Gearbox Low-frequency Fault Feature Extraction." Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) 13, no. 2 (2020): 285–94. http://dx.doi.org/10.2174/2352096512666190130100336.

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Background: The vibration signals acquired from multistage gearbox’s slow-speed gear with localized fault may be directly mixed with source noise and measured noise. In addition, Constrained Independent Component Analysis (CICA) method has strong immunity to the measured noise but not to the source noise. These questions cause the difficulty for applying CICA method to directly extract lowfrequency and weak fault characteristic from the gear vibration signals with source noise. Methods: In order to extract the low-frequency and weak fault feature from the multistage gearbox, the source noise a

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Zima, M., P. Tichavský, K. Paul, and V. Krajča. "Robust removal of short-duration artifacts in long neonatal EEG recordings using wavelet-enhanced ICA and adaptive combining of tentative reconstructions." Physiological Measurement 33, no. 8 (2012): N39—N49. http://dx.doi.org/10.1088/0967-3334/33/8/n39.

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Zia, Zahra, Ahsen Ejaz, Kai Liang Lew, et al. "The Role of Electroencephalography in Advancing Sleep Research." International Journal on Robotics, Automation and Sciences 7, no. 1 (2025): 93–103. https://doi.org/10.33093/ijoras.2025.7.1.11.

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Electroencephalography (EEG) is fundamental in sleep research, providing critical insights into cerebral activity and significantly contributing to the diagnosis of sleep disorders. This study examines recent progress in EEG-based sleep research, emphasizing cutting-edge methods for sleep staging and disease identification. The amalgamation of machine learning and deep learning methodologies, encompassing hybrid models such as CNN-LSTM, has markedly improved the precision of sleep stage categorization and automated analysis. Enhancements in signal quality and dependability, especially by impro

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Abdubrani, Rafiuddin, Mahfuzah Mustafa, and Zarith Liyana Zahari. "A ROBUST FRAMEWORK FOR DRIVER FATIGUE DETECTION FROM EEG SIGNALS USING ENHANCEMENT OF MODIFIED Z-SCORE AND MULTIPLE MACHINE LEARNING ARCHITECTURES." IIUM Engineering Journal 24, no. 2 (2023): 354–72. http://dx.doi.org/10.31436/iiumej.v24i2.2799.

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Physiological signals, such as electroencephalogram (EEG), are used to observe a driver’s brain activities. A portable EEG system provides several advantages, including ease of operation, cost-effectiveness, portability, and few physical restrictions. However, it can be challenging to analyse EEG signals as they often contain various artefacts, including muscle activities, eye blinking, and unwanted noises. This study utilised an independent component analysis (ICA) approach to eliminate such unwanted signals from the unprocessed EEG data of 12 young, physically fit male participants between t

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Ma, Chau Thi, Hieu Nguyen Dinh, Kien Nguyen Minh, and Long Vu Thanh. "Research on Evaluating EEG Signal Processing Methods for the Development of BCI Systems." International Journal of Religion 6, no. 1 (2025): 377–95. https://doi.org/10.61707/s51hjt17.

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This article investigates the evaluation of EEG signal processing methods to enhance the development of Brain-Computer Interface (BCI) systems. Given the critical role of EEG signal quality in determining BCI performance, we explore the impact of various noise types—such as artifacts from eye movements, muscle activity, and electronic interference—on signal integrity. We focus on noise filtering techniques, particularly their effectiveness in preserving essential signal components while eliminating unwanted noise. Our research includes a detailed analysis of several common noise processing met

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Park, Seonghwan, Minseok Kim, Eunseo Baek, and Junghoon Park. "A Study on Preprocessing Method in Deep Learning for ICS Cyber Attack Detection." Korean Institute of Smart Media 12, no. 11 (2023): 36–47. http://dx.doi.org/10.30693/smj.2023.12.11.36.

