Central Venous Pulse Pressure Analysis Using an R-Synchronized Pressure Measurement System

Fujita, Yoshihisa; Hayashi, Daisuke; Wada, Shinya; Yoshioka, Naoya; Yasukawa, Takeshi; Pestel, G.

doi:10.1007/s10877-006-9035-y

Cited by 4 publications

(6 citation statements)

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“…As demonstrated by clinical study [ 10 , 19 ] and Figure 1 and 2 , the primary morphological feature of JET onset lies in the a peak. We propose the following features extraction strategy to measure characteristics of the a peak and the overall CVP cycle waveform.…”

Section: Methodsmentioning

confidence: 95%

“…This signal reflects the amount of the blood that is returned to the heart [ 1 ] and the filling pressure of the right ventricle [ 5 ]. The characteristics and amplitude of the CVP waveform components can change significantly with arrhythmias and tricuspid valve pathology [ 19 , 10 ]. Thus, this signal provides valuable clinical information for arrhythmia diagnosis and automatic detection.…”

Section: Introductionmentioning

confidence: 99%

“…During JET, the characteristics and amplitude of the CVP waveform components change significantly. A tall a wave, termed a cannon a wave is observed [ 19 , 10 ]. Figure 1 presents a processed and aligned median stack of CVP waveform.…”

Section: Introductionmentioning

confidence: 99%

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Detection of Junctional Ectopic Tachycardia by Central Venous Pressure

Tan

Dai

Humayun

et al. 2021

Artificial Intelligence in Medicine

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Central venous pressure (CVP) is the blood pressure in the venae cavae, near the right atrium of the heart. This signal waveform is commonly collected in clinical settings, and yet there has been limited discussion of using this data for detecting arrhythmia and other cardiac events. In this paper, we develop a signal processing and feature engineering pipeline for CVP waveform analysis. Through a case study on pediatric junctional ectopic tachycardia (JET), we show that our extracted CVP features reliably detect JET with comparable results to the more commonly used electrocardiogram (ECG) features. This machine learning pipeline can thus improve the clinical diagnosis and ICU monitoring of arrhythmia. It also corroborates and complements the ECG-based diagnosis, especially when the ECG measurements are unavailable or corrupted.

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Section: Methodsmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

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Detection of Junctional Ectopic Tachycardia by Central Venous Pressure

Tan

Dai

Humayun

et al. 2021

Artificial Intelligence in Medicine

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“…Measurements were performed after induction of anesthesia but before starting surgery. The ECG and radial pressure signals were analyzed using a PC with an ECG-R synchronization program [6]. Briefly, the two signals were sampled for 10 s at 1000 Hz from a hemodynamic monitor and transferred to a PC equipped with an analog to digital interface (PCI-3156; Interface Co., Hiroshima, Japan) and custom-made software (R-Synch, Version 1).…”

Section: Methodsmentioning

confidence: 99%

Do radial arterial pressure curves have diagnostic validity for identify severe aortic stenosis?

et al. 2010

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“…67 In contemporary practice, the CVP waveform is used simply to extract the CVP value at end expiration. 85 With the appropriate technology 85 other secondary variables can be extracted from CVP which could be used to predict cardiopulmonary decompensation by expressing the underlying physiology.…”

Section: Advanced Waveform Analysis: Untapped Secondary Variablesmentioning

confidence: 99%

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Holder

Clermont

2015

Critical Care Clinics

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A physiologic signature can be defined as a consistent and robust collection of physiologic measurements characterizing a disease process and its temporal evolution. If a library of physiologic signatures of impending cardiopulmonary instability were available to clinicians caring for inpatients, many episodes of clinical decompensation and their downstream effects could potentially be averted. The development and resolution of cardiopulmonary instability are processes that take time to become clinically apparent, and the treatments provided take time to have an impact. The characterization of dynamic changes in hemodynamic and metabolic variables is implicit in the concept of physiologic signatures. Changes in vital signs such as blood pressure and heart rate, as well as measures of flow such as cardiac output are some of the standard variables used by clinicians to determine cardiopulmonary instability. When these primary variables are collected with high enough frequency to derive new variables, this data hierarchy can be used to development physiologic signatures. The construction of new variables from primary variables, and therefore the creation of physiologic signatures requires no new information; additional knowledge is extracted from data that already exists. It is possible to create physiologic signatures for each stage in the process of clinical decompensation and recovery to improve patient outcomes.

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Central Venous Pulse Pressure Analysis Using an R-Synchronized Pressure Measurement System

Abstract: The mean CVP can be used as an index of right ventricular preload in patients under mechanical ventilation with regular sinus rhythm. Our newly developed system is useful for clinical monitoring and for education in circulatory physiology.

Cited by 4 publications

References 7 publications

Detection of Junctional Ectopic Tachycardia by Central Venous Pressure

Detection of Junctional Ectopic Tachycardia by Central Venous Pressure

Do radial arterial pressure curves have diagnostic validity for identify severe aortic stenosis?

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