Artifact Removal

Microwave imaging is a promising imaging modality for the detection of earlystage breast cancer. One of the most important signal processing components of microwave radar-based breast imaging is early-stage artifact removal. Several artifact removal algorithms have been reported in the literature. However, the neighbourhood-based skin subtraction and hybrid artifact removal algorithms have shown particularly promising results in different realistic 3D breast phantoms. For the first time in this paper, both algorithms have been evaluated and compared using the scan approaches of the most common microwave breast imaging prototype systems. The tests include 3D numerical as well as experimental breast phantoms scanned with hemispherical, cylindrical and adaptive scanning patterns. The efficacy of both algorithms has been evaluated across a range of appropriate performance metrics.

Removing artifacts from electroencephalography (EEG) signals is a common technique. Although numerous algorithms have been proposed, most rely solely on EEG data. In this study, we introduce a novel approach utilizing a hybrid convolutional neural network-long short-term memory (CNN-LSTM) architecture alongside simultaneous recording of facial and neck EMG signals. This setup enables the precise elimination of artifacts from the EEG signal. To validate the method, we collected a dataset from 24 participants who were presented with a light-emitting diode (LED) stimulus that elicited steady-state visual evoked potentials (SSVEPs) while they performed strong jaw clenching, an action known to induce significant artifacts. We then assessed the algorithm's ability to remove artifacts while preserving SSVEP responses. The results were compared against other commonly used algorithms, such as independent component analysis and linear regression. The findings demonstrate that the proposed method exhibits excellent performance, effectively removing artifacts while retaining the EEG signal's useful components.

Abstract: Brain tumor is inherently serious and life-threatening disease. Brain tumor is an abnormal growth of cells within the brain or inside the skull, which can be cancerous or noncancerous. Early detection and classification of brain tumors is very important in clinical practice. With recent development in the medical engineering and instruments, EEG instruments are able to record the brain electric activities with high accuracy, which establishes EEG as a primary tool for diagnosing the brain abnormalities. This paper represents earlier detection of brain tumor using artificial neural networks (ANNs).EEG signals carry the information of human brain. But EEG signal is contaminated with artifacts. These artifacts are removed by adaptative filtering. Then the spectral analysis method is applied for extracting generic features in an EEG signal. Fast Fourier Transform for spectral analysis is used to separate the signal features present in noise. The clean EEG is obtained and fed t...

Electrocardiogram (ECG) signal is a bio-electrical activity of the heart. It is a common routine and important cardiac diagnostic tool where in electrical signals are measured and recorded to know the functional status of heart, but ECG signal can be distorted with noise as, various artifacts corrupt the original ECG signal and reduces it quality. Therefore, there is a need to remove such artifacts from the original signal and improve its quality for better interpretation. Digital filters are used to remove noise error from the low frequency ECG signal and improve the accuracy the signal. Noise can be any interference due to motion artifacts or due to power equipment that are present where ECG had been taken. Thus, ECG signal processing has become a prevalent and effective tool for research and clinical practices. This paper presents the comparative analysis of FIR and IIR filters and their performances from the ECG signal for proper understanding and display of the ECG signal.

The aim of this series is to publish a Reference Library, including novel advances and developments in all aspects of Intelligent Systems in an easily accessible and well structured form. The series includes reference works, handbooks, compendia, textbooks, well-structured monographs, dictionaries, and encyclopedias. It contains well integrated knowledge and current information in the field of Intelligent Systems. The series covers the theory, applications, and design methods of Intelligent Systems. Virtually all disciplines such as engineering, computer science, avionics, business, e-commerce, environment, healthcare, physics and life science are included. The list of topics spans all the areas of modern intelligent systems such as:

The electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) signals, highly non-stationary in nature, greatly suffers from motion artifacts while recorded using wearable sensors. Since successful detection of various neurological and neuromuscular disorders is greatly dependent upon clean EEG and fNIRS signals, it is a matter of utmost importance to remove/reduce motion artifacts from EEG and fNIRS signals using reliable and robust methods. In this regard, this paper proposes two robust methods: (i) Wavelet packet decomposition (WPD) and (ii) WPD in combination with canonical correlation analysis (WPD-CCA), for motion artifact correction from single-channel EEG and fNIRS signals. The efficacy of these proposed techniques is tested using a benchmark dataset and the performance of the proposed methods is measured using two well-established performance matrices: (i) difference in the signal to noise ratio ( ) and (ii) percentage reduction in motion artifacts ( ). T...

Physiological signal measurement and processing are increasingly becoming popular in the ambulatory setting as the hospital-centric treatment is moving towards wearable and ubiquitous monitoring. Most of the physiological signals are highly susceptible to various types of noises, especially movement artifacts. The electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) signals are no exception to motion artifacts, which become prominent in the ambulatory setting. Since successful detection of various neurological disorders is greatly dependent upon clean EEG and fNIRS signals, it is a matter of utmost importance to remove motion artifacts from these two signal modalities using reliable and robust methods. This paper proposes three novel multiresolution analysis techniques: i) Variational mode decomposition (VMD), ii) VMD in combination with principal component analysis (VMD-PCA), and iii) VMD in combination with canonical correlation analysis (VMD-CCA), for motion artifact correction from single-channel EEG and fNIRS signals. The efficacy of these novel techniques is validated by computing the difference in the signal to noise ratio (SNR) and percentage reduction in motion artifacts (η). Among the three proposed novel methods, VMD-CCA decomposed with 15 intrinsic mode functions (IMFs) has shown the best denoising performance for EEG signals producing an average SNR and η values of 23.81 dB and 57.01%, respectively for all 23 EEG recordings. On the other hand, for the available 16 fNIRS recordings, VMD-CCA decomposed with 10 IMFs produced an average SNR and η values of 15.97 dB and 39.01%, respectively. The results reported using the proposed methods outperform most of the existing state-of-the-art techniques. INDEX TERMS Motion artifact, electroencephalogram (EEG), functional near-infrared spectroscopy (fNIRS), variational mode decomposition (VMD), principal component analysis (PCA), canonical correlation analysis (CCA).

