Relevant bibliographies by topics / EEG DENOISING

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'EEG DENOISING'

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Published: 11 September 2023

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Journal articles on the topic "EEG DENOISING"

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An, Yang, Hak Keung Lam, and Sai Ho Ling. "Auto-Denoising for EEG Signals Using Generative Adversarial Network." Sensors 22, no. 5 (February 23, 2022): 1750. http://dx.doi.org/10.3390/s22051750.

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The brain–computer interface (BCI) has many applications in various fields. In EEG-based research, an essential step is signal denoising. In this paper, a generative adversarial network (GAN)-based denoising method is proposed to denoise the multichannel EEG signal automatically. A new loss function is defined to ensure that the filtered signal can retain as much effective original information and energy as possible. This model can imitate and integrate artificial denoising methods, which reduces processing time; hence it can be used for a large amount of data processing. Compared to other neural network denoising models, the proposed model has one more discriminator, which always judges whether the noise is filtered out. The generator is constantly changing the denoising way. To ensure the GAN model generates EEG signals stably, a new normalization method called sample entropy threshold and energy threshold-based (SETET) normalization is proposed to check the abnormal signals and limit the range of EEG signals. After the denoising system is established, although the denoising model uses the different subjects’ data for training, it can still apply to the new subjects’ data denoising. The experiments discussed in this paper employ the HaLT public dataset. Correlation and root mean square error (RMSE) are used as evaluation criteria. Results reveal that the proposed automatic GAN denoising network achieves the same performance as the manual hybrid artificial denoising method. Moreover, the GAN network makes the denoising process automatic, representing a significant reduction in time.
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Elsherbieny, Zeinab, Nagy Messiha, Adel S. El-Fisawy, Mohamed Rihan, and Fathi E. Abd El-Samie. "Efficient Denoising Schemes of EEG Signals." Menoufia Journal of Electronic Engineering Research 28, no. 1 (December 1, 2019): 209–13. http://dx.doi.org/10.21608/mjeer.2019.77020.

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Grobbelaar, Maximilian, Souvik Phadikar, Ebrahim Ghaderpour, Aaron F. Struck, Nidul Sinha, Rajdeep Ghosh, and Md Zaved Iqubal Ahmed. "A Survey on Denoising Techniques of Electroencephalogram Signals Using Wavelet Transform." Signals 3, no. 3 (August 17, 2022): 577–86. http://dx.doi.org/10.3390/signals3030035.

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Electroencephalogram (EEG) artifacts such as eyeblink, eye movement, and muscle movements widely contaminate the EEG signals. Those unwanted artifacts corrupt the information contained in the EEG signals and degrade the performance of qualitative analysis of clinical applications and as well as EEG-based brain–computer interfaces (BCIs). The applications of wavelet transform in denoising EEG signals are increasing day by day due to its capability of handling non-stationary signals. All the reported wavelet denoising techniques for EEG signals are surveyed in this paper in terms of the quality of noise removal and retrieving important information. In order to evaluate the performance of wavelet denoising techniques for EEG signals and to express the quality of reconstruction, the techniques were evaluated based on the results shown in the respective literature. We also compare certain features in the evaluation of the wavelet denoising techniques, such as the requirement of reference channel, automation, online, and performance on a single channel.
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Zhao, Haoyan, and Bin Guo. "EEG Signal Denoising Based on Deep Residual Shrinkage Network." Journal of Physics: Conference Series 2395, no. 1 (December 1, 2022): 012076. http://dx.doi.org/10.1088/1742-6596/2395/1/012076.

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Abstract In recent years, EEG signals are usually denoised by traditional algorithms, which suffer from large amounts of computation, a limited number of channels, or modal aliasing. To achieve better denoising, a deep residual contraction network is proposed to denoise EEG signals. At the same time, on the basis of the original residual shrinkage building units, a new soft threshold module is used to replace the ReLU function to construct a new network model. Through different groups of denoising experiments, the effectiveness of this denoising algorithm is verified.
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PERDHANA, HASBIAN FAUZY, and HASBALLAH ZAKARIA. "Pembersihan Artefak EOG dari Sinyal EEG menggunakan Denoising Autoencoder." ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika 10, no. 3 (July 19, 2022): 639. http://dx.doi.org/10.26760/elkomika.v10i3.639.