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Industrial Control System(ICS), which controls facilities at major industrial sites, is increasingly connected to other systems through networks. With this integration and the development of intelligent attacks that can lead to a single external intrusion as a whole system paralysis, the risk and impact of security on industrial control systems are increasing. As a result, research on how to protect and detect cyber attacks is actively underway, and deep learning models in the form of unsupervised learning have achieved a lot, and many abnormal detection technologies based on deep learning are

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Shah, Kamal, Bahaaeldin Abdalla, Thabet Abdeljawad, and Iyad Suwan. "An efficient matrix method for coupled systems of variable fractional order differential equations." Thermal Science 27, Spec. issue 1 (2023): 195–210. http://dx.doi.org/10.2298/tsci23s1195s.

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We establish a powerful numerical algorithm to compute numerical solutions of coupled system of variable fractional order differential equations. Our numer?ical procedure is based on Bernstein polynomials. The mentioned polynomials are non-orthogonal and have the ability to produce good numerical results as compared to some other numerical method like wavelet. By variable fractional order differentiation and integration, some operational matrices are formed. On using the obtained matrices, the proposed coupled system is reduced to a system of algebraic equations. Using MATLAB, we solve the giv

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Yang, Junjie. "Application of Deep Learning in the Classification of EEG Signals of Parkinson's Disease Patients." Transactions on Computer Science and Intelligent Systems Research 5 (August 12, 2024): 905–14. http://dx.doi.org/10.62051/v4ps5h93.

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This article summarizes research on classifying electroencephalogram (EEG) signals of Parkinson's disease (PD) patients using deep learning technologies. Parkinson's disease is a common neurodegenerative disorder that severely affects quality of life. EEG signals provide valuable information about brain activity. Therefore, analyzing and classifying the EEG signals of PD patients can aid in early diagnosis and treatment. The paper outlines the assessment metrics and cross-validation methods in the diagnosis of Parkinson's disease, highlighting the effectiveness of deep learning in diagnosis. T

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Melinda, Melinda, Faris Zahran Jemi, Muliyadi Muliyadi, Rini Safitri, Muharratul Mina Rizky, and Maiza Duana. "Classification of autism features in electroencephalography recordings using random forest method." Sriwijaya Electrical and Computer Engineering Journal 1, no. 2 (2025): 54–63. https://doi.org/10.62420/selco.v1i2.9.

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Autism Spectrum Disorder (ASD) is a developmental disorder that significantly impacts communication, social interaction, and behavior in children, often leading to withdrawal, repetitive behaviors, and difficulties with eye contact. Traditional diagnostic methods primarily relied on behavioral assessments, which have proven insufficient in accuracy. This study aims to enhance ASD diagnosis by employing Electroencephalography (EEG) as an objective marker to differentiate between individuals with ASD and neurotypical individuals. Utilizing a dataset from King Abdulaziz University comprising 16 c

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"Performance Analysis of MSB Based Iris Recognition Using Hybrid Features Extraction Technique." International Journal of Engineering and Advanced Technology 8, no. 6 (2019): 230–39. http://dx.doi.org/10.35940/ijeat.e7292.088619.

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In the modern days, biometric identification is more promising and reliable to verify the human identity. Biometric refers to a science for analyzing the human characteristics such as physiological or behavioral patterns. Iris is a physiological trait, which is unique among all the biometric traits to recognize an individual effectively. In this paper, MSB based iris recognition based on Discrete Wavelet Transform, Independent Component Analysis and Binariezed Statistical Image Features is proposed. The left and right region is extracted from eye images using morphological operations. Binary s

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He, Jing, Zijun Huang, Yunde Li, et al. "Single-channel attention classification algorithm based on robust Kalman filtering and norm-constrained ELM." Frontiers in Human Neuroscience 18 (January 9, 2025). https://doi.org/10.3389/fnhum.2024.1481493.

