Ensemble empirical mode decomposition‐based optimised power line interference removal algorithm for electrocardiogram signal

Jebaraj, Jenitta; Arumugam, Rajeswari

doi:10.1049/iet-spr.2015.0292

Cited by 21 publications

(7 citation statements)

References 21 publications

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“…In [267], two different hybrid signal processing schemes, namely i) EEMD-BLMS (ensemble EMD (EEMD) combined by Block LMS (BLMS)) adaptive algorithm and ii) Wavelet neural network (WNN) (discrete Wavelet transform (DWT) combined by the neural network), have been applied for baseline wander and power line interference suppression. Another EEMD-based method for removing power line interference in noisy ECG recordings is introduced in [268], where they decomposed the noisy ECG signal into intrinsic mode functions (IMFs) via EMD. The work in [269] attempts to reduce the number of required components in filter implementation and then introduced nonrecursive FIR filters (NRFIR) for removal of base-line wander and power line interference from the ECG signal.…”

Section: ) Intra-subject Variabilitymentioning

confidence: 99%

A Comprehensive Survey on ECG Signals as New Biometric Modality for Human Authentication: Recent Advances and Future Challenges

Uwaechia

Ramli

2021

IEEE Access

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Electrocardiogram (ECG) has extremely discriminative characteristics in the biometric field and has recently received significant interest as a promising biometric trait. However, ECG signals are susceptible to several types of noises, such as baseline wander, powerline interference, and high/lowfrequency noises, making it challenging to realize biometric identification systems precisely and robustly. Therefore, ECG signal denoising is a major pre-processing step and plays a crucial role in ECG-based biometric human identification. ECG signal analysis for biometric recognition can combine several steps, such as preprocessing, feature extraction, feature selection, feature transformation, and classification which is a very challenging task. Moreover, the selected success measures and proper structure of the ECG signal database play significant roles in biometric system analysis, considering that publicly available databases are essential by the research community to evaluate the performance of their proposed algorithms. In this survey, we review most of the techniques employed for the ECG as biometrics for human authentication. Firstly, we present an overview and discussion on ECG signal preprocessing, feature extraction, feature selection, and feature transformation for ECG-based biometric systems. Secondly, we present a survey of the available ECG databases to evaluate and compare the acquisition protocol, acquisition hardware, and acquisition resolution (bits) for ECG-based biometric systems. Thirdly, we also present a survey on different techniques, including deep learning methods: deep supervised learning, deep semi-supervised learning, and deep unsupervised learning, for ECG signal classification. Lastly, we present the state-of-art approaches of information fusion in multimodal biometric systems.

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Section: ) Intra-subject Variabilitymentioning

confidence: 99%

A Comprehensive Survey on ECG Signals as New Biometric Modality for Human Authentication: Recent Advances and Future Challenges

Uwaechia

Ramli

2021

IEEE Access

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“…Due to the ability of dealing with non-stationary and nonlinear signals, the EMD-based ECG denoising methods gained extensive attention recently [15,16,17,18,19,20,21]. The EMD method was first introduced in [22] for analyzing data from nonstationary and nonlinear processes.…”

Section: Introductionmentioning

confidence: 99%

Sparse ECG Denoising with Generalized Minimax Concave Penalty

Jin

Dong

Shu

et al. 2019

Sensors

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The electrocardiogram (ECG) is an important diagnostic tool for cardiovascular diseases. However, ECG signals are susceptible to noise, which may degenerate waveform and cause misdiagnosis. In this paper, the ECG noise reduction techniques based on sparse recovery are investigated. A novel sparse ECG denoising framework combining low-pass filtering and sparsity recovery is proposed. Two sparsity recovery algorithms are developed based on the traditional ℓ 1 -norm penalty and the novel generalized minimax concave (GMC) penalty, respectively. Compared with the ℓ 1 -norm penalty, the non-differentiable non-convex GMC penalty has the potential to strongly promote sparsity while maintaining the convexity of the cost function. Moreover, the GMC punishes large values less severely than ℓ 1 -norm, which is utilized to overcome the drawback of underestimating the high-amplitude components for the ℓ 1 -norm penalty. The proposed methods are evaluated on ECG signals from the MIT-BIH Arrhythmia database. The results show that underestimating problem is overcome by the proposed GMC-based method. The GMC-based method shows significant improvement with respect to the average of output signal-to-noise ratio improvement ( S N R i m p ), the average of root mean square error (RMSE) and the percent root mean square difference (PRD) over almost any given SNR compared with the classical methods, thus providing promising approaches for ECG denoising.

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“…For example, Chang et al presented a scheme to cancel the white noise in ECG signals where noisy low-order IMFs were removed by a predefined threshold [ 12 ]. Jenitta et al used the zero-crossing ratio of adjacent IMFs to discriminate noisy IMFs by its noise energy [ 19 ]. Yannis et al proposed a scheme according to the energy of the first-order IMF through which noise cancellation was performed among IMFs for ECG signals [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

ECG Noise Cancellation Based on Grey Spectral Noise Estimation

Liu

Hsieh

Chen

et al. 2019

Sensors

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In recent years, wearable devices have been popularly applied in the health care field. The electrocardiogram (ECG) is the most used signal. However, the ECG is measured under a body-motion condition, which is easily coupled with some noise, like as power line noise (PLn) and electromyogram (EMG). This paper presents a grey spectral noise cancellation (GSNC) scheme for electrocardiogram (ECG) signals where two-stage discrimination is employed with the empirical mode decomposition (EMD), the ensemble empirical mode decomposition (EEMD) and the grey spectral noise estimation (GSNE). In the first stage of the proposed GSNC scheme, the input ECG signal is decomposed by the EMD to obtain a set of intrinsic mode functions (IMFs). Then, the noise energies of IMFs are estimated by the GSNE. When an IMF is considered as noisy one, it is forwarded to the second stage for further check. In the second stage, the suspicious IMFs are reconstructed and decomposed by the EEMD. Then the IMFs are discriminated with a threshold. If the IMF is considered as noisy, it is discarded in the reconstruction process of the ECG signal. The proposed GSNC scheme is justified by forty-three ECG signal datasets from the MIT-BIH cardiac arrhythmia database where the PLn and EMG noise are under consideration. The results indicate that the proposed GSNC scheme outperforms the traditional EMD and EEMD based noise cancellation schemes in the given datasets.

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Ensemble empirical mode decomposition‐based optimised power line interference removal algorithm for electrocardiogram signal

Cited by 21 publications

References 21 publications

A Comprehensive Survey on ECG Signals as New Biometric Modality for Human Authentication: Recent Advances and Future Challenges

A Comprehensive Survey on ECG Signals as New Biometric Modality for Human Authentication: Recent Advances and Future Challenges

Sparse ECG Denoising with Generalized Minimax Concave Penalty

ECG Noise Cancellation Based on Grey Spectral Noise Estimation

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