Multiresolution representation and analysis of ECG waveforms

Jager, F.; Koren, Iztok; Gyergyek, L.

doi:10.1109/cic.1990.144276

Cited by 7 publications

(5 citation statements)

References 7 publications

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“…Then, the sliding window length can be estimated roughly according to the above-mentioned principle. Besides, time domain energy of a signal [44] and instantaneous curvature of a signal's waveform [45] can also be used to identify boundaries of a PD pulse. In addition, ASTSVD can also locate the start points and the end points of a PD pulse by identifying the number of effective singular values r. The procedure is as follows: we move a sliding window point by point along the time axis of the PD signal.…”

Section: Principle Of Sliding Window Length Selectionmentioning

confidence: 99%

An Improved Denoising Method for Partial Discharge Signals Contaminated by White Noise Based on Adaptive Short-Time Singular Value Decomposition

Zhou

et al. 2019

Energies

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To extract partial discharge (PD) signals from white noise efficiently, this paper proposes a denoising method for PD signals, named adaptive short-time singular value decomposition (ASTSVD). First, a sliding window was moved along the time axis of a PD signal to cut a whole signal into segments with overlaps. The singular value decomposition (SVD) method was then applied to each segment to obtain its singular value sequence. The minimum description length (MDL) criterion was used to determine the number of effective singular values automatically. Then, the selected singular values of each signal segment were used to reconstruct the noise-free signal segment, from which the denoised PD signal was obtained. To evaluate ASTSVD, we applied ASTSVD and two other methods on simulated, laboratory-measured, and field-detected noisy PD signals, respectively. Compared to the other two methods, the denoised PD signals of ASTSVD contain less residual noise and exhibit smaller waveform distortion.

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Section: Principle Of Sliding Window Length Selectionmentioning

confidence: 99%

An Improved Denoising Method for Partial Discharge Signals Contaminated by White Noise Based on Adaptive Short-Time Singular Value Decomposition

Zhou

et al. 2019

Energies

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“…지금까지 연구되고 있는 심전도 신호 압축 방식은 직접 압축 방식, 변환 압축 방식 등이 있다 [3][4][5]. 또한 심전도 파 형의 정확한 기술(delineation)을 위하여 수치 미분, 패턴 인 식, 수학적 모델 등에 기반한 다양한 접근 방법이 제안되었 다 [6][7][8].…”

Section: 서 론unclassified

ECG Signal Compression using Feature Points based on Curvature

Kim¹,

Ryu²,

Yun³

et al. 2010

Journal of Korean Institute of Intelligent Systems

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As electrocardiogram(ECG) signals are generally sampled with a frequency of over 200Hz, a method to compress diagnostic information without losing data is required to store and transmit them efficiently. In this paper, an ECG signal compression method, which uses feature points based on curvature, is proposed. The feature points of P, Q, R, S, T waves, which are critical components of the ECG signal, have large curvature values compared to other vertexes. Thus, these vertexes are extracted with the proposed method, which uses local extremum of curvatures. Furthermore, in order to minimize reconstruction errors of the ECG signal, extra vertexes are added according to the iterative vertex selection method. Through the experimental results on the ECG signals from MIT-BIH Arrhythmia database, it is concluded that the vertexes selected by the proposed method preserve all feature points of the ECG signals. In addition, they are more efficient than the AZTEC(Amplitude Zone Time Epoch Coding) method.

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“…A major signal-processing task where multi-scale decomposition has been shown to be very useful is denoising, based on the intuition that information pertaining to the noise would be accurately characterized in certain scales that are separate from the scales of the signal. The main literature works in multi-scale decomposition have focused on scale-space decomposition [6,7,8,9,10], empirical mode decomposition [11,12,13], and wavelet transform [14,15,16,17,18]. In scale-space theory [19], a signal is decomposed into a single-parameter family of signals with a progressive decrease in fine scale signal information between successive scales.…”

Section: Introductionmentioning

confidence: 99%

MSICA: multi-scale signal decomposition based on independent component analysis with application to denoising and reliable multi-channel

Hajisami¹,

Pompili²

2020

ITU J-FET

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Multi-scale decomposition is a signal description method in which the signal is decomposed into multiple scales, which has been shown to be a valuable method in information preservation. Much focus on multi-scale decomposition has been based on scale-space theory and wavelet transform. In this article, a new powerful method to perform multi-scale decomposition exploiting Independent Component Analysis (ICA), called MSICA, is proposed to translate an original signal into multiple statistically independent scales. It is proven that extracting the independent components of the even and odd samples of a digital signal results in the decomposition of the same into approximation and detail. It is also proven that the whitening procedure in ICA is equivalent to a filter bank structure. Performance results of MSICA in signal denoising are presented; also, the statistical independency of the approximation and detail is exploited to propose a novel signal-denoising strategy for multi-channel noisy transmissions aimed at improving communication reliability by exploiting channel diversity.

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Multiresolution representation and analysis of ECG waveforms

Cited by 7 publications

References 7 publications

An Improved Denoising Method for Partial Discharge Signals Contaminated by White Noise Based on Adaptive Short-Time Singular Value Decomposition

An Improved Denoising Method for Partial Discharge Signals Contaminated by White Noise Based on Adaptive Short-Time Singular Value Decomposition

ECG Signal Compression using Feature Points based on Curvature

MSICA: multi-scale signal decomposition based on independent component analysis with application to denoising and reliable multi-channel

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