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ABSTRAKElektroensefalografi (EEG) adalah teknik perekaman yang merekam aktivitas elektrik pada otak menggunakan elektroda yang ditempelkan pada kulit kepala. Artefak elektrookulografi (EOG) adalah salah satu artefak yang kerap muncul pada perekaman EEG dikarenakan pergerakan mata dan menyebabkan sinyal EEG berubah bentuk. Untuk membersihkan EEG, artefak perlu dibuang dengan tetap menjaga informasi penting dari EEG. Pada penelitian ini kami mendeteksi artefak EOG menggunakan Independent Component Analysis (ICA) dan deteksi puncak, dan untuk rekonstruksi sinyal EEG kami menggunakan Denoising Autoencoder (DAE). Pada penelitian ini kami meneliti model DAE apakah dapat merekonstruksi sinyal EEG dari artefak EOG. Metode pendeteksian artefak mendapatkan 85% sensitivitas dan 83% Positive Predictive Value (PPV) pada dataset sekunder dan 82% sensitivitas pada dataset primer. Model DAE dilatih dengan validasi silang 10 lipat dan mendapatkan rerata mean squared error (MSE) 0,007±0,008. Penelitian ini membuktikan kemampuan DAE untuk merekonstruksi sinyal EEG denganmasukan segmen sinyal EEG terkontaminasi artefak EOG.Kata kunci: EEG, Artefak EOG, Denoising Autoencoder ABSTRACTThe Electroencephalography (EEG) is a recording technique to record electrical activity on the brain using electrodes attached to the head scalp. Electrooculography (EOG) is one of the artifacts that are prone to appear on EEG due to eye movement and cause EEG signals to deform. To fix the EEG signal, we need to remove artifacts while conserving EEG information. In this research, we detect EOG artifactual signal using Independent Component Analysis (ICA) and peak detection and used a generative model Denoising Autoencoder (DAE) to reconstruct clean EEG by using EEG artifact-corrupted signal. Our artifact detection method scores 85% sensitivity and 83% Positive Predictive Value on the secondary dataset and 82% sensitivity on the primary dataset. We train the DAE model with 10-fold cross-validation and got 0.007 ± 0.008 Mean Squared Error (MSE). We demonstrated DAE on its ability to generate a clean EEG segment by feeding it contaminated EEG segment.Keywords: EEG, Eye movement artifact, Denoising Autoencoder
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Yan, Wenqiang, Chenghang Du, Yongcheng Wu, Xiaowei Zheng, and Guanghua Xu. "SSVEP-EEG Denoising via Image Filtering Methods." IEEE Transactions on Neural Systems and Rehabilitation Engineering 29 (2021): 1634–43. http://dx.doi.org/10.1109/tnsre.2021.3104825.

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UTHAYAKUMAR, R., and D. EASWARAMOORTHY. "MULTIFRACTAL-WAVELET BASED DENOISING IN THE CLASSIFICATION OF HEALTHY AND EPILEPTIC EEG SIGNALS." Fluctuation and Noise Letters 11, no. 04 (December 2012): 1250034. http://dx.doi.org/10.1142/s0219477512500344.

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Identification of abnormality in Electroencephalogram (EEG) signals is the vast area of research in the neuroscience. Especially, the classification of healthy and epileptic subjects through EEG signals is the crucial problem in the biomedical sciences. Denoising of EEG signals is another important task in signal processing. The noises must be corrected or reduced before the subsequent decision analysis. This paper presents a wavelet-based denoising method for the recovery of EEG signal contaminated by nonstationary noises and investigates the recognition of healthy and epileptic EEG signals by using multifractal measures such as Generalized Fractal Dimensions. The multifractal measures show the significant differences among normal, interictal and epileptic ictal EEGs with denoising by wavelet transform as the pre-processing step. The denoised artifact-free EEG presents a very good improvement in the identification rate of epileptic seizure. The proposed scheme illustrates with high accuracy through the suitable graphical and statistical tools and performs an important role in the epileptic seizure detection.
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Zhang, Zhen, Xiaoyan Yu, Xianwei Rong, and Makoto Iwata. "A Novel Multimodule Neural Network for EEG Denoising." IEEE Access 10 (2022): 49528–41. http://dx.doi.org/10.1109/access.2022.3173261.

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Turnip, Arjon, and Jasman Pardede. "Artefacts Removal of EEG Signals with Wavelet Denoising." MATEC Web of Conferences 135 (2017): 00058. http://dx.doi.org/10.1051/matecconf/201713500058.

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Li, Junhua, Zbigniew Struzik, Liqing Zhang, and Andrzej Cichocki. "Feature learning from incomplete EEG with denoising autoencoder." Neurocomputing 165 (October 2015): 23–31. http://dx.doi.org/10.1016/j.neucom.2014.08.092.

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Dissertations / Theses on the topic "EEG DENOISING"

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Zhang, Shuoyue [Verfasser], and Jürgen [Akademischer Betreuer] Hennig. "Artifacts denoising of EEG acquired during simultaneous EEG-FMRI." Freiburg : Universität, 2021. http://d-nb.info/1228786968/34.

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Becker, Hanna. "Débruitage, séparation et localisation de sources EEG dans le contexte de l'épilepsie." Thesis, Nice, 2014. http://www.theses.fr/2014NICE4075/document.

