Changes of Arterial Pulse Waveform Characteristics with Gestational Age during Normal Pregnancy

Li, Kunyan; Zhang, Song; Yang, Lin; Jiang, Haihua; Chi, Zhenyu; Wang, Anran; Yang, Yimin; Li, Xuwen; Hao, Dongmei; Zhang, Lei; Zheng, Dingchang

doi:10.1038/s41598-018-33890-1

Cited by 17 publications

(12 citation statements)

References 41 publications

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“…Three typical characteristic peak of wrist pulses were captured including the percussion wave (P‐wave), tidal wave (T‐wave), valley, and diastolic wave (D‐wave). Many studies have indicated the correlation between pulse waveform and physical and pathological characters of cardiovascular system in human bodies . So accurate waveform is essential to the quantitative analysis of the cardiovascular parameters.…”

Section: Resultsmentioning

confidence: 99%

High‐Performance Flexible Tactile Sensor Enabling Intelligent Haptic Perception for a Soft Prosthetic Hand

Liang

Cheng

Zhao

et al. 2019

Adv Materials Technologies

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Compared to the traditional motor‐driven prosthetic hand, a soft prosthetic hand (SPD) has intrisic advantages such as kinematics dexterity and friendly external interaction. In order for an SPD to realize the function of a real hand, the integration of flexible high‐performance tactile sensors is essential. Current research on flexible tactile sensors is mainly focused on increasing sensitivity, but ignores other issues, such as the complexity of arraying and compatibility with soft actuators. A high‐performance flexible force sensor enabling intelligent tactile perception for an SPD is reported. With the aid of reasonable arrangement of functional material and effective 3D structure design, the force sensor exhibits good linearity (R2 = 0.982), accuracy and repeatability, very low hysteresis, fast dynamical response (<1 ms), high stability (30000 loading–unloading cycles), stable performance under high frequency (>50 Hz), and can easily be arrayed and integrated with an SPD. With such a sensor mounted on its surface, an SPD achieves various complicated tasks in the same manner as a real hand, including feeling the softness of objects and imitating the process of traditional Chinese medicine pulse diagnosis to monitor a pulse wave in the radial artery. Finally, through use of a force‐sensor array, an SPD provides real‐time spatial distribution of pressure during the grabbing of objects.

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Section: Resultsmentioning

confidence: 99%

High‐Performance Flexible Tactile Sensor Enabling Intelligent Haptic Perception for a Soft Prosthetic Hand

Liang

Cheng

Zhao

et al. 2019

Adv Materials Technologies

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“…The arterial pulse wave is a rich source of information for assessing vascular health in humans as it is influenced by the cardiac and vascular properties 80 and thus can reflect physiological changes in the cardiovasculature. 80 – 82 Arterial pulse signals are measured in both clinical practice and wearable devices. Two commonly obtained pulse signals are the PPG and radial BP.…”

Section: Future Research Directionsmentioning

confidence: 99%

Leveraging the potential of machine learning for assessing vascular ageing: state-of-the-art and future research

Bikia

Fong

Climie

et al. 2021

European Heart Journal - Digital Health

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Vascular ageing biomarkers have been found to be predictive of cardiovascular risk independently of classical risk factors, yet are not widely used in clinical practice. In this review we present two basic approaches for using machine learning (ML) to assess vascular age: parameter estimation and risk classification. We then summarize their role in developing new techniques to assess vascular ageing quickly and accurately. We discuss the methods used to validate ML-based markers, the evidence for their clinical utility, and key directions for future research. The review is complemented by case studies of the use of ML in vascular age assessment which can be replicated using freely available data and code.

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“…Usually, patients with hypertension also have other individual differences, which results in interference of PPG waveforms and decreases the fitting accuracy of the model. These individual differences can be affected by several factors, such as diabetes, arrhythmia, pregnancy, lifestyle, age, gender, body mass index, daily variations, or environmental conditions such as temperature and experimental errors during the data measurement [34][35][36][37][38]. As cardiovascular features and signals vary among conditions and change with time, determining how much the BP estimation varies depending on a factor or in terms of time is important.…”

Section: Introductionmentioning

confidence: 99%

A Data-Driven Model with Feedback Calibration Embedded Blood Pressure Estimator Using Reflective Photoplethysmography

Chen

Huang

Fang

et al. 2022

Sensors

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Ambulatory blood pressure (BP) monitoring (ABPM) is vital for screening cardiovascular activity. The American College of Cardiology/American Heart Association guideline for the prevention, detection, evaluation, and management of BP in adults recommends measuring BP outside the office setting using daytime ABPM. The recommendation to use night–day BP measurements to confirm hypertension is consistent with the recommendation of several other guidelines. In recent studies, ABPM was used to measure BP at regular intervals, and it reduces the effect of the environment on BP. Out-of-office measurements are highly recommended by almost all hypertension organizations. However, traditional ABPM devices based on the oscillometric technique usually interrupt sleep. For all-day ABPM purposes, a photoplethysmography (PPG)-based wrist-type device has been developed as a convenient tool. This optical, noninvasive device estimates BP using morphological characteristics from PPG waveforms. As measurement can be affected by multiple variables, calibration is necessary to ensure that the calculated BP values are accurate. However, few studies focused on adaptive calibration. A novel adaptive calibration model, which is data-driven and embedded in a wearable device, was proposed. The features from a 15 s PPG waveform and personal information were input for estimation of BP values and our data-driven calibration model. The model had a feedback calibration process using the exponential Gaussian process regression method to calibrate BP values and avoid inter- and intra-subject variability, ensuring accuracy in long-term ABPM. The estimation error of BP (ΔBP = actual BP—estimated BP) of systolic BP was −0.1776 ± 4.7361 mmHg; ≤15 mmHg, 99.225%, and of diastolic BP was −0.3846 ± 6.3688 mmHg; ≤15 mmHg, 98.191%. The success rate was improved, and the results corresponded to the Association for the Advancement of Medical Instrumentation standard and British Hypertension Society Grading criteria for medical regulation. Using machine learning with a feedback calibration model could be used to assess ABPM for clinical purposes.

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Changes of Arterial Pulse Waveform Characteristics with Gestational Age during Normal Pregnancy

Cited by 17 publications

References 41 publications

High‐Performance Flexible Tactile Sensor Enabling Intelligent Haptic Perception for a Soft Prosthetic Hand

High‐Performance Flexible Tactile Sensor Enabling Intelligent Haptic Perception for a Soft Prosthetic Hand

Leveraging the potential of machine learning for assessing vascular ageing: state-of-the-art and future research

A Data-Driven Model with Feedback Calibration Embedded Blood Pressure Estimator Using Reflective Photoplethysmography

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