Pulse baseline wander removal using wavelet approximation

Wang, K.Q.; Xu, Lisheng; Wang, L.; Li, Z.G.; Li, Y.Z.

doi:10.1109/cic.2003.1291228

Cited by 17 publications

(6 citation statements)

References 5 publications

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“…As a method of indirectly estimating and removing the baseline, interpolation methods, such as linear interpolation and cubic spline interpolation, can be used for baseline estimation. A method combining wavelet and least mean square (LMS) adaptive filter can also be used ( Wang et al, 2003 ). The linear interpolation method can simply estimate the baseline with a low-order polynomial.…”

Section: Resultsmentioning

confidence: 99%

Photoplethysmogram Analysis and Applications: An Integrative Review

et al. 2022

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Beyond its use in a clinical environment, photoplethysmogram (PPG) is increasingly used for measuring the physiological state of an individual in daily life. This review aims to examine existing research on photoplethysmogram concerning its generation mechanisms, measurement principles, clinical applications, noise definition, pre-processing techniques, feature detection techniques, and post-processing techniques for photoplethysmogram processing, especially from an engineering point of view. We performed an extensive search with the PubMed, Google Scholar, Institute of Electrical and Electronics Engineers (IEEE), ScienceDirect, and Web of Science databases. Exclusion conditions did not include the year of publication, but articles not published in English were excluded. Based on 118 articles, we identified four main topics of enabling PPG: (A) PPG waveform, (B) PPG features and clinical applications including basic features based on the original PPG waveform, combined features of PPG, and derivative features of PPG, (C) PPG noise including motion artifact baseline wandering and hypoperfusion, and (D) PPG signal processing including PPG preprocessing, PPG peak detection, and signal quality index. The application field of photoplethysmogram has been extending from the clinical to the mobile environment. Although there is no standardized pre-processing pipeline for PPG signal processing, as PPG data are acquired and accumulated in various ways, the recently proposed machine learning-based method is expected to offer a promising solution.

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Section: Resultsmentioning

confidence: 99%

Photoplethysmogram Analysis and Applications: An Integrative Review

et al. 2022

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“…1 depicts the proposed CAF filter. First, we decompose the pulse signal and detect its baseline drift level by computing its energy ratio (ER) [10]. If the ER is less than 50 dB, the pulse is filtered in two stages, with a discrete Meyer wavelet filter followed by cubic spline estimation.…”

Section: B Related Work On Baseline Drift Removalmentioning

confidence: 99%

Wavelet-Based Cascaded Adaptive Filter for Removing Baseline Drift in Pulse Waveforms

Zhang

Wang

2005

IEEE Trans. Biomed. Eng.

108

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ibrated single-plunge bipolar electrode array for mapping myocardial vector fields in three dimensions during high-voltage transthoracic defibrillation," IEEE Trans. Biomed. Eng., vol. 48,no. 8, Wavelet-Based Cascaded Adaptive Filter for Removing Baseline Drift in Pulse WaveformsLisheng Xu, David Zhang*, and Kuanquan WangAbstract-This paper presents an energy ratio-based method and a wavelet-based cascaded adaptive filter (CAF) for detecting and removing baseline drift from pulse waveforms. Experiments on 50 simulated and five hundred real pulse signals demonstrate that this CAF outperforms traditional filters both in removing baseline drift and in preserving the diagnostic information of pulse waveforms.

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“…Mneimneh et al [6] applied adaptive Kalman filter for removing the wander noise in ECG. Similarly, wavelet transform was selected for the pulse baseline wander removal [7][8][9]. The research by Chen et al [10] only considers the modified Gaussian models to exclude the pulse noise.…”

Section: Introductionmentioning

confidence: 99%

Period Segmentation for Wrist Pulse Signal Based on Adaptive Cascade Thresholding and Machine Learning

Wang

2014

2014 International Conference on Medical Biometrics

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Wrist pulse signal has been regarded as a physical health indicator for a long history in Traditional Chinese Medicine (TCM). The quantized pulse diagnosis by using the signal processing and pattern recognition technology is introduced to take over the traditional subjective judgments in recent years, and it's attracting more and more attention. However, the previous researches with pulse preprocessing mainly concentrate on the denoising and baseline wander correction procedure. The evaluation criterion isn't associated with the feature analysis, and the performance with shape classification doesn't give any contributions to the pulse diagnosis. Moreover, the signals are processed in a simulated environment by adding disturbance manually. In this paper, we propose a period segmentation method based on adaptive cascade thresholding and machine learning for extracting the information within single period. It's a novel preprocessing stage and the pulse data collected in real conditions for practical usage is analyzed. The experiments show that our method is significant in the pulse preprocessing stage and improves the accuracy for the disease classification between healthy subjects and diabetes.

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Pulse baseline wander removal using wavelet approximation

Abstract: Abstract

Cited by 17 publications

References 5 publications

Photoplethysmogram Analysis and Applications: An Integrative Review

Photoplethysmogram Analysis and Applications: An Integrative Review

Wavelet-Based Cascaded Adaptive Filter for Removing Baseline Drift in Pulse Waveforms

Period Segmentation for Wrist Pulse Signal Based on Adaptive Cascade Thresholding and Machine Learning

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