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L'électroencéphalographie (EEG) est une technique qui est couramment utilisée pour le diagnostic et le suivi de l'épilepsie. L'objectif de cette thèse consiste à fournir des algorithmes pour l'extraction, la séparation, et la localisation de sources épileptiques à partir de données EEG. D'abord, nous considérons deux étapes de prétraitement. La première étape vise à éliminer les artéfacts musculaires à l'aide de l'analyse en composantes indépendantes (ACI). Dans ce contexte, nous proposons un nouvel algorithme par déflation semi-algébrique qui extrait les sources épileptiques de manière plus efficace que les méthodes conventionnelles, ce que nous démontrons sur données EEG simulées et réelles. La deuxième étape consiste à séparer des sources corrélées. A cette fin, nous étudions des méthodes de décomposition tensorielle déterministe exploitant des données espace-temps-fréquence ou espace-temps-vecteur-d'onde. Nous comparons les deux méthodes de prétraitement à l'aide de simulations pour déterminer dans quels cas l'ACI, la décomposition tensorielle, ou une combinaison des deux approches devraient être utilisées. Ensuite, nous traitons la localisation de sources distribuées. Après avoir présenté et classifié les méthodes de l'état de l'art, nous proposons un algorithme pour la localisation de sources distribuées qui s'appuie sur les résultats du prétraitement tensoriel. L'algorithme est évalué sur données EEG simulées et réelles. En plus, nous apportons quelques améliorations à une méthode de localisation de sources basée sur la parcimonie structurée. Enfin, une étude des performances de diverses méthodes de localisation de sources est conduite sur données EEG simulées
Electroencephalography (EEG) is a routinely used technique for the diagnosis and management of epilepsy. In this context, the objective of this thesis consists in providing algorithms for the extraction, separation, and localization of epileptic sources from the EEG recordings. In the first part of the thesis, we consider two preprocessing steps applied to raw EEG data. The first step aims at removing muscle artifacts by means of Independent Component Analysis (ICA). In this context, we propose a new semi-algebraic deflation algorithm that extracts the epileptic sources more efficiently than conventional methods as we demonstrate on simulated and real EEG data. The second step consists in separating correlated sources that can be involved in the propagation of epileptic phenomena. To this end, we explore deterministic tensor decomposition methods exploiting space-time-frequency or space-time-wave-vector data. We compare the two preprocessing methods using computer simulations to determine in which cases ICA, tensor decomposition, or a combination of both should be used. The second part of the thesis is devoted to distributed source localization techniques. After providing a survey and a classification of current state-of-the-art methods, we present an algorithm for distributed source localization that builds on the results of the tensor-based preprocessing methods. The algorithm is evaluated on simulated and real EEG data. Furthermore, we propose several improvements of a source imaging method based on structured sparsity. Finally, a comprehensive performance study of various brain source imaging methods is conducted on physiologically plausible, simulated EEG data
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Hajipour, Sardouie Sepideh. "Signal subspace identification for epileptic source localization from electroencephalographic data." Thesis, Rennes 1, 2014. http://www.theses.fr/2014REN1S185/document.

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Lorsque l'on enregistre l'activité cérébrale en électroencéphalographie (EEG) de surface, le signal d'intérêt est fréquemment bruité par des activités différentes provenant de différentes sources de bruit telles que l'activité musculaire. Le débruitage de l'EEG est donc une étape de pré-traitement important dans certaines applications, telles que la localisation de source. Dans cette thèse, nous proposons six méthodes permettant la suppression du bruit de signaux EEG dans le cas particulier des activités enregistrées chez les patients épileptiques soit en période intercritique (pointes) soit en période critique (décharges). Les deux premières méthodes, qui sont fondées sur la décomposition généralisée en valeurs propres (GEVD) et sur le débruitage par séparation de sources (DSS), sont utilisées pour débruiter des signaux EEG épileptiques intercritiques. Pour extraire l'information a priori requise par GEVD et DSS, nous proposons une série d'étapes de prétraitement, comprenant la détection de pointes, l'extraction du support des pointes et le regroupement des pointes impliquées dans chaque source d'intérêt. Deux autres méthodes, appelées Temps Fréquence (TF) -GEVD et TF-DSS, sont également proposées afin de débruiter les signaux EEG critiques. Dans ce cas on extrait la signature temps-fréquence de la décharge critique par la méthode d'analyse de corrélation canonique. Nous proposons également une méthode d'Analyse en Composantes Indépendantes (ICA), appelé JDICA, basée sur une stratégie d'optimisation de type Jacobi. De plus, nous proposons un nouvel algorithme direct de décomposition canonique polyadique (CP), appelé SSD-CP, pour calculer la décomposition CP de tableaux à valeurs complexes. L'algorithme proposé est basé sur la décomposition de Schur simultanée (SSD) de matrices particulières dérivées du tableau à traiter. Nous proposons également un nouvel algorithme pour calculer la SSD de plusieurs matrices à valeurs complexes. Les deux derniers algorithmes sont utilisés pour débruiter des données intercritiques et critiques. Nous évaluons la performance des méthodes proposées pour débruiter les signaux EEG (simulés ou réels) présentant des activités intercritiques et critiques épileptiques bruitées par des artéfacts musculaires. Dans le cas des données simulées, l'efficacité de chacune de ces méthodes est évaluée d'une part en calculant l'erreur quadratique moyenne normalisée entre les signaux originaux et débruités, et d'autre part en comparant les résultats de localisation de sources, obtenus à partir des signaux non bruités, bruités, et débruités. Pour les données intercritiques et critiques, nous présentons également quelques exemples sur données réelles enregistrées chez des patients souffrant d'épilepsie partielle
In the process of recording electrical activity of the brain, the signal of interest is usually contaminated with different activities arising from various sources of noise and artifact such as muscle activity. This renders denoising as an important preprocessing stage in some ElectroEncephaloGraphy (EEG) applications such as source localization. In this thesis, we propose six methods for noise cancelation of epileptic signals. The first two methods, which are based on Generalized EigenValue Decomposition (GEVD) and Denoising Source Separation (DSS) frameworks, are used to denoise interictal data. To extract a priori information required by GEVD and DSS, we propose a series of preprocessing stages including spike peak detection, extraction of exact time support of spikes and clustering of spikes involved in each source of interest. Two other methods, called Time Frequency (TF)-GEVD and TF-DSS, are also proposed in order to denoise ictal EEG signals for which the time-frequency signature is extracted using the Canonical Correlation Analysis method. We also propose a deflationary Independent Component Analysis (ICA) method, called JDICA, that is based on Jacobi-like iterations. Moreover, we propose a new direct algorithm, called SSD-CP, to compute the Canonical Polyadic (CP) decomposition of complex-valued multi-way arrays. The proposed algorithm is based on the Simultaneous Schur Decomposition (SSD) of particular matrices derived from the array to process. We also propose a new Jacobi-like algorithm to calculate the SSD of several complex-valued matrices. The last two algorithms are used to denoise both interictal and ictal data. We evaluate the performance of the proposed methods to denoise both simulated and real epileptic EEG data with interictal or ictal activity contaminated with muscular activity. In the case of simulated data, the effectiveness of the proposed algorithms is evaluated in terms of Relative Root Mean Square Error between the original noise-free signals and the denoised ones, number of required ops and the location of the original and denoised epileptic sources. For both interictal and ictal data, we present some examples on real data recorded in patients with a drug-resistant partial epilepsy
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Romo, Vazquez Rebeca del Carmen. "Contribution à la détection et à l'analyse des signaux EEG épileptiques : débruitage et séparation de sources." Thesis, Vandoeuvre-les-Nancy, INPL, 2010. http://www.theses.fr/2010INPL005N/document.