Electroencephalography (EEG) is the most popular brain activity recording technique used in wide range of applications. One of the commonly faced problems in EEG recordings is the presence of artifacts that come from sources other than brain and contaminate the acquired signals significantly. Therefore, much research over the past 15 years has focused on identifying ways for handling such artifacts in the preprocessing stage. However, this is still an active area of research as no single existing artifact detection/removal method is complete or universal. This article presents an extensive review of the existing state-of-the-art artifact detection and removal methods from scalp EEG for all potential EEG-based applications and analyses the pros and cons of each method. First, a general overview of the different artifact types that are found in scalp EEG and their effect on particular applications are presented. In addition, the methods are compared based on their ability to remove ce...

Electroencephalography (EEG) is the most popular brain activity recording technique used in wide range of applications. One of the commonly faced problems in EEG recordings is the presence of artifacts that come from sources other than brain and contaminate the acquired signals significantly. Therefore, much research over the past 15 years has focused on identifying ways for handling such artifacts in the preprocessing stage. However, this is still an active area of research as no single existing artifact detection/removal method is complete or universal. This article presents an extensive review of the existing state-of-the-art artifact detection and removal methods from scalp EEG for all potential EEG-based applications and analyses the pros and cons of each method. First, a general overview of the different artifact types that are found in scalp EEG and their effect on particular applications are presented. In addition, the methods are compared based on their ability to remove ce...

The Electroencephalogram (EEG) recordings from the frontal lobe of the human brain help in analyzing several important brain functions like motor functions, problem-solving skills, etc. They are also used for the diagnosis of disorders associated with the frontal lobe. These recordings are often contaminated by ocular artifacts (OAs) like eye blinks, flutters and lateral eye movements (LEMs). Due to their high amplitude and large duration time, these artifacts corrupt a considerable segment of EEG. In this study, an enhanced version of signal decomposition scheme i.e. Variational Mode Decomposition (VMD) based algorithm is used for suppression of OAs. The signal decomposition is preceded by identification of ocular artifact corrupted segment using Multiscale modified sample entropy (mMSE). The band limited intrinsic mode functions (BLIMFs) are obtained using predefined K (number of required BLIMFs) and a (balancing parameter). These parameters help to detrend the EEG segment in yielding the low frequency and high amplitude BLIMFs related to OA efficiently. Upon identifying OA components from the BLIMFs and estimating OA, it is regressed with the contaminated EEG to obtain the clean EEG. The proposed VMD based algorithm provides an improved performance in comparison with the existing single channel algorithms based on Empirical mode decomposition (EMD) and Ensembled EMD (EEMD) and multi-channel algorithms like Independent component analysis (ICA) and wavelet enhanced ICA for artifact suppression and is also able to overcome their limitations. The significance of the algorithm are: (1) no additional reference EOG channel requirement, (2) OA artifact based thresholds for identification and estimation from the mode functions obtained using VMD, and (3) also address the flutter artifacts Q3 .

Electroencephalograms (EEGs) signal, obtained by recording the brain waves are used to analyse health problems related to neurology and clinical neurophysiology. This signal is often contaminated by a range of physiological and non-physiological artefacts, which leads to a misinterpretation in EEG signal analysis. Hence, artefact removal is one of the pre-processing step required for clinical usefulness of the EEG signal. One of the physiological artefact, i.e., electrocardiogram (ECG) contaminated EEG can affect the clinical analysis and diagnosis of brain health in various ways. This paper presents a review of engineering approaches adopted till date for ECG artefact identification and removal from contaminated EEG signal. In addition, the technical approach, computational extensiveness, input requirement and the results achieved with every method is discussed. Along with that, the feasibility study for real time implementation of the algorithms is discussed. Also, an analysis of these methods has been reported based on their performance.

The Electroencephalogram (EEG) recordings from the frontal lobe of the human brain help in analyzing several important brain functions like motor functions, problem-solving skills, etc. They are also used for the diagnosis of disorders associated with the frontal lobe. These recordings are often contaminated by ocular artifacts (OAs) like eye blinks, flutters and lateral eye movements (LEMs). Due to their high amplitude and large duration time, these artifacts corrupt a considerable segment of EEG. In this study, an enhanced version of signal decomposition scheme i.e. Variational Mode Decomposition (VMD) based algorithm is used for suppression of OAs. The signal decomposition is preceded by identification of ocular artifact corrupted segment using Multiscale modified sample entropy (mMSE). The band limited intrinsic mode functions (BLIMFs) are obtained using predefined K (number of required BLIMFs) and a (balancing parameter). These parameters help to detrend the EEG segment in yielding the low frequency and high amplitude BLIMFs related to OA efficiently. Upon identifying OA components from the BLIMFs and estimating OA, it is regressed with the contaminated EEG to obtain the clean EEG. The proposed VMD based algorithm provides an improved performance in comparison with the existing single channel algorithms based on Empirical mode decomposition (EMD) and Ensembled EMD (EEMD) and multi-channel algorithms like Independent component analysis (ICA) and wavelet enhanced ICA for artifact suppression and is also able to overcome their limitations. The significance of the algorithm are: (1) no additional reference EOG channel requirement, (2) OA artifact based thresholds for identification and estimation from the mode functions obtained using VMD, and (3) also address the flutter artifacts Q3 .

