Pulse wave analysis as a tool for the evaluation of resuscitation therapy in septic shock

Campitelli, Riccardo; Ferrario, Manuela; Su, Fuhong; Creteur, Jacques; Herpain, Antoine; Carrara, Marta

doi:10.1088/1361-6579/acfc94

Cited by 5 publications

(1 citation statement)

References 42 publications

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“…The roles of PWA in the diagnosis of CVDs encompass the assessment of arterial stiffness, pulse wave velocity analysis, and pulse waveform analysis. Several physiological parameters, such as the peripheral augmentation index (pAIx), pulse transit time (PTT), reflection magnitude (RM), and reflection index (RI), are employed in PWA ( Munir et al, 2008 ; Wang et al, 2010 ; Milicevic et al, 2020 ; Yao et al, 2022 ; Campitelli et al, 2023 ). The tidal peak (P 2 ), representing the second wave peak of the pure radial arterial pulse signals within a cardiac cycle, is one of the four key physiological points, which also include percussion peaks (P 1 ), diastolic notches (P 3 ), and diastolic peaks (P 4 ) ( Su et al, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

Increasing the sensor channels: a solution for the pressing offsets that cause the physiological parameter inaccuracy in radial artery pulse signal acquisition

Chen,

Luo

et al. 2024

Front. Bioeng. Biotechnol.

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Introduction: In studies of pulse wave analysis, single-channel sensors only adopt single temporal pulse signals without spatial information to show pulse-feeling patterns. Multi-channel arterial pulse signals, also named as three-dimensional pulse images (3DPIs), provide the spatial and temporal characteristics of radial pulse signals. When involving single or few-channel sensors, pressing offsets have substantial impacts on obtaining inaccurate physiological parameters like tidal peak (P2).Methods: This study discovers the pressing offsets in multi-channel pulse signals and analyzes the relationship between the pressing offsets and time of P2 (T2) by qualifying the pressing offsets. First, we employ a data acquisition system to capture 3DPIs. Subsequently, the errorT2 is developed to qualify the pressing offsets.Results: The outcomes display a central low and peripheral high pattern. Additionally, the errorT2 increase as the distances from the artery increase, particularly at the radial ends of the blood flow direction. For every 1 mm increase in distances between sensing elements and center sensing elements, the errorT2 in the radial direction escalates by 4.87%. When the distance is greater than 3.42 mm, the errorT2 experiences a sudden increase.Discussion: The results show that increasing the sensor channels can overcome the pressing offsets in radial pulse signal acquisition.

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Section: Introductionmentioning

confidence: 99%

Increasing the sensor channels: a solution for the pressing offsets that cause the physiological parameter inaccuracy in radial artery pulse signal acquisition

Chen,

Luo

et al. 2024

Front. Bioeng. Biotechnol.

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The role of pulse wave analysis indexes for critically ill patients: a narrative review

Carrara,

Campitelli,

Guberti

et al. 2024

Physiol. Meas.

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Objective. Arterial pulse wave analysis (PWA) is now established as a powerful tool to investigate the cardiovascular system, and several clinical studies have shown how PWA can provide valuable prognostic information over and beyond traditional cardiovascular risk factors. Typically these techniques are applied to chronic conditions, such as hypertension or aging, to monitor the slow structural changes of the vascular system which lead to important alterations of the arterial PW. However, their application to acute critical illness is not currently widespread, probably because of the high hemodynamic instability and acute dynamic alterations affecting the cardiovascular system of these patients. Approach. In this work we propose a review of the physiological and methodological basis of PWA, describing how it can be used to provide insights into arterial structure and function, cardiovascular biomechanical properties, and to derive information on wave propagation and reflection. The applicability of these techniques to acute critical illness, especially septic shock, is extensively discussed, highlighting the feasibility of their use in acute critical patients and their role in optimizing therapy administration and hemodynamic monitoring. Significance. The potential for the clinical use of these techniques lies in the ease of computation and availability of arterial blood pressure signals, as invasive arterial lines are commonly used in these patients. We hope that the concepts illustrated in the present review will soon be translated into clinical practice.

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An accurate tidal peak localization method in radial arterial pulse signals based on hybrid neural networks

Chen,

Luo

et al. 2024

J. Inst.

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Background: cardiovascular diseases (CVDs) have become the leading causes of death worldwide. Arterial stiffness and elasticity are important indicators of cardiovascular health. Pulse wave analysis (PWA) is essential for analyzing arterial stiffness and elasticity, which are highly dependent on the tidal peak (P 2). P 2 is one of the four key physiological points, which also include percussion peaks (P 1), diastolic notches (P 3), and diastolic peaks (P 4). P 1, P 3, and P 4 are often local maxima or minima, facilitating their identification via the second derivatives method, a classic localization method for key physiological points. Classic methods such as the second derivative method, Empirical Mode Decomposition (EMD), and Wavelet Transform (WT), have been employed for the extraction and analysis of the P 2. Due to individual variation and arterial stiffness, locating the P 2 using classic methods is particularly challenging. Methods: we propose a hybrid neural network based on Residual Networks (ResNet) and bidirectional Long Short-Term Memory Networks (Bi-LSTM), successfully achieving high-precision localization of the P 2 in radial artery pulse signals. Meanwhile, we compared our method with the second derivative method, EMD, WT, Convolutional Neural Networks (CNN) and the hybrid model with ResNet and LSTM. Results: the results indicate that our proposed model exhibits significantly higher accuracy compared to other algorithms. Overall, MAEs and RMSEs for our proposed method are 62.60% and 58.84% on average less than those for other algorithms. The average R Adj 2 is 29.20% higher. The outcomes of the efficiency evaluation suggest that the hybrid model performs more balancedly without any significant shortcomings, which indicates that the Bi-LSTM structure upgrades the performances of LSTM. Significance: our hybrid model can provide the medical field with improved diagnostic tools and promote the development of clinical practice and research.

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Pulse wave analysis as a tool for the evaluation of resuscitation therapy in septic shock

Cited by 5 publications

References 42 publications

Increasing the sensor channels: a solution for the pressing offsets that cause the physiological parameter inaccuracy in radial artery pulse signal acquisition

Increasing the sensor channels: a solution for the pressing offsets that cause the physiological parameter inaccuracy in radial artery pulse signal acquisition

The role of pulse wave analysis indexes for critically ill patients: a narrative review

An accurate tidal peak localization method in radial arterial pulse signals based on hybrid neural networks

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