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IntroductionAttention classification based on EEG signals is crucial for brain-computer interface (BCI) applications. However, noise interference and real-time signal fluctuations hinder accuracy, especially in portable single-channel devices. This study proposes a robust Kalman filtering method combined with a norm-constrained extreme learning machine (ELM) to address these challenges.MethodsThe proposed method integrates Discrete Wavelet Transformation (DWT) and Independent Component Analysis (ICA) for noise removal, followed by a robust Kalman filter enhanced with convex optimization to pre

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Kini, K. Ramakrishna, and Muddu Madakyaru. "Improved Process Monitoring Scheme Using Multi-Scale Independent Component Analysis." Arabian Journal for Science and Engineering, June 25, 2021. http://dx.doi.org/10.1007/s13369-021-05822-1.

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AbstractThe task of fault detection is crucial in modern chemical industries for improved product quality and process safety. In this regard, data-driven fault detection (FD) strategy based on independent component analysis (ICA) has gained attention since it improves monitoring by capturing non-gaussian features in the process data. However, presence of measurement noise in the process data degrades performance of the FD strategy since the noise masks important information. To enhance the monitoring under noisy environment, wavelet-based multi-scale filtering is integrated with the ICA model

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Arab, M. R., A. A. Suratgar, V. M. Martínez Hernández, and A. Rezaei Ashtiani. "Electroencephalogram Signals Processing for the Diagnosis of Petit mal and Grand mal Epilepsies Using an Artificial Neural Network." Journal of Applied Research and Technology 8, no. 01 (2010). http://dx.doi.org/10.22201/icat.16656423.2010.8.01.483.

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In this study, a novel wavelet transform‐neural network method is presented. The presented method is used for the classification of grand mal (clonic stage) and petit mal (absence) epilepsies into healthy, ictal and interictal (EEGs). Preprocessing is included to remove an artifact occurred by blinking and a wandering baseline (electrodes movement) as well as an eyeball movement artifact using the Discrete Wavelet Transformation (DWT). Denoising EEG signals from the AC power supply frequency with a suitable notch filter is another job of preprocessing. The preprocessing enhanced speed and accu

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Maheswari, K. Uma, and S. Sathiyamoorthy. "Fixed grid wavelet network segmentation on diffuse optical tomography image to detect sarcoma." Journal of Applied Research and Technology 16, no. 2 (2018). http://dx.doi.org/10.22201/icat.16656423.2018.16.2.706.

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Objective: To detect and explore the boundary of the sarcoma in Diffuse Optical Tomography (DOT) images, we need to extract the scattering and absorption property of the tissue at the cellular level. The DOT images suffer with lower optical resolution; therefore to improve the resolution in non-invasive imaging technique we apply Fixed Grid Wavelet Network (FGWN) image segmentation. Methods: We have subjected the reconstructed optical image to Vignette Correction to enhance the corners so that it traces the smooth boundary of tumor region. Fixed Grid Wavelet Network segmentation applied to red

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Xu, Yunyu, Wenbin Ji, Liqiao Hou, et al. "Enhanced CT-Based Radiomics to Predict Micropapillary Pattern Within Lung Invasive Adenocarcinoma." Frontiers in Oncology 11 (August 27, 2021). http://dx.doi.org/10.3389/fonc.2021.704994.

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ObjectiveWe aimed to investigate whether enhanced CT-based radiomics can predict micropapillary pattern (MPP) of lung invasive adenocarcinoma (IAC) in the pre-op phase and to develop an individual diagnostic predictive model for MPP in IAC.Methods170 patients who underwent complete resection for pathologically confirmed lung IAC were included in our study. Of these 121 were used as a training cohort and the other 49 as a test cohort. Clinical features and enhanced CT images were collected and assessed. Quantitative CT analysis was performed based on feature types including first order, shape,

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Rubio, E., and J. C. Jáuregui-Correa. "A Wavelet Approach to Estimate The Quality of Ground Parts." Journal of Applied Research and Technology 10, no. 1 (2012). http://dx.doi.org/10.22201/icat.16656423.2012.10.1.418.

