Enhanced Wavelet Transformation for Feature Extraction in Highly Variated ECG Signal

Lim, Ching Leng Peter; Woo, Wai Lok; Dlay, S.S.

doi:10.1049/cp.2015.1763

Cited by 8 publications

(3 citation statements)

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“…Ren et al [ 16 ] introduced an improved adaptive algorithm for interference cancellation of ECG signals. Lim et al [ 17 ] proposed an adaptive signal extraction method that uses discrete wavelet transformation coupled with adaptive parameters to address variated ECG signals due to varying heartrates. In addition, the adaptive filtering de-noising effect is good, and the waveform cannot be easily distorted; however, the computation load is quite heavy with AF.…”

Section: Introductionmentioning

confidence: 99%

ECG Signal De-noising and Baseline Wander Correction Based on CEEMDAN and Wavelet Threshold

Yang

Luo

Tao

et al. 2017

Sensors

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A novel electrocardiogram (ECG) signal de-noising and baseline wander correction method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and wavelet threshold is proposed. Although CEEMDAN is based on empirical mode decomposition (EMD), it represents a significant improvement of the original EMD by overcoming the mode-mixing problem. However, there has been no previous study on using CEEMDAN to de-noise ECG signals, to the authors’ best knowledge. In the proposed method, the original noisy ECG signal is decomposed into a series of intrinsic mode functions (IMFs) sorted from high to low frequency by CEEMDAN. Each IMF is then analyzed by the autocorrelation method to find out the first few high frequency IMFs containing random noise, and these IMFs should be de-noised by the wavelet threshold. The zero-crossing rate (ZCR) of all IMFs, including final residue, are computed, and the IMFs with ZCR less than a certain value are removed. Finally, the remaining IMFs are reconstructed to obtain the clean ECG signal. The proposed algorithm is validated through experiments using the MIT–BIH ECG databases, and the results show that the random noise in the ECG signal can be effectively suppressed, and at the same time the baseline wander can be corrected efficiently.

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Section: Introductionmentioning

confidence: 99%

ECG Signal De-noising and Baseline Wander Correction Based on CEEMDAN and Wavelet Threshold

Yang

Luo

Tao

et al. 2017

Sensors

View full text Add to dashboard Cite

show abstract

“…To enable a successful extraction of heartwave features, the extraction uses a Discrete Waveform Transform (DWT) hybridized with heartrate related parameters of QT Interval and PR Interval to perform extraction of features related to P-Wave and T-Wave [27]. In elevated heartrate, period of T-Wave under intense physical duress can vary as much as 40% as compared T-Wave under normal heartrate.…”

Section: Heartwave Data and Features Extractionmentioning

confidence: 99%

Heartrate-Dependent Heartwave Biometric Identification With Thresholding-Based GMM–HMM Methodology

Lim

Woo

Dlay

et al. 2019

IEEE Trans. Ind. Inf.

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“…However, DWT method has limitation for the delineation of heartwave from individual under elevated heartrate. Previously work has reported using DWT to extract heartwave of individual successfully [27]. However, it is to note that the mentioned work performed on signals acquired under non-physical duress condition.…”

Section: A Stage 1: Data Preparationmentioning

confidence: 99%

Deep Multiview Heartwave Authentication

Lim

Woo

Dlay

et al. 2019

IEEE Trans. Ind. Inf.

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This paper presents a heartwave based authentication method that utilizes an ensemble of Deep Belief Networks (DBNs) under different parameters to increase the reliability of feature extraction. The multi-view outputs are further embed into a single view before inputting into a stacked DBN for classification. The result of the proposed novel architecture achieved a classification rate of 98.3% with 30% training data. Importantly, it is able to perform user classification using heartwave signals acquired under intense physical exercise where heartrate ranges from 50bpm to as high as 180bpm. Under extreme physical duress, the heartwave from individual experiences extreme morphological variations which render conventional classification approaches non-applicable.

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Enhanced Wavelet Transformation for Feature Extraction in Highly Variated ECG Signal

Cited by 8 publications

References 0 publications

ECG Signal De-noising and Baseline Wander Correction Based on CEEMDAN and Wavelet Threshold

ECG Signal De-noising and Baseline Wander Correction Based on CEEMDAN and Wavelet Threshold

Heartrate-Dependent Heartwave Biometric Identification With Thresholding-Based GMM–HMM Methodology

Deep Multiview Heartwave Authentication

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