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L'objectif principal de cette thèse est le pré-traitement des signaux d'électroencéphalographie (EEG). En particulier, elle vise à développer une méthodologie pour obtenir un EEG dit "propre" à travers l'identification et l'élimination des artéfacts extra-cérébraux (mouvements oculaires, clignements, activité cardiaque et musculaire) et du bruit. Après identification, les artéfacts et le bruit doivent être éliminés avec une perte minimale d'information, car dans le cas d'EEG, il est de grande importance de ne pas perdre d'information potentiellement utile à l'analyse (visuelle ou automatique) et donc au diagnostic médical. Plusieurs étapes sont nécessaires pour atteindre cet objectif : séparation et identification des sources d'artéfacts, élimination du bruit de mesure et reconstruction de l'EEG "propre". A travers une approche de type séparation aveugle de sources (SAS), la première partie vise donc à séparer les signaux EEG dans des sources informatives cérébrales et des sources d'artéfacts extra-cérébraux à éliminer. Une deuxième partie vise à classifier et éliminer les sources d'artéfacts et elle consiste en une étape de classification supervisée. Le bruit de mesure, quant à lui, il est éliminé par une approche de type débruitage par ondelettes. La mise en place d'une méthodologie intégrant d'une manière optimale ces trois techniques (séparation de sources, classification supervisée et débruitage par ondelettes) constitue l'apport principal de cette thèse. La méthodologie développée, ainsi que les résultats obtenus sur une base de signaux d'EEG réels (critiques et inter-critiques) importante, sont soumis à une expertise médicale approfondie, qui valide l'approche proposée
The goal of this research is the electroencephalographic (EEG) signals preprocessing. More precisely, we aim to develop a methodology to obtain a "clean" EEG through the extra- cerebral artefacts (ocular movements, eye blinks, high frequency and cardiac activity) and noise identification and elimination. After identification, the artefacts and noise must be eliminated with a minimal loss of cerebral activity information, as this information is potentially useful to the analysis (visual or automatic) and therefore to the medial diagnosis. To accomplish this objective, several pre-processing steps are needed: separation and identification of the artefact sources, noise elimination and "clean" EEG reconstruction. Through a blind source separation (BSS) approach, the first step aims to separate the EEG signals into informative and artefact sources. Once the sources are separated, the second step is to classify and to eliminate the identified artefacts sources. This step implies a supervised classification. The EEG is reconstructed only from informative sources. The noise is finally eliminated using a wavelet denoising approach. A methodology ensuring an optimal interaction of these three techniques (BSS, classification and wavelet denoising) is the main contribution of this thesis. The methodology developed here, as well the obtained results from an important real EEG data base (ictal and inter-ictal) is subjected to a detailed analysis by medical expertise, which validates the proposed approach
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NIBHANUPUDI, SWATHI. "SIGNAL DENOISING USING WAVELETS." University of Cincinnati / OhioLINK, 2003. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1070577417.

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Villaron, Emilie. "Modèles aléatoires harmoniques pour les signaux électroencéphalographiques." Thesis, Aix-Marseille, 2012. http://www.theses.fr/2012AIXM4815.

