Estimation of central pulse wave velocity from radial pulse wave analysis

Yang, Yao; Zhou, Shuran; Alastruey, Jordi; Hao, Liling; Greenwald, Stephen E.; Zhang, Yuelan; Xu, Lin; Xu, Lisong; Yao, Yu-Dong

doi:10.1016/j.cmpb.2022.106781

Cited by 10 publications

(5 citation statements)

References 41 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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“…These methods eliminate the need for physical contact with the patient, which is particularly crucial for monitoring in unique or sensitive situations. For instance, the polarized multispectral imaging technique for noncontact heart rate measurement has refined the accuracy of data acquisition by pioneering new methods for extracting pulse waves from the palm [22]. This innovation contributes to the establishment of a standardized framework for pulse data, thus facilitating seamless data sharing and comparisons across various devices and systems.…”

Section: Palpationmentioning

confidence: 99%

AI: Bridging Ancient Wisdom and Modern Innovation in Traditional Chinese Medicine

Lu,

Tian

et al. 2024

JMIR Med Inform

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Traditional Chinese medicine (TCM) is a holistic medical system with more than 2,000 years of empirical backing, with unique diagnostic methods, including inspection, auscultation and olfaction, inquiry, and palpation. Despite TCM's holistic and subjective diagnostic approach, Artificial intelligence (AI) promises to enhance diagnostic accuracy, treatment efficacy, and prognosis by leveraging advanced data analysis and machine learning techniques. This review provides an overview the potential applications of AI across TCM's four main diagnostic methods, highlighting recent advancements, challenges, and future directions. AI-powered Tuina massage robots and AI-directed acupuncture manipulation, underscoring the potential for personalized treatment and improved clinical outcomes. However, successful AI integration faces challenges including data quality, interdisciplinary collaboration, computational interpretation of TCM theories, ethical considerations, and regulatory frameworks. Addressing these challenges is crucial for realizing AI's full potential in TCM and advancing patient care in modern healthcare systems.

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“…Poor health condition may cause variations in arterial properties, affecting pulse-related parameters including morphology, strength, rhythm, etc. [ 2 , 3 ]. Pulse diagnosis (PD), which evaluates health status by analyzing tactile radial arterial palpation with trained fingertips, plays an important role in oriental medicine including traditional Chinese medicine (TCM) and traditional Korean medicine (TKM) [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Towards Generating Realistic Wrist Pulse Signals Using Enhanced One Dimensional Wasserstein GAN

Chang

et al. 2023

Sensors

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For the past several years, there has been an increasing focus on deep learning methods applied into computational pulse diagnosis. However, one factor restraining its development lies in the small wrist pulse dataset, due to privacy risks or lengthy experiments cost. In this study, for the first time, we address the challenging by presenting a novel one-dimension generative adversarial networks (GAN) for generating wrist pulse signals, which manages to learn a mapping strategy from a random noise space to the original wrist pulse data distribution automatically. Concretely, Wasserstein GAN with gradient penalty (WGAN-GP) is employed to alleviate the mode collapse problem of vanilla GANs, which could be able to further enhance the performance of the generated pulse data. We compared our proposed model performance with several typical GAN models, including vanilla GAN, deep convolutional GAN (DCGAN) and Wasserstein GAN (WGAN). To verify the feasibility of the proposed algorithm, we trained our model with a dataset of real recorded wrist pulse signals. In conducted experiments, qualitative visual inspection and several quantitative metrics, such as maximum mean deviation (MMD), sliced Wasserstein distance (SWD) and percent root mean square difference (PRD), are examined to measure performance comprehensively. Overall, WGAN-GP achieves the best performance and quantitative results show that the above three metrics can be as low as 0.2325, 0.0112 and 5.8748, respectively. The positive results support that generating wrist pulse data from a small ground truth is possible. Consequently, our proposed WGAN-GP model offers a potential innovative solution to address data scarcity challenge for researchers working with computational pulse diagnosis, which are expected to improve the performance of pulse diagnosis algorithms in the future.

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Estimation of central pulse wave velocity from radial pulse wave analysis

Cited by 10 publications

References 41 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

AI: Bridging Ancient Wisdom and Modern Innovation in Traditional Chinese Medicine

Towards Generating Realistic Wrist Pulse Signals Using Enhanced One Dimensional Wasserstein GAN

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