The paper proposes a multi-sensing system for the jointly assessment of electromyographic (EMG) and electroencephalographic (EEG) signals for the neuromuscular syndromes progression assessment, such as the Parkinson's disease (PD). The architecture implemented on Altera Cyclone V FPGA, interfaces 8 wireless surface EMGs (lower limbs) and 7 wireless EEGs (motor-cortex). The acquired signals, digitized and preprocessed, underwent a time-frequency features extraction (FE), making data suitable for a Serial Support Vector Machine (SSVM) real-time classification. The system has been in-vivo tested on 5 subjects (n=3 affected by mild PD and n=2 by severe PD). The experimental results showed an accuracy of ~94% in the pathology stage recognition, and near the 100% in distinguish healthy subjects from PD affected ones. The FPGA resources utilization results in: 31.04% ALMs, 15.87% registers, < 6% memory blocks and the 82.75% DSP blocks.

The aim of this series is to publish a Reference Library, including novel advances and developments in all aspects of Intelligent Systems in an easily accessible and well structured form. The series includes reference works, handbooks, compendia, textbooks, well-structured monographs, dictionaries, and encyclopedias. It contains well integrated knowledge and current information in the field of Intelligent Systems. The series covers the theory, applications, and design methods of Intelligent Systems. Virtually all disciplines such as engineering, computer science, avionics, business, e-commerce, environment, healthcare, physics and life science are included. The list of topics spans all the areas of modern intelligent systems such as:

Background and aim: Respiratory sounds, i.e. tracheal and lung sounds, have been of great interest due to their diagnostic values as well as the potential of their use in the estimation of the respiratory dynamics (mainly airflow). Thus the aim of the study is to present a new method to filter the heart sound interference from the tracheal sounds. Materials and methods: Tracheal sounds and airflow signals were collected by using an accelerometer from 10 healthy subjects. Tracheal sounds were then pre-processed by Recursive Least Square - Adaptive Noise Cancellation (RLS-ANC) filter to remove background noise. Gabor time-frequency expansion was used for both heart sound localization and reduction problem. Results: In the first step of filtering, RLS-ANC successfully filtered out the broad - band ambient noise. Reconstruction of tracheal sound was achieved from modified Gabor coefficients without heart sound noise. Conclusion: Visual inspection and quantitative analysis demonstrated t...

This paper presents an extensive review on understanding all types of EEG artifacts which are incorporated in EEG signals while taking measurements from scalp of the different subjects. Artifacts which are more prominent and occurred very often are 'Ocular artifacts'. The characteristics of an EEG signal having OA's are mentioned and explained in the paper. Ocular artifacts have a large impact on measurement. Reasons for inclusion of these artifacts and why the elimination is essential have been explained in this paper. Artifacts can be also named as noise; and noise has to be removed from the signal. For removing the OA's signal modeling is essential. Many Artifact correction approaches have been developed to provide reliable results of identification detection and removal. The different methods like ICA, WT, ANN etc and their characteristics which are explained by the researchers is the main review part of this paper which will give us an idea of building a more accurate system algorithm to remove ocular artifacts from the contaminated EEG signal. In This paper, the different methods and their result parameters along with their values have been given in the tabular form.

This paper introduces LIBS, a lightweight and inexpensive wearable sensing system, that can capture electrical activities of human brain, eyes, and facial muscles with two pairs of custom-built flexible electrodes each of which is embedded on an off-the-shelf foam earplug. A supervised nonnegative matrix factorization algorithm to adaptively analyze and extract these bioelectrical signals from a single mixed in-ear channel collected by the sensor is also proposed. While LIBS can enable a wide class of low-cost selfcare, human computer interaction, and health monitoring applications, we demonstrate its medical potential by developing an autonomous whole-night sleep staging system utilizing LIBS's outputs. We constructed a hardware prototype from off-the-shelf electronic components and used it to conduct 38 hours of sleep studies on 8 participants over a period of 30 days. Our evaluation results show that LIBS can monitor biosignals representing brain activities, eye movements, and muscle contractions with excellent fidelity such that it can be used for sleep stage classification with an average of more than 95% accuracy.

Diagnosing cardiac conditions require careful examination of an electrocardiogram (ECG). However, a significant issue arises when capturing an ECG due to interference from various noises. Noises like power line interference (PLI) and muscle artifact (MA) change the morphology, making it difficult to interpret the original signal. Our research proposes an improved variational mode extraction (IVME) technique using a Heap-based optimization (HBO) algorithm and an automatic wavelet interval-dependent thresholding (AWIT) method to eliminate such noises. First, HBO uses the envelope entropy spectrum (EES) as the objective function to find the best fitness value for optimizing the VME parameter, known as penalty factor α. Then, we extract a specific mode using the optimal α value in VME to accurately remove PLI from the signal. Finally, the AWIT method automatically computes the intervals and their respective threshold values to remove excessive MA noise from the PLI-filtered ECG signal. We evaluate the efficiency of ten random real-time ECG signals from the MIT-BIH arrhythmia database. The result analysis proves that our algorithm can accurately extract the mode containing PLI and eradicate MA from the noisy ECG signal. It also shows improvement in signal parameters like signal-to-noise ratio (SNR improvement), mean square error (MSE), and correlation coefficients (CC) with 36.7968 dB, 0.00030901, and 99.7278%, respectively. INDEX TERMS Automatic wavelet interval-dependent thresholding, electrocardiogram signal, envelope entropy spectrum, heap-based optimization algorithm, muscle artifact, power line interference, variational mode extraction.

