Wavelet transform analysis of the arterial pressure waveform

Antonelli, Lynn T.; Khamlach, R.; Ohley, William J.

doi:10.1109/tfsa.1994.467288

Cited by 3 publications

(1 citation statement)

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“…Most of the reported detectors paid major attention on systolic peaks and the onsets or dicrotic notches only [1][2][3][4]. Although the onset of ABP pulse denotes the arrival of the arterial pressure pulse at the recording site, the majority of the ABP pulse and pulse-component detection methods identify the peak of the ABP waveform as the fiducial mark of the ABP pulse [1,[5][6][7]. As reported previously [8], the approach of peak detection rather than pulse onset detection is unsuitable for studying pulse wave velocity and ECG-ABP delay time characteristics, as the duration of the upslope depends, among other things, on ventricular and valvular properties.…”

Section: Introductionmentioning

confidence: 99%

Empirical wavelet transform-based delineator for arterial blood pressure waveforms

Singh

Sunkaria

2016

Bio-Algorithms and Med-Systems

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Arterial blood pressure (ABP) waveforms provide plenty of pathophysiological information about the cardiovascular system. ABP pulse analysis is a routine process used to investigate the health status of the cardiovascular system. ABP pulses correspond to the contraction and relaxation phenomena of the human heart. The contracting or pumping phase of the cardiac chamber corresponds to systolic pressure, whereas the resting or filling phase of the cardiac chamber corresponds to diastolic pressure. An ABP waveform commonly comprises systolic peak, diastolic onset, dicrotic notch, and dicrotic peak. Automatic ABP delineation is extremely important for various biomedical applications. In this paper, a delineator for onset and systolic peak detection in ABP signals is presented. The algorithm uses a recently developed empirical wavelet transform (EWT) for the delineation of arterial blood pulses. EWT is a new mathematical tool used to decompose a given signal into different modes and is based on the design of an adaptive wavelet filter bank. The performance of the proposed delineator is evaluated and validated over ABP waveforms of standard databases, such as the MIT-BIH Polysomnoghaphic Database, Fantasia Database, and Multiparameter Intelligent Monitoring in Intensive Care Database. In terms of pulse onset detection, the proposed delineator achieved an average error rate of 0.11%, sensitivity of 99.95%, and positive predictivity of 99.92%. In a similar manner for systolic peak detection, the proposed delineator achieved an average error rate of 0.10%, sensitivity of 99.96%, and positive predictivity of 99.92%.

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Section: Introductionmentioning

confidence: 99%

Empirical wavelet transform-based delineator for arterial blood pressure waveforms

Singh

Sunkaria

2016

Bio-Algorithms and Med-Systems

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An open-source algorithm to detect onset of arterial blood pressure pulses

Zong

Heldt

Moody

et al. 2003

Computers in Cardiology, 2003

257

190

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In this paper; we present an effective algorithm for detecting the onset of anerial blood pressure (ABP) IntroductionThe ABP waveform contains rich information about the cardiovascular system, such as heart rate, systolic, mean, and diastolic arterial pressures, and it can be used to assess properties of the arterial vessel wall [l, 21. Reliable and accurate ABP pulse detection is crucial for beat-by-beat extraction and analysis of the information mentioned above. This task is rendered difficult, however, since the ABP measurement is prone to noise and artifacts (see Figure 1 (b) and (d)). Furthermore, the waveform morphology can change dramatically, even over short periods of time, in response to altered pathologic or physiologic stresses (Figure 1 (a) and (c)).Although it is the ABP pulse onset that denotes the arrival of the arterial pressure pulse at the recording site, most ABP pulse and pulse-component detection algorithms identify the peak of the ABP waveform as the fiducial

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