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Cette thèse s'inscrit dans le contexte de l'analyse des signaux biomédicaux multicapteurs par des méthodes stochastiques. Les signaux auxquels nous nous intéressons présentent un caractère oscillant transitoire bien représenté par les décompositions dans le plan temps-fréquence c'est pourquoi nous avons choisi de considérer non plus les décours temporels de ces signaux mais les coefficients issus de la décomposition de ces derniers dans le plan temps-fréquence. Dans une première partie, nous décomposons les signaux multicapteurs sur une base de cosinus locaux (appelée base MDCT) et nous modélisons les coefficients à l'aide d'un modèle à états latents. Les coefficients sont considérés comme les réalisations de processus aléatoires gaussiens multivariés dont la distribution est gouvernée par une chaîne de Markov cachée. Nous présentons les algorithmes classiques liés à l'utilisation des modèles de Markov caché et nous proposons une extension dans le cas où les matrices de covariance sont factorisées sous forme d'un produit de Kronecker. Cette modélisation permet de diminuer la complexité des méthodes de calcul numérique utilisées tout en stabilisant les algorithmes associés. Nous appliquons ces modèles à des données électroencéphalographiques et nous montrons que les matrices de covariance représentant les corrélations entre les capteurs et les fréquences apportent des informations pertinentes sur les signaux analysés. Ceci est notamment illustré par un cas d'étude sur la caractérisation de la désynchronisation des ondes alpha dans le contexte de la sclérose en plaques
This thesis adresses the problem of multichannel biomedical signals analysis using stochastic methods. EEG signals exhibit specific features that are both time and frequency localized, which motivates the use of time-frequency signal representations. In this document the (time-frequency labelled) coefficients are modelled as multivariate random variables. In the first part of this work, multichannel signals are expanded using a local cosine basis (called MDCT basis). The approach we propose models the distribution of time-frequency coefficients (here MDCT coefficients) in terms of latent variables by the use of a hidden Markov model. In the framework of application to EEG signals, the latent variables describe some hidden mental state of the subject. The latter control the covariance matrices of Gaussian vectors of fixed-time vectors of multi-channel, multi-frequency, MDCT coefficients. After presenting classical algorithms to estimate the parameters, we define a new model in which the (space-frequency) covariance matrices are expanded as tensor products (also named Kronecker products) of frequency and channels matrices. Inference for the proposed model is developped and yields estimates for the model parameters, together with maximum likelihood estimates for the sequences of latent variables. The model is applied to electroencephalogram data, and it is shown that variance-covariance matrices labelled by sensor and frequency indices can yield relevant informations on the analyzed signals. This is illustrated with a case study, namely the detection of alpha waves in rest EEG for multiple sclerosis patients and control subjects
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Tikkanen, P. (Pauli). "Characterization and application of analysis methods for ECG and time interval variability data." Doctoral thesis, University of Oulu, 1999. http://urn.fi/urn:isbn:9514252144.

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Abstract The quantitation of the variability in cardiovascular signals provides information about the autonomic neural regulation of the heart and the circulatory system. Several factors have an indirect effect on these signals as well as artifacts and several types of noise are contained in the recorded signal. The dynamics of RR and QT interval time series have also been analyzed in order to predict a risk of adverse cardiac events and to diagnose them. An ambulatory measurement setting is an important and demanding condition for the recording and analysis of these signals. Sophisticated and robust signal analysis schemes are thus increasingly needed. In this thesis, essential points related to ambulatory data acquisition and analysis of cardiovascular signals are discussed including the accuracy and reproducibility of the variability measurement. The origin of artifacts in RR interval time series is discussed, and consequently their effects and possible correction procedures are concidered. The time series including intervals differing from a normal sinus rhythm which sometimes carry important information, but may not be as such suitable for an analysis performed by all approaches. A significant variation in the results in either intra- or intersubject analysis is unavoidable and should be kept in mind when interpreting the results. In addition to heart rate variability (HRV) measurement using RR intervals, the dy- namics of ventricular repolarization duration (VRD) is considered using the invasively obtained action potential duration (APD) and different estimates for a QT interval taken from a surface electrocardiogram (ECG). Estimating the low quantity of the VRD vari- ability involves obviously potential errors and more strict requirements. In this study, the accuracy of VRD measurement was improved by a better time resolution obtained through interpolating the ECG. Furthermore, RTmax interval was chosen as the best QT interval estimate using simulated noise tests. A computer program was developed for the time interval measurement from ambulatory ECGs. This thesis reviews the most commonly used analysis methods for cardiovascular vari- ability signals including time and frequency domain approaches. The estimation of the power spectrum is presented on the approach using an autoregressive model (AR) of time series, and a method for estimating the powers and the spectra of components is also presented. Time-frequency and time-variant spectral analysis schemes with applica- tions to HRV analysis are presented. As a novel approach, wavelet and wavelet packet transforms and the theory of signal denoising with several principles for the threshold selection is examined. The wavelet packet based noise removal approach made use of an optimized signal decomposition scheme called best tree structure. Wavelet and wavelet packet transforms are further used to test their effciency in removing simulated noise from the ECG. The power spectrum analysis is examined by means of wavelet transforms, which are then applied to estimate the nonstationary RR interval variability. Chaotic modelling is discussed with important questions related to HRV analysis.ciency in removing simulated noise from the ECG. The power spectrum analysis is examined by means of wavelet transforms, which are then applied to estimate the nonstationary RR interval variability. Chaotic modelling is discussed with important questions related to HRV analysis.
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Pantelopoulos, Alexandros A. "¿¿¿¿¿¿¿¿¿¿¿¿PROGNOSIS: A WEARABLE SYSTEM FOR HEALTH MONITORING OF PEOPLE AT RISK." Wright State University / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=wright1284754643.