Electrical Impedance Epigastrography (EIE) is a non-invasive method that allows the assessment of gastric emptying rates without using ionizing radiation. This method works by applying an alternating current with a frequency of 32 kHz, which can be varied by the operator from 1 to 4 mA, through electrodes over the epigastric region, and measuring the potential difference between them. The post acquisition analysis relies on the Short-Time Fourier Transform (STFT) to extract the gastric motility component of the signal (centre frequency of 0.05 Hz with a bandwidth of 0.02 Hz). Motion artifacts that occur during the test have been found to produce positive and negative spikes. Software was produced to model the EIE signals and apply simulated motion artifacts of known amplitude at specific points in the signal. The signals were labelled pseudo EIE signals. Due to the large frequency range of a delta function, the spikes produced false positives in the frequency domain, suggesting the presence of gastric contractility when there was none, or exaggerating the magnitude of real events in both real and pseudo EIE signals. A motion artifact rejection algorithm (MARA) was developed for removing the artifacts in the time domain, before processing by the STFT. The efficacy of the algorithm was tested on the pseudo EIE signals by comparing the power density calculated by the STFT before and after the simulation of artifacts and after the application of the algorithm.

Electroencephalogram (EEG) recordings are often contaminated with muscle artifacts. These artifacts obscure the EEG and complicate its interpretation or even make the interpretation unfeasible. This paper focuses on the particular context of extraction of low-voltage rapid ictal discharges from ictal scalp-EEG activity cantaminated by muscle artifact. In this context our aim was to evaluate the ability of Independent Component Analysis (ICA) and Canonical Correlation Analysis (CCA), to remove muscle artefacts from surface EEG signals. The efficiency of ICA and CCA to correct the muscular artifact was evaluated both on simulated data and on real data recorded in an epileptic patient. The obtained results show that some ICA methods and CCA removed successfully the muscle artifact without altering the recorded underlying ictal activity.

Photoplethysmography (PPG) has recently become a popular method for heart rate estimation due to its simple acquisition technique. However, the main challenge in determining the heart rate from the PPG signals is its high vulnerability to motion artifacts (MA). In this paper, a new scheme is proposed for heart rate estimation through frame selective multistage adaptive noise cancellation (MANC). The frame selective approach determines the specific frames of PPG signal which are significantly interfered with MA, and the MA removal operation is only employed over those specific frames. The MANC scheme is implemented through the Least Mean Square (LMS) algorithm in which instead of the conventional approach of using accelerometer data directly, we propose to utilize mode-based decomposed 3-channel accelerometer data as reference signals independently in a sequential manner. The use of decomposed modes offers high degrees of controllability in the ANC scheme depending on the overlap between the spectra corresponding to MA and heart rate, thereby offers effective denoising. A peak searching algorithm is employed to estimate heart rate-related peaks from the resulting noise-reduced PPG signal. The novelty of the proposed scheme lies in the use of decomposed reference inputs to the MANC algorithm (named as DERMANC scheme) which is accomplished through both empirical mode decomposition (EMD) and variational mode decomposition (VMD). Performance of the proposed EMD and VMD based schemes (E-DERMANC and V-DERMANC) has been tested on a publicly available dataset and very satisfactory results are obtained in terms of estimation accuracy and computational time (0.95 and 1.10 BPM, respectively on 12 recordings) that makes the schemes worthy to be implemented in wearable devices. INDEX TERMS Acceleration data, adaptive noise cancellation (ANC), empirical mode decomposition (EMD), heart rate, motion artifacts, photoplethysmography (PPG), variational mode decomposition (VMD).

An electroencephalogram (EEG) is a medical examination that records the electrical activity of the human brain. In order to record these signals, electrodes are placed on the scalp, and these electrodes detect any activity of the brain cells. This result is used to diagnose patients with epilepsy, sleep disorders, brain tumors, seizure disorders, and brain disorders. However, these signals are often corrupted by various artefacts either generated from the human body(physiological) or the electrical apparatus used(non-physiological). The removal of these artefacts is vital as their presence may result in the wrong diagnosis of a disease. One of these artefacts is generated because of various kinds of eye movements, i.e. EOG. In this paper, we will be focusing on how ocular artefacts occur and how they affect the EEG signal. Later we will look into different types of artefact removal techniques and how they perform differently from each other on the basis of different factors such as signal-to-noise-ratio etc. The aim of this study is to get a detailed understanding of various ocular artefact removal techniques, challenges related to them and finding an efficient way to obtain an uncorrupted EEG signal.

Photoplethysmography (PPG) has recently become a popular method for heart rate estimation due to its simple acquisition technique. However, the main challenge in determining the heart rate from the PPG signals is its high vulnerability to motion artifacts (MA). In this paper, a new scheme is proposed for heart rate estimation through frame selective multistage adaptive noise cancellation (MANC). The frame selective approach determines the specific frames of PPG signal which are significantly interfered with MA, and the MA removal operation is only employed over those specific frames. The MANC scheme is implemented through the Least Mean Square (LMS) algorithm in which instead of the conventional approach of using accelerometer data directly, we propose to utilize mode-based decomposed 3-channel accelerometer data as reference signals independently in a sequential manner. The use of decomposed modes offers high degrees of controllability in the ANC scheme depending on the overlap between the spectra corresponding to MA and heart rate, thereby offers effective denoising. A peak searching algorithm is employed to estimate heart rate-related peaks from the resulting noise-reduced PPG signal. The novelty of the proposed scheme lies in the use of decomposed reference inputs to the MANC algorithm (named as DERMANC scheme) which is accomplished through both empirical mode decomposition (EMD) and variational mode decomposition (VMD). Performance of the proposed EMD and VMD based schemes (E-DERMANC and V-DERMANC) has been tested on a publicly available dataset and very satisfactory results are obtained in terms of estimation accuracy and computational time (0.95 and 1.10 BPM, respectively on 12 recordings) that makes the schemes worthy to be implemented in wearable devices. INDEX TERMS Acceleration data, adaptive noise cancellation (ANC), empirical mode decomposition (EMD), heart rate, motion artifacts, photoplethysmography (PPG), variational mode decomposition (VMD).

