Semi-real-time removal of baseline fluctuations in electrocardiogram (ECG) signals by an infinite impulse response low-pass filter (IIR-LPF)

Kim, Jeong‐Hwan; Lee, Kang-Hwi; Lee, Jeong‐Whan; Kim, Kyeong-Seop

doi:10.1007/s11227-018-2608-y

Cited by 9 publications

(4 citation statements)

References 11 publications

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“…Some of these methods use high pass filters for removing PLI and low pass filters to remove BW. However, these methods generate computational delays and nonlinear phase distortion [22][23][24][25]. This can be addressed by exploiting the advantage of Zero phase filter bank [26].…”

Section: Fourier Decomposition Methods For Noise Suppressionmentioning

confidence: 99%

An Improved R-Peaks Marking Method Using Fourier Decomposition and Teager Energy Operator

Harikrishna¹,

Shaik²

2020

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The exact discovery of R-peak becomes very much crucial while extracting prominent features from Electrocardiogram (ECG) signal. However, identification of R-peaks precisely becomes more challenging due to contamination of noise and fragmented QRS complexes. This paper presents an improved method of marking R-peaks. Initially, an efficient Fourier Decomposition Methodology (FDM) is used for removing noise. The accuracy of finding R-peaks can be improved by enhancing the QRS complexes using Teager Energy Operator. Hilbert Transform and Zero Cross Detector (ZCD) are used for marking the R-peaks. The MIT-BIH arrhythmia database is used for validating the proposed scheme and attained 99.97% accuracy, 99.98% of sensitivity and 99.98% of positive predictivity. The findings proved that proposed method is superior as compared to the proven techniques in the literature.

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Section: Fourier Decomposition Methods For Noise Suppressionmentioning

confidence: 99%

An Improved R-Peaks Marking Method Using Fourier Decomposition and Teager Energy Operator

Harikrishna¹,

Shaik²

2020

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“…Kim et al [9] proposed a new method for implementing low order Chebyshev Type II, IIR Low pass filter to remove baseline wander in ECG signal in semi real time. Den trending fluctuation analysis is used to remove baseline draft in the ECG signal.…”

Section: Existing Methodology 21related Workmentioning

confidence: 99%

Heart beat peak detection using signal filtering in ECG data

Rexy¹,

Velmani²,

Rajakumar³

2019

IJATEE

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The human heart is the muscular organ that pumps the blood through the circulatory system and paves way for a human to be alive. Human being loss their life if the heart malfunctions. Nowadays the death ratio of human being has been increased due to heart disease. Heart disease is the leading cause of many human deaths. The physical interpretation of the electrical behaviour throughout the cardiac cycle is recorded as electrocardiogram (ECG). ECG is used to measure the rate and rhythm of heartbeats which is the base to identify the heart beat disorders. ECG leads to monitor the heartbeat status and makes way for earlier detection of heart problems. ECG signal is a graphical representation or cardiac activity which helps to identify cardiac diseases [1].

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“…However, these two methods have no effect on the denoising of signal details, and further denoising needs to be combined with other methods [26,27]. Verma et al uses empirical wavelet transform to remove baseline drift noise [28]; Sanyal et al eliminated baseline wander based on a smooth wavelet tight frame with vanishing moments [29]; Sheetal et al combines a hybrid derivative and MaMeMi filter to remove baseline drift [30]; Kim et al semi-real-time removal of baseline fluctuations in ECG signals using an infinite impulse response low-pass filter [31]; These methods require artificial selection of appropriate wavelet basis functions or filter functions, which are subjective and easily lead to signal distortion. In an article presented at the 7th international symposium on sensor science [32], the empirical mode decomposition is used to decompose the signal into intrinsic mode functions and their residual steady-state quantities.…”

Section: Introductionmentioning

confidence: 99%

MEMS Hydrophone Signal Denoising and Baseline Drift Removal Algorithm Based on Parameter-Optimized Variational Mode Decomposition and Correlation Coefficient

Yan

Wang

et al. 2019

Sensors

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Underwater acoustic technology is an important means of detecting the ocean. Due to the complex influence of the marine environment, there is a lot of noise and baseline drift in the signals collected by hydrophones. In order to solve this problem, this paper proposes a denoising and baseline drift removal algorithm for MEMS vector hydrophone based on whale-optimized variational mode decomposition (VMD) and correlation coefficient (CC). Firstly, the power spectrum entropy (PSE), which reflects the variation characteristics of the signal frequency is selected as the fitness function of the whale-optimization algorithm to find the parameters (K,α) of the VMD. It is easier to find the global optimal solution of the parameters by combining the whale-optimization algorithm. Then, using the VMD algorithm after obtaining the parameters, the original signal is decomposed to obtain the intrinsic mode functions (IMFs), and calculating the correlation coefficients (CCs) between the IMFs and the original signal. Finally, the CC threshold is used to remove the noise IMFs, and the rest of the useful IMFs are reconstructed to complete the denoising and baseline drift removal process of the original signals. In the simulation experiments, the algorithm proposed in this paper shows better performance by comparing conventional digital signal-processing methods and the related algorithms proposed recently. Applied in the experiments of a MEMS hydrophone, the effectiveness of the proposed algorithm is also verified. This algorithm can provide new ideas for signal denoising and baseline drift removal.

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Semi-real-time removal of baseline fluctuations in electrocardiogram (ECG) signals by an infinite impulse response low-pass filter (IIR-LPF)

Cited by 9 publications

References 11 publications

An Improved R-Peaks Marking Method Using Fourier Decomposition and Teager Energy Operator

An Improved R-Peaks Marking Method Using Fourier Decomposition and Teager Energy Operator

Heart beat peak detection using signal filtering in ECG data

MEMS Hydrophone Signal Denoising and Baseline Drift Removal Algorithm Based on Parameter-Optimized Variational Mode Decomposition and Correlation Coefficient

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