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Akhbari, Mahsa. "Analyse des intervalles ECG inter- et intra-battement sur des modèles d'espace d'état et de Markov cachés." Thesis, Université Grenoble Alpes (ComUE), 2016. http://www.theses.fr/2016GREAT026.

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Les maladies cardiovasculaires sont l'une des principales causes de mortalité chez l'homme. Une façon de diagnostiquer des maladies cardiaques et des anomalies est le traitement de signaux cardiaques tels que le ECG. Dans beaucoup de ces traitements, des caractéristiques inter-battements et intra-battements de signaux ECG doivent être extraites. Ces caractéristiques comprennent les points de repère des ondes de l’ECG (leur début, leur fin et leur point de pic), les intervalles significatifs et les segments qui peuvent être définis pour le signal ECG. L'extraction des points de référence de l'ECG consiste à identifier l'emplacement du pic, de début et de la fin de l'onde P, du complexe QRS et de l'onde T. Ces points véhiculent des informations cliniquement utiles, mais la segmentation precise de chaque battement de l'ECG est une tâche difficile, même pour les cardiologues expérimentés.Dans cette thèse, nous utilisons un cadre bayésien basé sur le modèle dynamique d'ECG proposé par McSharry. Depuis ce modèle s'appuyant sur la morphologie des ECG, il peut être utile pour la segmentation et l'analyse d'intervalles d'ECG. Afin de tenir compte de la séquentialité des ondes P, QRS et T, nous utiliserons également l'approche de Markov et des modèles de Markov cachés (MMC). En bref dans cette thèse, nous utilisons un modèle dynamique (filtre de Kalman), un modèle séquentiel (MMC) et leur combinaison (commutation de filtres de Kalman (SKF)). Nous proposons trois méthodes à base de filtres de Kalman, une méthode basée sur les MMC et un procédé à base de SKF. Nous utilisons les méthodes proposées pour l'extraction de points de référence et l'analyse d'intervalles des ECG. Le méthodes basées sur le filtrage de Kalman sont également utilisés pour le débruitage d'ECG, la détection de l'alternation de l'onde T, et la détection du pic R de l'ECG du foetus.Pour évaluer les performances des méthodes proposées pour l'extraction des points de référence de l'ECG, nous utilisons la base de données "Physionet QT", et une base de données "Swine" qui comprennent ECG annotations de signaux par les médecins. Pour le débruitage d'ECG, nous utilisons les bases de données "MIT-BIH Normal Sinus Rhythm", "MIT-BIH Arrhythmia" et "MIT-BIH noise stress test". La base de données "TWA Challenge 2008 database" est utilisée pour la détection de l'alternation de l'onde T. Enfin, la base de données "Physionet Computing in Cardiology Challenge 2013 database" est utilisée pour la détection du pic R de l'ECG du feotus. Pour l'extraction de points de reference, la performance des méthodes proposées sont évaluées en termes de moyenne, écart-type et l'erreur quadratique moyenne (EQM). Nous calculons aussi la sensibilité des méthodes. Pour le débruitage d'ECG, nous comparons les méthodes en terme d'amélioration du rapport signal à bruit
Cardiovascular diseases are one of the major causes of mortality in humans. One way to diagnose heart diseases and abnormalities is processing of cardiac signals such as ECG. In many of these processes, inter-beat and intra-beat features of ECG signal must be extracted. These features include peak, onset and offset of ECG waves, meaningful intervals and segments that can be defined for ECG signal. ECG fiducial point (FP) extraction refers to identifying the location of the peak as well as the onset and offset of the P-wave, QRS complex and T-wave which convey clinically useful information. However, the precise segmentation of each ECG beat is a difficult task, even for experienced cardiologists.In this thesis, we use a Bayesian framework based on the McSharry ECG dynamical model for ECG FP extraction. Since this framework is based on the morphology of ECG waves, it can be useful for ECG segmentation and interval analysis. In order to consider the time sequential property of ECG signal, we also use the Markovian approach and hidden Markov models (HMM). In brief in this thesis, we use dynamic model (Kalman filter), sequential model (HMM) and their combination (switching Kalman filter (SKF)). We propose three Kalman-based methods, an HMM-based method and a SKF-based method. We use the proposed methods for ECG FP extraction and ECG interval analysis. Kalman-based methods are also used for ECG denoising, T-wave alternans (TWA) detection and fetal ECG R-peak detection.To evaluate the performance of proposed methods for ECG FP extraction, we use the "Physionet QT database", and a "Swine ECG database" that include ECG signal annotations by physicians. For ECG denoising, we use the "MIT-BIH Normal Sinus Rhythm", "MIT-BIH Arrhythmia" and "MIT-BIH noise stress test" databases. "TWA Challenge 2008 database" is used for TWA detection and finally, "Physionet Computing in Cardiology Challenge 2013 database" is used for R-peak detection of fetal ECG. In ECG FP extraction, the performance of the proposed methods are evaluated in terms of mean, standard deviation and root mean square of error. We also calculate the Sensitivity for methods. For ECG denoising, we compare methods in their obtained SNR improvement
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Casaca, Wallace Correa de Oliveira. "Restauração de imagens digitais com texturas utilizando técnicas de decomposição e equações diferenciais parciais /." São José do Rio Preto : [s.n.], 2010. http://hdl.handle.net/11449/94247.