This article presents a study on ECG signal filtering algorithms to denoise signals corrupted by various types of noise sources. The study also examines the effect of Kronecker tensor product values on ECG rates. The study is conducted in a Matlab environment, and the results demonstrate that a constant number for the respective codes can effectively denoise ECG signals without any trouble. These findings have significant implications for diagnosing abnormal heart rhythms and investigating chest pains. The present study is novel in that it explores the relationship between ECG rate and Kronecker delta values across different age groups, which has not been extensively studied in previous literature. The study&#39;s unique contribution is the determination of age-specific values of the constant K required to represent this relationship accurately in different populations, which could inform the development of more effective algorithms for denoising ECG signals in clinical settings. Ad...

ObjectiveCardiovascular diseases (CVDs) account for a high fatality rate worldwide. Heart murmurs can be detected from phonocardiograms (PCGs) and may indicate CVDs. Still they are often overlooked as their detection and correct clinical interpretation requires expert skills. In this work, we aim to predict the presence of murmurs and clinical outcome from multiple PCG recordings employing an explainable multitask model.ApproachOur approach consists of a two-stage multitask model. In the first stage, we predict the murmur presence in single PCGs using a multiple instance learning (MIL) framework. MIL also allows us to derive sample-wise classifications (i.e. murmur locations) while only needing one annotation per recording (“weak label”) during training. In the second stage, we fuse explainable hand-crafted features with features from a pooling-based artificial neural network (PANN) derived from the MIL framework. Finally, we predict the presence of murmurs as well as the clinical o...

Denoising of electrooculography (EOG) signals is a challenging task as the noise and signal share the same frequency band. This paper proposes a two-stage framework for denoising EOG signals. The first stage approach is based on preserving the nature of eye movements while the second stage is based on the nature of noise (Gaussian or not). In the first stage, denoising is carried out using one out of four filtering methods, each filter being optimal for a particular EOG pattern. The four methods used in the first stage are linear bandpass filtering, stationary wavelet transform (SWT), empirical mode decomposition (EMD) and median filtering. The Stage I framework selects the output that provides the highest estimated signal to noise ratio (SNR). In case, the Stage I filtering does not provide a significant SNR, the system uses Stage II filtering. In the second stage, we use two recursive state estimators, i.e. a Kalman filter and a particle filter for further denoising. The two-stage method is found to provide a better SNR as compared to a single stage method.

In this paper some efficient and low computation complex signal conditioning algorithms are proposed in distant health tracking applications, for improvement of the electroencephalogram (EEG) signal. Few artifacts are contaminated also mask small characteristics underlying EEG signal activity in medical environments during extraction of EEG signal. Low computational difficulty filters are appealing especially within distant healthcare surveillance. Therefore, we provided several effective and less computing adaptive noise cancellers (ANCs) in this work to improve EEG signal. Most of these techniques use easy addition as well as shift calculations also attain significant convergence performance compared to other standard techniques. Real EEG signals collected using emotional EEG systems are verified for proposed implementations. Using several performances measures our studies demonstrate that techniques suggested provides best performance than prevailing methods. This methodology is ...

Eye movements, eye blinks, cardiac signals, muscle noise, and line noise present serious problems for electroenceph-alographic~EEG! interpretation and analysis when rejecting contaminated EEG segments results in an unacceptable data loss. Many methods have been proposed to remove artifacts from EEG recordings, especially those arising from eye movements and blinks. Often regression in the time or frequency domain is performed on parallel EEG and electrooculographic~EOG! recordings to derive parameters characterizing the appearance and spread of EOG artifacts in the EEG channels. Because EEG and ocular activity mix bidirectionally, regressing out eye artifacts inevitably involves subtracting relevant EEG signals from each record as well. Regression methods become even more problematic when a good regressing channel is not available for each artifact source, as in the case of muscle artifacts. Use of principal component analysis~PCA! has been proposed to remove eye artifacts from multichannel EEG. However, PCA cannot completely separate eye artifacts from brain signals, especially when they have comparable amplitudes. Here, we propose a new and generally applicable method for removing a wide variety of artifacts from EEG records based on blind source separation by independent component analysis~ICA!. Our results on EEG data collected from normal and autistic subjects show that ICA can effectively detect, separate, and remove contamination from a wide variety of artifactual sources in EEG records with results comparing favorably with those obtained using regression and PCA methods. ICA can also be used to analyze blink-related brain activity. Descriptors: Independent component analysis, ICA, EEG, Artifact removal, EOG Eye movements, eye blinks, muscle noise, heart signals, and line noise often produce large and distracting artifacts in electroenceph-alographic~EEG! recordings. Asking subjects to fixate a visual target may reduce voluntary eye movements~blinks and saccades! in cooperative subjects during brief EEG sessions, but fixation does not eliminate involuntary eye movements and cannot be used when the subject is performing a task requiring eye movements. Rejecting EEG segments with artifacts larger than an arbitrarily preset value is the most commonly used method for dealing with artifacts in research settings. However, when limited data are available, or blinks and muscle movements occur too frequently, as in some patient groups, the amount of data lost to artifact rejection may be unacceptable. Several proposed methods for removing eye-movement artifacts are based on regression in the time domain~Gratton, Coles,

Discovering the information about several disorders prevailing in brain and neurology is by no means a new scientific technique. A neurological disorder of any human being can be analyzed using EEG (Electroencephalography) signal from the electrode's output. Epilepsy (spontaneous recurrent seizure) detection is usually carried out by the physicians using a visual scanning of the signals produced by EEG, which is onerous and may be inaccurate. EEG signal is often used to determine epilepsy, for its merits, such as non-invasive, portable, and economical, can exhibit superior temporal tenacity. This paper surveys the existing artifact removal methods. It puts a new-fangled mode forward to confiscate artifacts and hauls informative derived values from EEG to automate Epilepsy detection. The automated Epilepsy detection has to precisely indicate and detect the neural abnormality of the brain. This indication and detection process necessitates a proficient approach for the prompt removal of artifacts of the EEG signals. An effective artifact removal of EEG signals can alone enable the useful features of the original signals for further processing. Once the original signals excluding the noise is obtained, a delicate strategy for extracting the features of the signals, becomes mandatory in order to accomplish robust classification of the signal. Then an expert classification technique is implemented to aid the automated analysis process to correctly distinguish the EEG signal features.