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The manufacturing process of metal parts is subjected to machining instabilities that can degrade the surface finishquality and the reliability of products. Some instabilities are of transient nature and traditional inspection systems areunable to adequately estimate the grade of affectation in the finished parts. Time-frequency analyses are techniquesthat overcome these limitations and have recently been incorporated in special industry inspection equipment toimprove the quality of the manufactured parts. Grinding is a process used to get high precision and very smoothsurface in flat or cylin

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Mohammed, Rziza, El Aroussi Mohamed, El Hassouni Mohammed, Ghouzali Sanaa, and Aboutajdine Driss. "Local Curvelet Based Classification Using Linear Discriminant Analysis for Face Recognition." April 23, 2009. https://doi.org/10.5281/zenodo.1062918.

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In this paper, an efficient local appearance feature extraction method based the multi-resolution Curvelet transform is proposed in order to further enhance the performance of the well known Linear Discriminant Analysis(LDA) method when applied to face recognition. Each face is described by a subset of band filtered images containing block-based Curvelet coefficients. These coefficients characterize the face texture and a set of simple statistical measures allows us to form compact and meaningful feature vectors. The proposed method is compared with some related feature extraction methods such

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Hsieh, Jui-Chien, Hsing Shih, Ling-Lin Xin, Chung-Chi Yang, and Chi-Lu Han. "12-lead ECG signal processing and atrial fibrillation prediction in clinical practice." Technology and Health Care, September 1, 2022, 1–17. http://dx.doi.org/10.3233/thc-212925.

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BACKGROUND: Because clinically used 12-lead electrocardiography (ECG) devices have high falsepositive errors in automatic interpretations of atrial fibrillation (AF), they require substantial improvements before use. OBJECTIVE: A clinical 12-lead ECG pre-processing method with a parallel convolutional neural network (CNN) model for 12-lead ECG automatic AF recognition is introduced. METHODS: Raw AF diagnosis data from a 12-lead ECG device were collected and analyzed by two cardiologists to differentiate between true- and false-positives. Using a stationary wavelet transform (SWT) and independe

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Radhakrishnan, Menaka, Karthik Ramamurthy, Saranya Shanmugam, et al. "A hybrid model for the classification of Autism Spectrum Disorder using Mu rhythm in EEG." Technology and Health Care, July 15, 2024, 1–19. http://dx.doi.org/10.3233/thc-240644.

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BACKGROUND: Autism Spectrum Disorder (ASD) is a condition with social interaction, communication, and behavioral difficulties. Diagnostic methods mostly rely on subjective evaluations and can lack objectivity. In this research Machine learning (ML) and deep learning (DL) techniques are used to enhance ASD classification. OBJECTIVE: This study focuses on improving ASD and TD classification accuracy with a minimal number of EEG channels. ML and DL models are used with EEG data, including Mu Rhythm from the Sensory Motor Cortex (SMC) for classification. METHODS: Non-linear features in time and fr

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Zhang, Xin, Tianyang Fang, Jafar Saniie, Sasan Bakhtiari, and Alexander Heifetz. "Unsupervised learning-enabled pulsed infrared thermographic microscopy of subsurface defects in stainless steel." Scientific Reports 14, no. 1 (2024). http://dx.doi.org/10.1038/s41598-024-64214-1.

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AbstractMetallic structures produced with laser powder bed fusion (LPBF) additive manufacturing method (AM) frequently contain microscopic porosity defects, with typical approximate size distribution from one to 100 microns. Presence of such defects could lead to premature failure of the structure. In principle, structural integrity assessment of LPBF metals can be accomplished with nondestructive evaluation (NDE). Pulsed infrared thermography (PIT) is a non-contact, one-sided NDE method that allows for imaging of internal defects in arbitrary size and shape metallic structures using heat tran

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