A flexible tonoarteriography-based body sensor network for cuffless measurement of arterial blood pressure

Ding, Xiaorong; Dai, Wenxuan; Luo, Ningqi; Liu, Jing; Zhao, Ni; Zhang, Yuan-Ting

doi:10.1109/bsn.2015.7299405

Cited by 14 publications

(6 citation statements)

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“…Definition of PTT The word "transit" is defined as "carrying of people or things from one place to another" according to Oxford Dictionaries. Accordingly, the definition of PTT-"The time for the carrying of pulse wave information by pulse signal from one location to another in the cardiovascular system"-is a more broad definition which covers the transit times determined by different approaches, such as the time intervals between R wave of the ECG and the PPG/tonoarteriogram (TAG) [40] which includes the PEP, impedance cardiogram (ICG)/ballistocardiogram (BCG)/ phonocardiogram (PCG) to PPG/TAG, PPG to TAG, or by two PPGs/TAGs at different arterial sites, etc. However, "arrival" should be referred to the pulse in the same form of energy from the starting point to the arrival point, suggesting that PAT is not equivalent of PTT.…”

Section: Definition and Calculation Of Pttmentioning

confidence: 99%

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Pulse transit time technique for cuffless unobtrusive blood pressure measurement: from theory to algorithm

Ding

Zhang

2019

Biomed. Eng. Lett.

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Cuffless technique holds great promise to measure blood pressure (BP) in an unobtrusive way, improving diagnostics and monitoring of hypertension and its related cardiovascular diseases, and maximizing the independence and participation of individual. Pulse transit time (PTT) has been the most commonly employed techniques for cuffless BP estimation. Many studies have been conducted to explore its feasibility and validate its performance in the clinical settings. However, there is still issues and challenges ahead before its wide application. This review will investigate the understanding and development of the PTT technique in depth, with a focus on the physiological regulation of arterial BP, the relationship between PTT and BP, and the summaries of the PTT-based models for BP estimation.

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Section: Definition and Calculation Of Pttmentioning

confidence: 99%

“…E for ECG; B represents the signals collected from the distal site, e.g. P for PPG, T for TAG [40], "c" represents the feature point of the distal signal, e.g. "f" for foot, "u" for maximal upslope, and "p" for peak 1.…”

Section: Relationship Between Ptt and Bpmentioning

confidence: 99%

Pulse transit time technique for cuffless unobtrusive blood pressure measurement: from theory to algorithm

Ding

Zhang

2019

Biomed. Eng. Lett.

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“…As illustrated in Fig. 8, PTT is usually calculated as time intervals between the R wave of electrocardiogram (ECG) and characteristic points of PPG/tonoarteriogram (TAG) [35], which includes the pre-ejection period (PEP), between impedance cardiogram (ICG)/ballistocardiogram (BCG)/seismocardiogram (SCG) [36,37]/phonocardiogram (PCG) and PPG/TAG, between PPG and TAG, or between two PPGs/TAGs at different arterial sites. PEP has been a concern to PTT-based BP estimation.…”

Section: Ptt Calculationmentioning

confidence: 99%

Continuous Blood Pressure Measurement From Invasive to Unobtrusive: Celebration of 200th Birth Anniversary of Carl Ludwig

Ding

Zhao

Yang

et al. 2016

IEEE J. Biomed. Health Inform.

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146

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“…PTT is related to PWV, and both exhibit an inverse relationship with BP [ 8 ]. The PTT is the time taken for an ECG R-wave to propagate to the PPG sensor located at the patient’s finger, as shown in Figure 3 [ 25 ].…”

Section: Methodsmentioning

confidence: 99%

Hybrid CNN-SVR Blood Pressure Estimation Model Using ECG and PPG Signals

Rastegar¹,

Hosseini

Lowe

2023

Sensors

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Continuous blood pressure (BP) measurement is vital in monitoring patients’ health with a high risk of cardiovascular disease. The complex and dynamic nature of the cardiovascular system can influence BP through many factors, such as cardiac output, blood vessel wall elasticity, circulated blood volume, peripheral resistance, respiration, and emotional behavior. Yet, traditional BP measurement methods in continuously estimating the BP are cumbersome and inefficient. This paper presents a novel hybrid model by integrating a convolutional neural network (CNN) as a trainable feature extractor and support vector regression (SVR) as a regression model. This model can automatically extract features from the electrocardiogram (ECG) and photoplethysmography (PPG) signals and continuously estimates the systolic blood pressure (SBP) and diastolic blood pressure (DBP). The CNN takes the correct topology of input data and establishes the relationship between ECG and PPG features and BP. A total of 120 patients with available ECG, PPG, SBP, and DBP data are selected from the MIMIC III database to evaluate the performance of the proposed model. This novel model achieves an overall Mean Absolute Error (MAE) of 1.23 2.45 mmHg (MAE ± STD) for SBP and 3.08 5.67 for DBP, all of which comply with the accuracy requirements of the AAMI SP10 standard.

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A flexible tonoarteriography-based body sensor network for cuffless measurement of arterial blood pressure

Cited by 14 publications

References 10 publications

Pulse transit time technique for cuffless unobtrusive blood pressure measurement: from theory to algorithm

Pulse transit time technique for cuffless unobtrusive blood pressure measurement: from theory to algorithm

Continuous Blood Pressure Measurement From Invasive to Unobtrusive: Celebration of 200th Birth Anniversary of Carl Ludwig

Hybrid CNN-SVR Blood Pressure Estimation Model Using ECG and PPG Signals

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