Heart disease is one of the major problems that needs to be addressed using the latest methods of signal processing. Different measuring parameters are used to identify heart disease. Electrocardiogram (ECG) plays an important role in diagnosis of heart disease. Practically it is not possible to acquire ECG signal without noise. For analysis and interpretation of ECG signal, it is very important to diagnose this signal. In this paper, different kinds of noise present in the ECG signal are illustrated, and several techniques for their removal are discussed. Index Terms-Artifact, baseline wander, high frequency noise.

Ballistocardiogram (BCG) artifact remains a major challenge that renders electroencephalographic (EEG) signals hard to interpret in simultaneous EEG and functional MRI (fMRI) data acquisition. Here, we propose an integrated learning and inference approach that takes advantage of a commercial high-density EEG cap, to estimate the BCG contribution in noisy EEG recordings from inside the MR scanner. To estimate reliably the full-scalp BCG artifacts, a near-optimal subset (20 out of 256) of channels first was identified using a modified recording setup. In subsequent recordings inside the MR scanner, BCG-only signal from this subset of channels was used to generate continuous estimates of the full-scalp BCG artifacts via inference, from which the intended EEG signal was recovered. The reconstruction of the EEG was performed with both a direct subtraction and an optimization scheme. We evaluated the performance on both synthetic and real contaminated recordings, and compared it to the be...

As the technique of electroencephalogram (EEG) developed for such many years, its application spreads and permeates into different areas, such like, clinical diagnosis, brain-computer interface, mental state estimation, and so on. Recently, using EEG as a tool for estimate people’s mental state and its extensional applications have jump into the limelight. These practical applications are urgently needed because the lack of subjectively estimating methods for the so called metal states, such as the concentration during study, the cognitive workload in driving, the calmness under mental training and so on. On the other hand, the application of EEG signals under daily life conditions especially when eye movements are totally without any constrains under a ‘fully free-view’ condition are obedient to the traditional ocular artifact suppression methods and how it meets the neuroscience standard has not been clearly expounded. This cause the ambiguities of explaining the obtain data and l...

Inaccurate estimation of average dielectric properties can have a tangible impact on microwave radar-based breast images. Despite this, recent patient imaging studies have used a fixed estimate although this is known to vary from patient to patient. Parameter search algorithms are a promising technique for estimating the average dielectric properties from the reconstructed microwave images themselves without additional hardware. In this work, qualities of accurately reconstructed images are identified from point spread functions. As the qualities of accurately reconstructed microwave images are similar to the qualities of focused microscopic and photographic images, this work proposes the use of focal quality metrics for average dielectric property estimation. The robustness of the parameter search is evaluated using experimental dielectrically heterogeneous phantoms on the three-dimensional volumetric image. Based on a very broad initial estimate of the average dielectric propertie...

This article presents a study on ECG signal filtering algorithms to denoise signals corrupted by various types of noise sources. The study also examines the effect of Kronecker tensor product values on ECG rates. The study is conducted in a Matlab environment, and the results demonstrate that a constant number for the respective codes can effectively denoise ECG signals without any trouble. These findings have significant implications for diagnosing abnormal heart rhythms and investigating chest pains. The present study is novel in that it explores the relationship between ECG rate and Kronecker delta values across different age groups, which has not been extensively studied in previous literature. The study's unique contribution is the determination of age-specific values of the constant K required to represent this relationship accurately in different populations, which could inform the development of more effective algorithms for denoising ECG signals in clinical settings. Additionally, this study's finding of an inverse relationship between ECG rate and Kronecker delta values could have broader implications for understanding the physiological factors that contribute to variability in ECG measurements. The study provides valuable insights into ECG signal processing and suggests that the implemented techniques can improve the accuracy of ECG signal analysis in real-time clinical settings. Overall, the manuscript is a valuable contribution to the field of biomedical signal processing and provides important information for researchers and healthcare professionals.

This paper describes some of the basic principles and motivations underlying our brain-computer interface design. Our intent is to abstractly describe multi-rate filtering and orthogonal subspace decomposition appropriate for processing electroencephalographic data and identify some of the constraints imposed on the interface when considering a user with amyotrophic lateral sclerosis . Using an additive Gaussian noise model, orthogonal filter bank decomposition using a custom basis vector is demonstrated as an effective means for identification of events in an electroencephalogram. INTRODUCTION The University of Victoria Assistive Technology Team, (UVATT) is developing a non-invasive brain-computer interface, (BCI) to provide persons with a means to communicate when all other conventional methods have failed. More broadly, the technology under development may also be used to detect and characterize electrical parameters within the human brain associated with specific behavioural fun...

