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

Xu, Lisheng; Zhang, David; Wang, Kuanquan

doi:10.1109/tbme.2005.856296

Cited by 108 publications

(41 citation statements)

References 8 publications

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“…Chinese Pulse Diagnosis Theory (CPDT) [9] has been used in clinical diagnosis for thousands of years and has been proved to be valuable. According to CPDT, wrist-pulse data contains the abundant physiological and pathological parameter information of human body.…”

Section: Health Status Detectionmentioning

confidence: 99%

“…According to CPDT [9], we must first extract the five most important time-domain feature points (see Figure 3) before running MFA algorithm in our health status detection. Thus, we propose FEA, an adaptive feature extraction algorithm for pulse data.…”

Section: Feature Extractionmentioning

confidence: 99%

“…Table 3: Different weights of multi-preference parameters for health status detection. [9,10], we investigate that what wrist-pulse parameters from diverse sources (i.e., Cun, Guan and Chi on left and right human wrists [13]) have great impacts on revealing disease/symptom information. Based on this investigation (see Figure 8), we further compute different weight values that are listed in the 2-6 columns of Table 3.…”

Section: Health Status Detection Performancementioning

confidence: 99%

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MPD-Model: A Distributed Multipreference-Driven Data Fusion Model and Its Application in a WSNs-Based Healthcare Monitoring System

Gong

Xiao

et al. 2012

International Journal of Distributed Sensor Networks

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We first propose an MPD-Model, a novel distributed multipreference-driven data fusion model for WSNs. Here, preferences are looked as the core elements of collaboration mechanism in a data fusion procedure. We then present MFA, a distributed multipreference feature-level fusion algorithm based on weighted average method. Next, to implement feature extraction of wristpulse data, we propose FEA, a light-weight adaptive feature extraction algorithm for time series sensed data. Simultaneously, we design TFD-Pattern that is a unique human pulse pattern. Based on historical data, we propose an SVM-based algorithm for health status detection tasks. Finally, we implement the proposed methods in a real wearable healthcare monitoring system which had been previously developed in-house. We validate the proposed methods using real-world data sets with 2046 pulse samples. Experimental results show that the proposed methods outperform the baseline methods, and the proposed MPD-Model is reasonable and effective.

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Section: Health Status Detectionmentioning

confidence: 99%

Section: Feature Extractionmentioning

confidence: 99%

Section: Health Status Detection Performancementioning

confidence: 99%

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MPD-Model: A Distributed Multipreference-Driven Data Fusion Model and Its Application in a WSNs-Based Healthcare Monitoring System

Gong

Xiao

et al. 2012

International Journal of Distributed Sensor Networks

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“…An EMD-based method described in our former work [18] is adopted for denoising. To address the baseline drift problem, the wavelet-based cascade adaptive filter method [20] …”

Section: Data Acquisition and Preprocessingmentioning

confidence: 99%

“…As shown in Figure 3, we define several fiducial points, and the meanings (of a, b, c, d, a') are explained in Table 1. The procedure of fiducial point location is described as follows 1) Find the onsets of each period using the method described in [20], and then locate the points a and a', and their corresponding time labels are t a and t a' .…”

Section: Spatial Feature Extraction Of Blood Flow Velocity Signalmentioning

confidence: 99%

Wrist blood flow signal-based computerized pulse diagnosis using spatial and spectrum features

Zhang¹,

Zuo²,

Zhang³

et al. 2010

JBiSE

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Current computerized pulse diagnosis is mainly based on pressure and photoelectric signal. Considering the richness and complication of pulse diagnosis information, it is valuable to explore the feasibility of novel types of signal and to develop appropriate feature representation for diagnosis. In this paper, we present a study on computerized pulse diagnosis based on blood flow velocity signal. First, the blood flow velocity signal is collected using Doppler ultrasound device and preprocessed. Then, by locating the fiducial points, we extract the spatial features of blood flow velocity signal, and further present a Hilbert-Huang transform-based method for spectrum feature extraction. Finally, support vector machine is applied for computerized pulse diagnosis. Experiment results show that the proposed method is effective and promising in distinguishing healthy people from patients with cholecystitis or nephritis.

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Adaptive filter for eliminating baseline wander of pulse wave signals

Fedotov

Akulova

2015

IFMBE Proceedings

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

Cited by 108 publications

References 8 publications

MPD-Model: A Distributed Multipreference-Driven Data Fusion Model and Its Application in a WSNs-Based Healthcare Monitoring System

MPD-Model: A Distributed Multipreference-Driven Data Fusion Model and Its Application in a WSNs-Based Healthcare Monitoring System

Wrist blood flow signal-based computerized pulse diagnosis using spatial and spectrum features

Adaptive filter for eliminating baseline wander of pulse wave signals

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