On an automatic delineator for arterial blood pressure waveforms

Li, Bing Nan; Ming, Dong; Vai, Mang I

doi:10.1016/j.bspc.2009.06.002

Cited by 145 publications

(112 citation statements)

References 20 publications

Supporting

Mentioning

108

Contrasting

Unclassified

Order By: Relevance

“…In our experiments, to extract the three respiratory-induced variations, PPG beat detection was performed using a segmentation algorithm proposed in [16], where a "beat" in this context corresponds to the pulse in the PPG associated with a heartbeat. This processing produces a series of maximum and minimum intensities for each pulse detected.…”

Section: Methodsmentioning

confidence: 99%

“…Feature-based extraction of a respiratory signal consists of the extraction of a time series of beat-by-beat feature measurements. Beat detection is typically performed using a segmentation algorithm (such as that proposed by [16] for PPG signals or that proposed by [26] for ECG signals). AM is commonly extracted by measuring the pulse peak-to-trough amplitude; FM is extracted by measuring beat-to-beat intervals (between consecutive fiducial points such as pulse peaks), and a composite of AM and BW is extracted by measuring pulse peak amplitudes.…”

Section: Extraction Of Respiratory Signalsmentioning

confidence: 99%

“…The overall signal quality is assessed using four measures: three based upon template matching and a fourth based upon flat-line detection. For the template methods, the same beat detector [16] was applied to the PPG signal. The first 10 beats in each signal are assumed to be good quality, and a template is created by averaging windows centered on these 10 beats.…”

Section: Signal Quality Indexmentioning

confidence: 99%

See 2 more Smart Citations

Probabilistic Estimation of Respiratory Rate from Wearable Sensors

Pimentel

Charlton

Clifton

2015

Smart Sensors, Measurement and Instrumentation

View full text Add to dashboard Cite

Abstract. Respiration rate (RR) is a physiological parameter that is typically used in clinical settings for monitoring patient condition. Consequently, it is measured in a wide range of clinical scenarios, notably absent from which is measurement using wearable sensors. With increasing numbers of patients being monitored via wearable sensors, as described below, there is an urgent need to be able to estimate RR from such sensors in a robust manner. In this chapter, we describe a novel technique for measuring RR using waveform data acquired from wearable sensors.The technique derives RR from a physiological signal which is routinely acquired by many mobile sensors: the photoplethysmogram (PPG). Each RR measurement from the proposed method is accompanied by a confidence measure, providing estimates of clinical quality that will allow the system to, for example, only report RR values when they exceed some probabilistic level of certainty. The goal of this method is to improve upon existing methods, which simply report RR values without probabilistic estimation, and which therefore suffer the lack of robustness that prevents their use in clinical practice.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Extraction Of Respiratory Signalsmentioning

confidence: 99%

Section: Signal Quality Indexmentioning

confidence: 99%

See 1 more Smart Citation

Probabilistic Estimation of Respiratory Rate from Wearable Sensors

Pimentel

Charlton

Clifton

2015

Smart Sensors, Measurement and Instrumentation

View full text Add to dashboard Cite

show abstract

“…The foot point and systolic peak were detected as a pair of zero-crossing points in the first derivative of pressure wave in each period [3,18]. When cuff pressure was raised to a supra-systolic level, a tidal wave after the first systolic peak would appear in the late systole (see Figure 2) [16,19], which had been used to assess aortic stiffness [19,20].…”

Section: Feature Extraction and Pulse Waveform Parametersmentioning

confidence: 99%

Influential factors for pressure pulse waveform in healthy young adults

Zhi³

et al. 2015

BME

View full text Add to dashboard Cite

Abstract. The effects of gender and other contributory factors on pulse waveform are still under arguments. In view of different results caused by few considerations of possible influential factors and general agreement of gender relating to pulse waveform, this study aims to address the confounding factors interfering with the association between gender and pulse waveform characteristics. A novel method was proposed to noninvasively detect pressure pulse wave and assess the morphology of pulse wave. Forty healthy young subjects were included in the present research. Height, weight, systolic blood pressure (SBP), and diastolic blood pressure (DBP) were measured manually and body mass index (BMI), pulse blood pressure (PP) and heart rate (HR) were calculated automatically. Student's t test was used to analyze the gender difference and analysis of variance (ANOVA) to examine the effects of intrinsic factors. Univariate regression analysis was performed to assess the main factors on the waveform characteristics. Waveform features were found significantly different between genders. However this study indicates that the main factors for time-related and amplitude-related parameters are HR and SBP respectively. In conclusion, the impact of HR and SBP on pulse waveform features should not be underestimated, especially when analyzing the gender difference.

show abstract

“…Next, the characteristic points of a full beat of blood pressure signal should be estimated. A different method of estimation can be utilized including a windowed and weighted slope sum function [3], a filter bank with variable cutoff frequencies, rank-order nonlinear filters, and decision logic [4], inflection and zero-crossing points of blood pressure, and then combinatorial amplitude and interval criteria to select the onset and systolic peak [5], wavelets [6], principal components [7], waveform descriptor compared with a customized template [8], determined lines and polynomial approximation [9] or Fourier series interpolation [10].…”

Section: Introductionmentioning

confidence: 99%

Estimation of blood pressure parameters using ex-Gaussian model

Poliński

Kocejko

2016

Annals of Computer Science and Information Systems

View full text Add to dashboard Cite

Abstract-The paper presents an example of model-based estimation of blood pressure parameters (onset, systolic and diastolic pressure) from continuous measurements. First, the signal was low pass filtered and its quality was estimated. Good quality periods were divided into beats using an electrocardiogram. Next, the beginning of each beat of the blood pressure signal was approximated basing on the function created from the sum of two independent distributions: Gaussian and exponential. The nonlinear least square method was used to fit measurement data to the model. The initial conditions for the fitting procedure were selected for each beat on the basis of its parameters. Finally, the diastolic and systolic values of blood pressure and onset were determined.

show abstract

On an automatic delineator for arterial blood pressure waveforms

Cited by 145 publications

References 20 publications

Probabilistic Estimation of Respiratory Rate from Wearable Sensors

Probabilistic Estimation of Respiratory Rate from Wearable Sensors

Influential factors for pressure pulse waveform in healthy young adults

Estimation of blood pressure parameters using ex-Gaussian model

Contact Info

Product

Resources

About