This article presents a study on ECG signal filtering algorithms to denoise signals corrupted by various types of noise sources. The study also examines the effect of Kronecker tensor product values on ECG rates. The study is conducted in a Matlab environment, and the results demonstrate that a constant number for the respective codes can effectively denoise ECG signals without any trouble. These findings have significant implications for diagnosing abnormal heart rhythms and investigating chest pains. The present study is novel in that it explores the relationship between ECG rate and Kronecker delta values across different age groups, which has not been extensively studied in previous literature. The study&#39;s unique contribution is the determination of age-specific values of the constant K required to represent this relationship accurately in different populations, which could inform the development of more effective algorithms for denoising ECG signals in clinical settings. Ad...

Physiological signal measurement and processing are increasingly becoming popular in the ambulatory setting as the hospital-centric treatment is moving towards wearable and ubiquitous monitoring. Most of the physiological signals are highly susceptible to various types of noises, especially movement artifacts. The electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) signals are no exception to motion artifacts, which become prominent in the ambulatory setting. Since successful detection of various neurological disorders is greatly dependent upon clean EEG and fNIRS signals, it is a matter of utmost importance to remove motion artifacts from these two signal modalities using reliable and robust methods. This paper proposes three novel multiresolution analysis techniques: i) Variational mode decomposition (VMD), ii) VMD in combination with principal component analysis (VMD-PCA), and iii) VMD in combination with canonical correlation analysis (VMD-CCA), for motion artifact correction from single-channel EEG and fNIRS signals. The efficacy of these novel techniques is validated by computing the difference in the signal to noise ratio (SNR) and percentage reduction in motion artifacts (η). Among the three proposed novel methods, VMD-CCA decomposed with 15 intrinsic mode functions (IMFs) has shown the best denoising performance for EEG signals producing an average SNR and η values of 23.81 dB and 57.01%, respectively for all 23 EEG recordings. On the other hand, for the available 16 fNIRS recordings, VMD-CCA decomposed with 10 IMFs produced an average SNR and η values of 15.97 dB and 39.01%, respectively. The results reported using the proposed methods outperform most of the existing state-of-the-art techniques. INDEX TERMS Motion artifact, electroencephalogram (EEG), functional near-infrared spectroscopy (fNIRS), variational mode decomposition (VMD), principal component analysis (PCA), canonical correlation analysis (CCA).

Motion artefacts represent a severe problem in Electrocardiogram (ECG) monitoring using portable devices, as they may overlap the characteristic ECG waveforms. In this study, an adaptive filtering technique for artefacts removal is investigated. The adopted approach exploits motion-related information coming from an accelerometer attached on the ECG electrode. The experimental results indicate that adaptive filtering technique removes a large amount of motion artefacts with a decreased number of beat detection errors, making our approach a useful pre-processing scheme for ECG analysis. The developed system, implemented on a commercial microcontroller, verified real-time requirements, hence resulting suitably for portable monitoring devices.

Eye movements, eye blinks, cardiac signals, muscle noise, and line noise present serious problems for electroenceph-alographic~EEG! interpretation and analysis when rejecting contaminated EEG segments results in an unacceptable data loss. Many methods have been proposed to remove artifacts from EEG recordings, especially those arising from eye movements and blinks. Often regression in the time or frequency domain is performed on parallel EEG and electrooculographic~EOG! recordings to derive parameters characterizing the appearance and spread of EOG artifacts in the EEG channels. Because EEG and ocular activity mix bidirectionally, regressing out eye artifacts inevitably involves subtracting relevant EEG signals from each record as well. Regression methods become even more problematic when a good regressing channel is not available for each artifact source, as in the case of muscle artifacts. Use of principal component analysis~PCA! has been proposed to remove eye artifacts from multichannel EEG. However, PCA cannot completely separate eye artifacts from brain signals, especially when they have comparable amplitudes. Here, we propose a new and generally applicable method for removing a wide variety of artifacts from EEG records based on blind source separation by independent component analysis~ICA!. Our results on EEG data collected from normal and autistic subjects show that ICA can effectively detect, separate, and remove contamination from a wide variety of artifactual sources in EEG records with results comparing favorably with those obtained using regression and PCA methods. ICA can also be used to analyze blink-related brain activity. Descriptors: Independent component analysis, ICA, EEG, Artifact removal, EOG Eye movements, eye blinks, muscle noise, heart signals, and line noise often produce large and distracting artifacts in electroenceph-alographic~EEG! recordings. Asking subjects to fixate a visual target may reduce voluntary eye movements~blinks and saccades! in cooperative subjects during brief EEG sessions, but fixation does not eliminate involuntary eye movements and cannot be used when the subject is performing a task requiring eye movements. Rejecting EEG segments with artifacts larger than an arbitrarily preset value is the most commonly used method for dealing with artifacts in research settings. However, when limited data are available, or blinks and muscle movements occur too frequently, as in some patient groups, the amount of data lost to artifact rejection may be unacceptable. Several proposed methods for removing eye-movement artifacts are based on regression in the time domain~Gratton, Coles,

The Doppler signal of mitral valve is a biomedical signals and it is acquired by Doppler ultrasound device from mitral valve of hearth. It contains useful information about mitral valve and it can be used to diagnose mitral valve diseases by doctors. However, biomedical signal might be contaminated with noise or disruptive interference. In literature, different approaches have been introduced to remove the noise or interference from biomedical signal, but adaptive filtering is most popular technique for noise filtering. Therefore, a novel adaptive noise cancellation techniques based FIR digital filter was proposed using artificial bee colony algorithm (ABC) and it is employed for the noise reduction on noisy mitral valve Doppler signal. So, the meaningful data about mitral valve was saved to diagnose mitral valve diseases using denoising approach. Then, this novel approach was compared with the other methods suggested in literature such as recursive least square (RLS) algorithm or discrete wavelet transform (DWT). Also, to demonstrate the efficiency, different digital filter order and noise cases were used suggested in literature.

