Powerline interference reduction in ECG signals using empirical wavelet transform and adaptive filtering

Singh, Omkar; Sunkaria, Ramesh Kumar

doi:10.3109/03091902.2014.979954

Cited by 22 publications

(8 citation statements)

References 14 publications

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“…Researchers rarely justify the selection of the mother wavelet and those who do justify the selection based on superficial similarities with the signal (Rafiee et al, 2009;Shalchyan et al, 2012). The wavelet packet transform (WPT) (Jiang et al, 2007;Jiang and Adeli, 2004;Maheshwari et al, 2016;Singh and Sunkaria, 2015) provides an improvement over WT but it also has limited adaptability in the way it divides frequency bands in the frequency domain. Compared to WT and WPT, EWT can adapt both its basis and frequency bands to model the analyzed signal properly.…”

Section: Methodsmentioning

confidence: 99%

MUSIC-Expected maximization gaussian mixture methodology for clustering and detection of task-related neuronal firing rates

Ortiz-Rosario

Adeli

Buford

2017

Behavioural Brain Research

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Researchers often rely on simple methods to identify involvement of neurons in a particular motor task. The historical approach has been to inspect large groups of neurons and subjectively separate neurons into groups based on the expertise of the investigator. In cases where neuron populations are small it is reasonable to inspect these neuronal recordings and their firing rates carefully to avoid data omissions. In this paper, a new methodology is presented for automatic objective classification of neurons recorded in association with behavioral tasks into groups. By identifying characteristics of neurons in a particular group, the investigator can then identify functional classes of neurons based on their relationship to the task. The methodology is based on integration of a multiple signal classification (MUSIC) algorithm to extract relevant features from the firing rate and an expectation-maximization Gaussian mixture algorithm (EM-GMM) to cluster the extracted features. The methodology is capable of identifying and clustering similar firing rate profiles automatically based on specific signal features. An empirical wavelet transform (EWT) was used to validate the features found in the MUSIC pseudospectrum and the resulting signal features captured by the methodology. Additionally, this methodology was used to inspect behavioral elements of neurons to physiologically validate the model. This methodology was tested using a set of data collected from awake behaving non-human primates.

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Section: Methodsmentioning

confidence: 99%

MUSIC-Expected maximization gaussian mixture methodology for clustering and detection of task-related neuronal firing rates

Ortiz-Rosario

Adeli

Buford

2017

Behavioural Brain Research

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“…Omkar Singh et.al, [11] suggested techniques have contrasted with "empirical mode decomposition (EMD) based PLI cancellation" strategies. A sum of 6 strategies for PLI decrease dependent on EWT & EMD is investigated and their outcomes are introduced in this manuscript.…”

Section: Related Workmentioning

confidence: 99%

Baseline Wandering Removal in ECG Signal Using Filters

2020

IJATCSE

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The electrocardiogram (ECG) signals consists numerous kinds of noises; they are "electromyographic (EMG) noise, baseline wander (BW), electrode motion artefact, and power line interference". The EMG noise will be high frequency noise of above 100Hz & might be eliminated by a LPF of "suitable cut-off frequency". The BW will be low frequency noise of 0.5 to 0.6 Hz. To avoid it, HPF with "cut off frequency" 0.5 to 0.6 Hz might be utilized. The electrode motion artefacts might be suppresses through decreasing movements mad by subject. The power line interference might be eliminated by utilizing "notch filter of 50 or 60Hz cut-off frequency". This scheme prominence on diverse kinds of basic noise sources in ECG signals &applies easy signal processing methods for eliminating them, alongside amplification of information to 1x,2x,3x,4x and amplifying it accurately without losing the clinical centrality.

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“…When analyzing the time-frequency characteristics of GPR signals, EWT can provide a high resolution [20]. Meanwhile, it effectively remove the powerline interference and baseline wander in the electrocardiogram signal [21]. In research and application, EWT is a new and effective algorithm.…”

Section: Figure 1 Road Collapsementioning

confidence: 99%

Ground Penetrating Radar Weak Signals Denoising via Semi-soft Threshold Empirical Wavelet Transform

Qiao¹,

Feng²,

Zheng³

2019

ISI

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Ground penetrating radar (GPR) weak signals have the characteristics of low signal-to-noise ratio (SNR) and high frequency, which is a major challenge to noise attenuation. In this paper, we propose a GPR denoising approach based on empirical wavelet transform (EWT) combined with semi-soft thresholding. According to the frequency characteristics of signal, a spectrum segmentation strategy is designed. It can adaptively decompose signal and noise into different modes. The mode which contains more valid signals is processed by hard thresholding to reserve amplitude; the other modes which contain useless signals are processed by soft threshold functions to maintain the continuity of the signal. After weak signal denoising by our proposed method, we compared its performance on synthetic and field data using complete ensemble empirical mode decomposition (CEEMD) and synchro squeezed wavelet transform (SWT). The proposed method denoising performance is better than other two methods.

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Powerline interference reduction in ECG signals using empirical wavelet transform and adaptive filtering

Cited by 22 publications

References 14 publications

MUSIC-Expected maximization gaussian mixture methodology for clustering and detection of task-related neuronal firing rates

MUSIC-Expected maximization gaussian mixture methodology for clustering and detection of task-related neuronal firing rates

Baseline Wandering Removal in ECG Signal Using Filters

Ground Penetrating Radar Weak Signals Denoising via Semi-soft Threshold Empirical Wavelet Transform

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