An objective distortion measure is very crucial to accurately quantify the distortion introduced in the electroencephalogram (EEG) signal during the denoising process. Most of the existing algorithms report their denoising performance by comparing the original EEG signal and the reconstructed EEG signal using root mean square error (RMSE) and other similar measures. However, it is very important to quantify the distortion in each band of EEG signal since each band provides distinct information about the specific brain activity. Furthermore, quantification of band-wise distortion enables the selection of particular denoising algorithm for the application-specific EEG signal analysis. Therefore, in this paper, we propose two robust distortion measures such as weighted signal to noise ratio (WSNR) and weighted correlation coefficient (WCC) for accurately representing the objective reconstruction loss in each band. These performance measures are computed between the wavelet subbands of the original and the denoised/reconstructed signal with weights equal to the relative wavelet energy and wavelet entropy of the corresponding subband wavelet coefficients. To demonstrate the effectiveness of the proposed performance measures, we evaluate the performance of six existing denoising methods using these measures. Results depict that these measures can adequately provide high mutual agreement between objective scores and subjective analysis. © 1994-2012 IEEE.

Madhur Deo Upadhayay

Electrical Engineering

(SoE) School of Engineering

Saini M.

Satija U.

<p>Shiv Nadar University is a comprehensive, multidisciplinary, research-focused, and student-centric University offering a full range of academic programs at the undergraduate, postgraduate and doctoral level. With state-of-the-art infrastructure, the University comprises of academic wings, sophisticated labs, international standard sports facilities, amphitheaters, auditorium, conference rooms and smart classrooms. The University&rsquo;s goal is to become internationally recognized for the quality of its research and creative endeavors and their applicability to improving quality of life, generating new insights and expanding the boundaries of human knowledge creativity. Committed to excellence in teaching, research and service, the University aims to serve the higher education needs of India and the world beyond.</p>

Shiv Nadar University

Wavelet Based Waveform Distortion Measures for Assessment of Denoised EEG Quality With Reference to Noise-Free EEG Signal

IEEE Signal Processing Letters

This Letter proposes an automated method for the detection and suppression of muscle artefacts (MAs) in the single-channel electroencephalogram (EEG) signal based on variational mode decomposition (VMD) and zero crossings count threshold criterion without the use of reference electromyogram (EMG). The proposed method involves three major steps: decomposition of the input EEG signal into two modes using VMD; detection of MAs based on zero crossings count thresholding in the second mode; retention of the first mode as MAs-free EEG signal only after detection of MAs in the second mode. The authors evaluate the robustness of the proposed method on a variety of EEG and EMG signals taken from publicly available databases, including Mendeley database, epileptic Bonn database and EEG during mental arithmetic tasks database (EEGMAT). Evaluation results using different objective performance metrics depict the superiority of the proposed method as compared to existing methods while preserving the clinical features of the reconstructed EEG signal. © 2020 Institution of Engineering and Technology. All rights reserved.

Fulltext

Healthcare Technology Letters

Effective automated method for detection and suppression of muscle artefacts from single-channel EEG signal

Removal of ocular artifacts (OAs) from electroencephalogram (EEG) signal is crucial for accurate and effective EEG analysis and brain-computer interface research. The elimination of OAs is quite challenging in absence of reference electro-oculogram and in single-channel EEG signal using existing independent component analysis based OA removal techniques. Though few of the recent OAs removal techniques suppress the OAs in the single-channel significantly, these techniques introduce distortion in clinical features of the EEG signal during artifact removal process. To address these issues, in this paper, we propose a robust framework for the detection and removal of OAs based on variational mode decomposition (VMD) and turning point count. The proposed framework exploits the effectiveness of VMD in two stages denoted as VMD-I and VMD-II respectively. The proposed framework has four components: EEG signal decomposition into two modes using VMD-I; rejection of low-frequency baseline components; processed EEG signal decomposition into three modes using VMD-II; rejection of mode containing OAs based on turning point count based threshold criteria. We evaluate the effectiveness of the proposed framework using the EEG signals in presence of various ocular artifacts with different amplitudes and shapes taken from three standard databases including, Mendeley database, MIT-BIH Polysmnographic database and EEG during mental arithmetic tasks database. Evaluation results demonstrate that proposed framework eliminates OAs with minimal loss in valuable clinical features in both reconstructed EEG signal and in all local rhythms. Furthermore, subjective and objective comparative analysis demonstrate that our framework outperforms few existing OAs removal techniques in removing OAs from single-channel EEG signal. © 2001-2012 IEEE.

IEEE Sensors Journal

An Effective and Robust Framework for Ocular Artifact Removal from Single-Channel EEG Signal Based on Variational Mode Decomposition

In this paper, the authors present an automated method for quality assessment of electrocardiogram (ECG) signal. Our proposed method not only detects and classifies the ECG noises but also localizes the ECG noises which can play a crucial role in extracting reliable clinical features for ECG analysis systems. The proposed method is based on three stages: Wavelet decomposition of ECG signal into sub-bands; simultaneous ECG signal and noise reconstruction; extraction of temporal features such as maximum absolute amplitude, zerocrossings, kurtosis and autocorrelation function for detection, localization and classification of ECG noises including flat line (FL), time-varying noise or pause (TVN), baseline wander (BW), abrupt change (AB), power line interference (PLI), muscle artifacts (MA) and additive white Gaussian noise (AWGN). The proposed method is tested and validated against manually annotated ECG signals corrupted with aforementioned noises taken from MIT-BIH arrhythmia database, Physionet challenge database, and real-time recorded ECG signals. Comparative detection and classification results depict the superior performance of the proposed method over state of art methods. Detection results show that our method can achieve an average sensitivity (Se), average specificity (Sp) and accuracy (A) of 99.61\%, 98.51\%, 99.49\% respectively. Also, the method achieves a Se of 98.18\%, and Sp of 94.97\% for real-time recorded ECG signals. The method has an average timing error of 0.14 s in localizing the noise segments. Further, classification results demonstrate that the proposed method achieves an average sensitivity (Se), average positive predictivity (PP) and classification accuracy (Ac) of 98.53\%, 98.89\%, 97.50\% respectively. © 2017 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences

Biocybernetics and Biomedical Engineering

An automated ECG signal quality assessment method for unsupervised diagnostic systems

IEEE Access

Identification and Removal of Physiological Artifacts From Electroencephalogram Signals: A Review

Journal of Medical Imaging and Health Informatics

Electroencephalogram Artifact Removal-Validation

Wavelet Based Waveform Distortion Measures for Assessment of Denoised EEG Quality with Reference to Noise-Free EEG Signal