Eye movements, eye blinks, cardiac signals, muscle noise, and line noise present serious problems for electroenceph-alographic~EEG! interpretation and analysis when rejecting contaminated EEG segments results in an unacceptable data loss. Many methods have been proposed to remove artifacts from EEG recordings, especially those arising from eye movements and blinks. Often regression in the time or frequency domain is performed on parallel EEG and electrooculographic~EOG! recordings to derive parameters characterizing the appearance and spread of EOG artifacts in the EEG channels. Because EEG and ocular activity mix bidirectionally, regressing out eye artifacts inevitably involves subtracting relevant EEG signals from each record as well. Regression methods become even more problematic when a good regressing channel is not available for each artifact source, as in the case of muscle artifacts. Use of principal component analysis~PCA! has been proposed to remove eye artifacts from multichannel EEG. However, PCA cannot completely separate eye artifacts from brain signals, especially when they have comparable amplitudes. Here, we propose a new and generally applicable method for removing a wide variety of artifacts from EEG records based on blind source separation by independent component analysis~ICA!. Our results on EEG data collected from normal and autistic subjects show that ICA can effectively detect, separate, and remove contamination from a wide variety of artifactual sources in EEG records with results comparing favorably with those obtained using regression and PCA methods. ICA can also be used to analyze blink-related brain activity. Descriptors: Independent component analysis, ICA, EEG, Artifact removal, EOG Eye movements, eye blinks, muscle noise, heart signals, and line noise often produce large and distracting artifacts in electroenceph-alographic~EEG! recordings. Asking subjects to fixate a visual target may reduce voluntary eye movements~blinks and saccades! in cooperative subjects during brief EEG sessions, but fixation does not eliminate involuntary eye movements and cannot be used when the subject is performing a task requiring eye movements. Rejecting EEG segments with artifacts larger than an arbitrarily preset value is the most commonly used method for dealing with artifacts in research settings. However, when limited data are available, or blinks and muscle movements occur too frequently, as in some patient groups, the amount of data lost to artifact rejection may be unacceptable. Several proposed methods for removing eye-movement artifacts are based on regression in the time domain~Gratton, Coles,

The assessment of a method for removing artifacts from electroencephalography (EEG) datasets often disregard verifying that global brain dynamics is preserved. In this study, we verified that the recently introduced optimized fingerprint method and the automatic removal of cardiac interference (ARCI) approach not only remove physiological artifacts from EEG recordings but also preserve global brain dynamics, as assessed with a new approach based on microstate analysis. We recorded EEG activity with a high-resolution EEG system during two resting-state conditions (eyes open, 25 volunteers, and eyes closed, 26 volunteers) known to exhibit different brain dynamics. After signal decomposition by independent component analysis (ICA), the independent components (ICs) related to eyeblinks, eye movements, myogenic interference, and cardiac electromechanical activity were identified with the optimized fingerprint method and ARCI approach and statistically compared with the outcome of the exp...

Eye movements, eye blinks, cardiac signals, muscle noise, and line noise present serious problems for electroenceph-alographic~EEG! interpretation and analysis when rejecting contaminated EEG segments results in an unacceptable data loss. Many methods have been proposed to remove artifacts from EEG recordings, especially those arising from eye movements and blinks. Often regression in the time or frequency domain is performed on parallel EEG and electrooculographic~EOG! recordings to derive parameters characterizing the appearance and spread of EOG artifacts in the EEG channels. Because EEG and ocular activity mix bidirectionally, regressing out eye artifacts inevitably involves subtracting relevant EEG signals from each record as well. Regression methods become even more problematic when a good regressing channel is not available for each artifact source, as in the case of muscle artifacts. Use of principal component analysis~PCA! has been proposed to remove eye artifacts from multichannel EEG. However, PCA cannot completely separate eye artifacts from brain signals, especially when they have comparable amplitudes. Here, we propose a new and generally applicable method for removing a wide variety of artifacts from EEG records based on blind source separation by independent component analysis~ICA!. Our results on EEG data collected from normal and autistic subjects show that ICA can effectively detect, separate, and remove contamination from a wide variety of artifactual sources in EEG records with results comparing favorably with those obtained using regression and PCA methods. ICA can also be used to analyze blink-related brain activity. Descriptors: Independent component analysis, ICA, EEG, Artifact removal, EOG Eye movements, eye blinks, muscle noise, heart signals, and line noise often produce large and distracting artifacts in electroenceph-alographic~EEG! recordings. Asking subjects to fixate a visual target may reduce voluntary eye movements~blinks and saccades! in cooperative subjects during brief EEG sessions, but fixation does not eliminate involuntary eye movements and cannot be used when the subject is performing a task requiring eye movements. Rejecting EEG segments with artifacts larger than an arbitrarily preset value is the most commonly used method for dealing with artifacts in research settings. However, when limited data are available, or blinks and muscle movements occur too frequently, as in some patient groups, the amount of data lost to artifact rejection may be unacceptable. Several proposed methods for removing eye-movement artifacts are based on regression in the time domain~Gratton, Coles,

Electrocardiogram (ECG) signal is the electrical recording of coronary heart activity. It is a common routine and vital cardiac diagnostic tool in which in electric signals are measured and recorded to recognize the practical status of heart, but ECG signal can be distorted with noise as, numerous artifacts corrupt the unique ECG signal and decreases it quality. Consequently, there may be a need to eliminate such artifacts from the authentic signal and enhance its quality for better interpretation. ECG signals are very low frequency signals of approximately 0.5Hz-100Hz and digital filters are used as efficient approach for noise removal of such low frequency signals. Noise may be any interference because of movement artifacts or due to power device that are present wherein ECG has been taken. Consequently, ECG signal processing has emerged as a common and effective tool for research and clinical practices. This paper gives the comparative evaluation of FIR and IIR filters and their ...