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Orientador: Maurílio Boaventura
Banca: Evanildo Castro Silva Júnior
Banca: Alagacone Sri Ranga
Resumo: Neste trabalho propomos quatro novas abordagens para tratar o problema de restauração de imagens reais contendo texturas sob a perspectiva dos temas: reconstrução de regiões danificadas, remoção de objetos, e eliminação de ruídos. As duas primeiras abor dagens são designadas para recompor partes perdias ou remover objetos de uma imagem real a partir de formulações envolvendo decomposiçãode imagens e inpainting por exem- plar, enquanto que as duas últimas são empregadas para remover ruído, cujas formulações são baseadas em decomposição de três termos e equações diferenciais parciais não lineares. Resultados experimentais atestam a boa performace dos protótipos apresentados quando comparados à modelagens correlatas da literatura.
Abstract: In this paper we propose four new approaches to address the problem of restoration of real images containing textures from the perspective of reconstruction of damaged areas, object removal, and denoising topics. The first two approaches are designed to reconstruct missing parts or to remove objects of a real image using formulations based on image de composition and exemplar based inpainting, while the last two other approaches are used to remove noise, whose formulations are based on decomposition of three terms and non- linear partial di®erential equations. Experimental results attest to the good performance of the presented prototypes when compared to modeling related in literature.
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Books on the topic "EEG DENOISING"

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Mourad, Talbi. ECG Denoising Based on Total Variation Denoising and Wavelets. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-25267-9.

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Wendling, Fabrice, Marco Congendo, and Fernando H. Lopes da Silva. EEG Analysis. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0044.

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This chapter addresses the analysis and quantification of electroencephalographic (EEG) and magnetoencephalographic (MEG) signals. Topics include characteristics of these signals and practical issues such as sampling, filtering, and artifact rejection. Basic concepts of analysis in time and frequency domains are presented, with attention to non-stationary signals focusing on time-frequency signal decomposition, analytic signal and Hilbert transform, wavelet transform, matching pursuit, blind source separation and independent component analysis, canonical correlation analysis, and empirical model decomposition. The behavior of these methods in denoising EEG signals is illustrated. Concepts of functional and effective connectivity are developed with emphasis on methods to estimate causality and phase and time delays using linear and nonlinear methods. Attention is given to Granger causality and methods inspired by this concept. A concrete example is provided to show how information processing methods can be combined in the detection and classification of transient events in EEG/MEG signals.
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ECG Denoising Based on Total Variation Denoising and Wavelets. Springer International Publishing AG, 2023.

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Congendo, Marco, and Fernando H. Lopes da Silva. Event-Related Potentials. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0039.

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Event-related potentials (ERPs) can be elicited by a variety of stimuli and events in diverse conditions. This chapter covers the methodology of analyzing and quantifying ERPs in general. Basic models (additive, phase modulation and resetting, potential asymmetry) that account for the generation of ERPs are discussed. The principles and requirements of ensemble time averaging are presented, along with several univariate and multivariate methods that have been proposed to improve the averaging procedure: wavelet decomposition and denoising, spatial, temporal and spatio-temporal filtering. We emphasize basic concepts of principal component analysis, common spatial pattern, and blind source separation, including independent component analysis. We cover practical questions related to the averaging procedure: overlapping ERPs, correcting inter-sweep latency and amplitude variability, alternative averaging methods (e.g., median), and estimation of ERP onset. Some specific aspects of ERP analysis in the frequency domain are surveyed, along with topographic analysis, statistical testing, and classification methods.
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Book chapters on the topic "EEG DENOISING"

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Peng, Weiwei. "EEG Preprocessing and Denoising." In EEG Signal Processing and Feature Extraction, 71–87. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9113-2_5.

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Freeman, Walter J., and Rodrigo Quian Quiroga. "Single-Trial Evoked Potentials: Wavelet Denoising." In Imaging Brain Function With EEG, 65–86. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-4984-3_5.

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Martišius, Ignas, and Robertas Damaševičius. "Class-Adaptive Denoising for EEG Data Classification." In Artificial Intelligence and Soft Computing, 302–9. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29350-4_36.

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Mert, A., and A. Akan. "EEG Denoising Based on Empirical Mode Decomposition and Mutual Information." In IFMBE Proceedings, 631–34. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-00846-2_156.

