Continuous and Noninvasive Measurement of Systolic and Diastolic Blood Pressure by One Mathematical Model with the Same Model Parameters and Two Separate Pulse Wave Velocities

Chen, Yan; Chen, Wen; Tao, Guocai; Bi, Min

doi:10.1007/s10439-011-0467-2

Cited by 55 publications

(39 citation statements)

References 19 publications

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“…These peaks are distorted by reflection of pressure waves from the terminal arteries. Chen et al proposed a novel method based on experimental data to use the mean of PTT fp (time delay between falling edge of central PPG and peak of peripheral PPG) and PTT rp (time delay between rising edge of reflecting central PPG and peak of peripheral PPG) to obtain PTT for SBP (Figure 3c) [63]. Other studies have indicated that posture, ambient temperature, and relaxation affect PPG, raising a question on the development of an accurate PTT device [64].…”

Section: Pulse Transit Time (Ptt)mentioning

confidence: 99%

Cuff-Less and Continuous Blood Pressure Monitoring: A Methodological Review

et al. 2017

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Blood pressure (BP) is one of the most important monitoring parameters in clinical medicine. For years, the cuff-based sphygmomanometer and the arterial invasive line have been the gold standards for care professionals to assess BP. During the past few decades, the wide spread of the oscillometry-based BP arm or wrist cuffs have made home-based BP assessment more convenient and accessible. However, the discontinuous nature, the inability to interface with mobile applications, the relative inaccuracy with movement, and the need for calibration have rendered those BP oscillometry devices inadequate for next-generation healthcare infrastructure where integration and continuous data acquisition and communication are required. Recently, the indirect approach to obtain BP values has been intensively investigated, where BP is mathematically derived through the "Time Delay" in propagation of pressure waves in the vascular system. This holds promise for the realization of cuffless and continuous BP monitoring systems, for both patients and healthy populations in both inpatient and outpatient settings. This review highlights recent efforts in developing these next-generation blood pressure monitoring devices and compares various mathematical models. The unmet challenges and further developments that are crucial to develop "Time Delay"-based BP devices are also discussed.

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Section: Pulse Transit Time (Ptt)mentioning

confidence: 99%

Cuff-Less and Continuous Blood Pressure Monitoring: A Methodological Review

et al. 2017

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“…There are two typical methods to measure PTT. The first [14] is to calculate the delay between Electrocardiogram (ECG) and PPG, and the second method [15] is to measure the delay of different PPGs acquired from different parts of the body. To improve the accuracy of the evaluation of BP, a new feature vector extraction algorithm was proposed by Li et al in [16].…”

Section: Introductionmentioning

confidence: 99%

Novel wavelet neural network algorithm for continuous and noninvasive dynamic estimation of blood pressure from photoplethysmography

Liu

Zhang

et al. 2015

Sci. China Inf. Sci.

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This paper proposes a novel wavelet neural network algorithm for the continuous and noninvasive dynamic estimation of blood pressure (BP). Unlike prior algorithms, the proposed algorithm capitalizes on the correlation between photoplethysmography (PPG) and BP. Complete BP waveforms are reconstructed based on PPG signals to extract systolic blood pressure (SBP) and diastolic blood pressure (DBP). To improve the robustness, Daubechies wavelet is implemented as the hidden layer node function for the neural network. An optimized neural network structure is proposed to reduce the computational complexity. Further, this paper investigates an inhomogeneous resilient backpropagation (IRBP) algorithm to calculate the weight of hidden layer nodes. The IRBP improves the convergence speed and reconstruction accuracy. Multiparameter intelligent monitoring in Intensive Care (MIMIC) databases, which contain a variety of physiological parameters captured from patient monitors, are used to validate this algorithm. The standard deviation σ between reconstructed and actual BP signals is 4.4797 mmHg, which satisfies the American National Standards of the Association for the Advancement of Medical Instrumentation. The reconstructed BP waveform can be used to extract the SBP and DBP, whose standard deviations σ are 2.91 mmHg and 2.41 mmHg respectively.Keywords photoplethysmogram (PPG), blood pressure (BP), Daubechies wavelet, neural network, inhomogeneous resilient backpropagation (IRBP) CitationLi P, Liu M, Zhang X, et al. Novel wavelet neural network algorithm for continuous and noninvasive dynamic estimation of blood pressure from photoplethysmography.

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“…where f(PTT) could be PTT [39,[49][50][51][52][53][54][55][56][57] or its variants, such as 1/PTT [58,59], 1/PTT 2 [60,61], ln(PTT) [40,61], and e k*PTT [62] [63]. Rather than only PTT, extra parameters related to cardiovascular activity can be introduced:…”

Section: Ptt-bp Modelingmentioning

confidence: 99%