A comparative analysis of EMD based filtering methods for 50 Hz noise cancellation in ECG signal

Suchetha, M.; Kumaravel, N.; Mani, Mohan Prasath; Jaganathan, Saravana Kumar

doi:10.1016/j.imu.2017.01.003

Cited by 37 publications

(14 citation statements)

References 13 publications

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“…Routinely, QC procedures for HFR radial velocity maps rely on the definition of threshold values that are applied to SNR or radial velocity speed. Examples of operational procedures applied in RT mode for HFR in general and WERA systems in particular can be found in [28,29], while advanced methodologies capable of handling complex cases of external radio frequency interferences are described in [13]. In most cases, these values are defined "a priori" without any assumption on the real range of variability or the physical processes in the region.…”

Section: Discussionmentioning

confidence: 99%

“…A first approach, presently implemented and operational across the Australian Ocean Radar HFR network, makes use of an iterative method that fits a 1-D or 2-D reference signal to the radial SNR map, and identifies and flags anomalous data that clearly stand out from the background SNR values. Other approaches, currently under investigation, act on the I/Q time series of the range-gated signals at each antenna, and subsequently identify and filter any anomalous signal in the frequency domain in a manner similar to [28][29][30][31]. The conventional beam-forming and the Doppler spectra inversion steps are then applied on the filtered data.…”

Section: Advanced Artifact Removalmentioning

confidence: 99%

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Improving Data Quality for the Australian High Frequency Ocean Radar Network through Real-Time and Delayed-Mode Quality-Control Procedures

et al. 2018

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Quality-control procedures and their impact on data quality are described for the High-Frequency Ocean Radar (HFR) network in Australia, in particular for the commercial phased-array (WERA) HFR type. Threshold-based quality-control procedures were used to obtain radial velocity and signal-to-noise ratio (SNR), however, values were set through quantitative analyses with independent measurements available within the HFR coverage, when available, or from long-term data statistics. An artifact removal procedure was also applied to the spatial distribution of SNR for the first-order Bragg peaks, under the assumption the SNR is a valid proxy for radial velocity quality and that SNR decays with range from the receiver. The proposed iterative procedure was specially designed to remove anomalous observations associated with strong SNR peaks caused by the 50 Hz sources. The procedure iteratively fits a polynomial along the radial beam (1-D case) or a surface (2-D case) to the SNR associated with the radial velocity. Observations that exceed a detection threshold were then identified and flagged. After removing suspect data, new iterations were run with updated detection thresholds until no additional spikes were found or a maximum number of iterations was reached.

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Section: Discussionmentioning

confidence: 99%

Section: Advanced Artifact Removalmentioning

confidence: 99%

Improving Data Quality for the Australian High Frequency Ocean Radar Network through Real-Time and Delayed-Mode Quality-Control Procedures

et al. 2018

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“…Originally, empirical mode decomposition (EMD) [11] is an adaptive data-driven method to process non-linear and non-stationary signals. This technology has been applied in various fields, such as the fault diagnosis of rotational machinery [12,13], signal filtering [14,15], and biomedical signal processing [16]. However, the decomposed IMFs may include discrete scales in one mode, or alike scales in different modes.…”

Section: Introductionmentioning

confidence: 99%

Fault Feature Extraction and Diagnosis of Rolling Bearings Based on Enhanced Complementary Empirical Mode Decomposition with Adaptive Noise and Statistical Time-Domain Features

Zhan

Zhang

et al. 2019

Sensors

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In this paper, a novel method is proposed to enhance the accuracy of fault diagnosis for rolling bearings. First, an enhanced complementary empirical mode decomposition with adaptive noise (ECEEMDAN) method is proposed by determining two critical parameters, namely the amplitude of added white noise (AAWN) and the ensemble trails (ET). By introducing the concept of decomposition level, the optimal AAWN can be determined by judging the mutation of mutual information (MI) between adjacent intrinsic mode functions (IMFs). Furthermore, the ET is fixed at two to reduce the computational cost. This method can avoid disturbance of the spurious mode in the signal decomposition and increase computational speed. Enhanced CEEMDAN demonstrates a more significant improvement than that of the traditional CEEMDAN. Vibration signals can be decomposed into a set of IMFs using enhanced CEEMDAN. Some IMFs, which are named intrinsic information modes (IIMs), effectively reflect the vibration characteristic. The evaluated comprehensive factor (CF), which combines the shape, crest and impulse factors, as well as the kurtosis, skewness, and latitude factor, is developed to identify the IIM. CF can retain the advantage of a single factor and make up corresponding drawbacks. Experiment results, especially for the extraction of bearing fault under variable speed, illustrate the superiority of the proposed method for the fault diagnosis of rolling bearings over other methods.

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“…The existing ECG detection methodologies aim to improve the detection accuracy via sophisticated signal processing techniques. Some of the popular detection methodologies can be numerated as spectrum analysis methods (Fourier transform and short-time Fourier transform) [9], [10], mathematical morphology [11], wavelet transform (WT) [12], empirical mode decomposition (EMD) [13], blind source separation [14], and sparsity based strategies [15].…”

Section: Introductionmentioning

confidence: 99%

Centralized Wavelet Multiresolution for Exact Translation Invariant Processing of ECG Signals

Chen

Zeng

2019

IEEE Access

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Dyadic wavelet transform is useful in analyzing electrocardiogram (ECG) signals due to its fast computation and its multiresolution ability. In order to improve the feature extraction performance of dyadic wavelet transform, a new construction example of centralized multiresolution (CMR) is proposed. The proposed CMR example consists of two elements, namely, a dyadic part and a non-dyadic part. The dyadic part, based on the maximal overlap second generation wavelet packet transform (SGWPT), generates dyadic wavelet packets. The non-dyadic part engenders ensemble wavelet packets by postprocessing on the dyadic part. The produced wavelet packets and ensemble wavelet packets are combined to realize continued spectral refinement around fixed central analysis frequencies. Numerical simulation and a case study of ECG signal decomposition are utilized to validate the enhancements of the proposed CMR example. The processing results of the CMR example are compared with those of the dual tree complex wavelet transform and the conventional SGWPT. It is validated this CMR example achieves better feature extraction performances due to the presence of the exact translation invariance property.

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A comparative analysis of EMD based filtering methods for 50 Hz noise cancellation in ECG signal

Cited by 37 publications

References 13 publications

Improving Data Quality for the Australian High Frequency Ocean Radar Network through Real-Time and Delayed-Mode Quality-Control Procedures

Improving Data Quality for the Australian High Frequency Ocean Radar Network through Real-Time and Delayed-Mode Quality-Control Procedures

Fault Feature Extraction and Diagnosis of Rolling Bearings Based on Enhanced Complementary Empirical Mode Decomposition with Adaptive Noise and Statistical Time-Domain Features

Centralized Wavelet Multiresolution for Exact Translation Invariant Processing of ECG Signals

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