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Roy, Vandana, and Shailja Shukla. "Image Denoising by Data Adaptive and Non-Data Adaptive Transform Domain Denoising Method Using EEG Signal." In Proceedings of All India Seminar on Biomedical Engineering 2012 (AISOBE 2012), 9–20. India: Springer India, 2012. http://dx.doi.org/10.1007/978-81-322-0970-6_2.

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Manoj Kumar Swain, Chaudhuri, Ashish Singh, and Indrakanti Raghu. "Electroencephalogram (EEG) Signal Denoising Using Optimized Wavelet Transform (WT) A Study." In Computational Intelligence in Medical Decision Making and Diagnosis, 169–82. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003309451-11.

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Felja, Meryem, Asmae Bencheqroune, Mohamed Karim, and Ghita Bennis. "The Effectiveness Daubechies Wavelet and Conventional Filters in Denoising EEG Signal." In Digital Technologies and Applications, 991–99. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-29860-8_99.

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Alyasseri, Zaid Abdi Alkareem, Ahamad Tajudin Khader, Mohammed Azmi Al-Betar, Ammar Kamal Abasi, and Sharif Naser Makhadmeh. "EEG Signal Denoising Using Hybridizing Method Between Wavelet Transform with Genetic Algorithm." In Lecture Notes in Electrical Engineering, 449–69. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5281-6_31.

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Abtahi, F., F. Seoane, K. Lindecrantz, and N. Löfgren. "Elimination of ECG Artefacts in Foetal EEG Using Ensemble Average Subtraction and Wavelet Denoising Methods: A Simulation." In IFMBE Proceedings, 551–54. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-00846-2_136.

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Bhatnagar, Ankita, Krushna Gupta, Utkarsh Pandharkar, Ramchandra Manthalkar, and Narendra Jadhav. "Comparative Analysis of ICA, PCA-Based EASI and Wavelet-Based Unsupervised Denoising for EEG Signals." In Advances in Intelligent Systems and Computing, 749–59. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1513-8_76.

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Conference papers on the topic "EEG DENOISING"

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Chen, Yongjian, Masatake Akutagawa, Masato Katayama, Qinyu Zhang, and Yohsuke Kinouchi. "Neural network based EEG denoising." In 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2008. http://dx.doi.org/10.1109/iembs.2008.4649140.

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Guo, Jiahe, Hongxv Wang, Chenjie Zhang, and Xuemei Bai. "EEG Signals Denoising Using Bayesian Estimation." In 2020 IEEE International Conference on Advances in Electrical Engineering and Computer Applications (AEECA). IEEE, 2020. http://dx.doi.org/10.1109/aeeca49918.2020.9213615.

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Kaushal, Gautam, Amanpreet Singh, and V. K. Jain. "Better approach for denoising EEG signals." In 2016 5th International Conference on Wireless Networks and Embedded Systems (WECON). IEEE, 2016. http://dx.doi.org/10.1109/wecon.2016.7993455.

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Harender and R. K. Sharma. "EEG signal denoising based on wavelet transform." In 2017 International Conference of Electronics, Communication and Aerospace Technology (ICECA). IEEE, 2017. http://dx.doi.org/10.1109/iceca.2017.8203645.

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Gupta, Manish, Scott A. Beckett, and Elizabeth B. Klerman. "On-line EEG Denoising using correlated sparse recovery." In 2016 10th International Symposium on Medical Information and Communication Technology (ISMICT). IEEE, 2016. http://dx.doi.org/10.1109/ismict.2016.7498892.

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Looney, David, Valentin Goverdovsky, Preben Kidmose, and Danilo P. Mandic. "Subspace denoising of EEG artefacts via multivariate EMD." In ICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2014. http://dx.doi.org/10.1109/icassp.2014.6854491.

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Lins Caldas, Arthur Sena, Eanes Torres Pereira, Niago Moreira Nobre Leite, Arthur Dimitri Brito Oliveira, and Ellen Ribeiro Lucena. "Towards Automatic EEG Signal Denoising by Quality Metric Optimization." In 2020 International Joint Conference on Neural Networks (IJCNN). IEEE, 2020. http://dx.doi.org/10.1109/ijcnn48605.2020.9207504.

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Estrada, Edson, Homer Nazeran, Gustavo Sierra, Farideh Ebrahimi, and S. Kamaledin Setarehdan. "Wavelet-based EEG denoising for automatic sleep stage classification." In 2011 21st International Conference on Electrical Communications and Computers (CONIELECOMP). IEEE, 2011. http://dx.doi.org/10.1109/conielecomp.2011.5749325.

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Ivannikov, Andriy, Tommi Karkkainen, Tapani Ristaniemi, and Heikki Lyytinen. "Spatial weighted averaging for ERP denoising in EEG data." In 4th International Symposium on Communications, Control and Signal Processing (ISCCSP 2010). IEEE, 2010. http://dx.doi.org/10.1109/isccsp.2010.5463494.

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Pentari, Anastasia, George Tzagkarakis, Kostas Marias, and Panagiotis Tsakalides. "Graph-based Denoising of EEG Signals in Impulsive Environments." In 2020 28th European Signal Processing Conference (EUSIPCO). IEEE, 2021. http://dx.doi.org/10.23919/eusipco47968.2020.9287329.

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