Cuff-Less Estimation of Blood Pressure Using Pulse Transit Time and Pre-ejection Period

Deb, Sujay; Nanda, Chinmayee; Goswami, Diganta; Mukhopadhyay, J.; Chakrabarti, Shamik

doi:10.1109/iccit.2007.4420381

Cited by 5 publications

(8 citation statements)

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“…The precise detection of the wave points such as R peak, PPG peak, etc., and explicit feature extraction plays a significant role in the estimation of BP. The approach to detect the above-stated wave points involves thresholding and derivative techniques [12], [24]. These techniques can detect R peak as well as PPG peak with acceptable precision, however, it is not capable enough to detect the rest of the wave points.…”

Section: Introductionmentioning

confidence: 99%

“…For the development of an independent feature vector, some of the above-stated research works involve the extraction of features from an isolated pulse of the physiological signal [12], [16], [24], [25]. Since the reference BP is measured using cuff based techniques, which provides BP after occlusion for about a minute, hence it is not a surrogate for beat by beat pressure.…”

Section: Introductionmentioning

confidence: 99%

“…The training can be done using various regression models such as linear regression (used most often) [23], [24], [26], non-linear regression models [27] and other machine learning techniques such as neural networks [11], [21]. The proposed model is trained using a log-linear regression analysis.…”

Section: Introductionmentioning

confidence: 99%

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Accurate Fiducial Point Detection Using Haar Wavelet for Beat-by-Beat Blood Pressure Estimation

Singla

Azeemuddin

Sistla

2020

IEEE J. Transl. Eng. Health Med.

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Pulse Arrival Time (PAT) derived from Electrocardiogram (ECG) and Photoplethysmogram (PPG) for cuff-less Blood Pressure (BP) measurement has been a contemporary and widely accepted technique. However, the features extracted for it are conventionally from an isolated pulse of ECG and PPG signals. As a result, the estimated BP is intermittent. Objective: This paper presents feature extraction from each beat of ECG and PPG signals to make BP measurements uninterrupted. These features are extracted by employing Haar transformation to adaptively attenuate measurement noise and improve the fiducial point detection precision. Method: the use of only PAT feature as an independent variable leads to an inaccurate estimation of either Systolic Blood Pressure (SBP) or Diastolic Blood Pressure (DBP) or both. We propose the extraction of supplementary features that are highly correlated to physiological parameters. Concurrent data was collected as per the Association for the Advancement of Medical Instrumentation (AAMI) guidelines from 171 human subjects belonging to diverse age groups. An Adaptive Window Wavelet Transformation (AWWT) technique based on Haar wavelet transformation has been introduced to segregate pulses. Further, an algorithm based on log-linear regression analysis is developed to process extracted features from each beat to calculate BP. Results: The mean error of 0.43 and 0.20 mmHg, mean absolute error of 4.6 and 2.3 mmHg, and Standard deviation of 6.13 and 3.06 mmHg is achieved for SBP and DBP respectively. Conclusions: The features extracted are highly precise and evaluated BP values are as per the AAMI standards. Clinical Impact: This continuous real-time BP monitoring technique can be useful in the treatment of hypertensive and potential-hypertensive subjects.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Accurate Fiducial Point Detection Using Haar Wavelet for Beat-by-Beat Blood Pressure Estimation

Singla

Azeemuddin

Sistla

2020

IEEE J. Transl. Eng. Health Med.

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“…Therefore, some methods have been proposed to estimate BP by electrocardiogram (ECG) and/or photoplethysmograph (PPG) [4][5][6][7]. Pulse transit time (PTT), pulse arrival time (PAT), and heart rate (HR) are generally employed methods in the estimation of BP [4,5,8].…”

Section: Introductionmentioning

confidence: 99%

A noninvasive time-frequency-based approach to estimate cuffless arterial blood pressure

Ertuğrul¹,

Sezgin²

2018

Turk J Elec Eng & Comp Sci

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Arterial blood pressure (ABP) is one of the most vital signs in the prophylaxis and treatment of blood pressure-related diseases because raised blood pressure is the most significant cause of death and the second major cause of disability in the world. Higher ABP yields greater strain on arteries and these extra strains turn arteries into thicker, less flexible, and more narrow structures. This increases the possibility of having an artery busting or artery occlusion, which are the primary reasons for heart attacks, kidney disease, or strokes. In addition to its importance in monitoring cardiovascular homeostasis, measurement of ABP is imperative in surgical operations. In this study, a simple and effective approach was proposed to estimate ABP from electrocardiogram (ECG) and photoplethysmograph (PPG) signals by an extreme learning machine (ELM) and statistical properties of the ECG and/or PPG signals in the time-frequency domain. To evaluate and apply the proposed approach, the Cuffless Blood Pressure Estimation Dataset, which was published and shared by UCI, was employed. First, the statistical properties were extracted from ECG and PPG signals that were in the time-frequency domain. Later, extracted features were employed to estimate cuffless ABP for each subject by the ELM and some popular machine learning methods. Achieved results and reported results in the literature showed that the proposed approach can be successfully employed for estimating cuffless blood pressure (BP) from ECGs and/or PPGs. Additionally, with the proposed approach, the systolic BP, mean BP, and diastolic BP can be calculated simultaneously.

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“…The delay caused by some of the existing medical devices causes some uncertainties in measuring the PTT [24]. Linear regression was used to estimate the blood pressure, which led to an estimation error of up to 25 mmHg [25]. A parameter extracted from the dicrotic notch of the PPG signal, called relative amplitude of secondary peak (RAS), was used for the estimation of systolic blood pressure in [26].…”

Section: Problem Statementmentioning

confidence: 99%

Signal Enhancement Applied to Pulse Transit Time Measurement

He¹

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High arterial blood pressure, or hypertension, is a major risk factor for cardiovascular diseases. Conventional noninvasive methods for estimating the arterial blood pressure rely on a brachial cuff that is placed around the upper arm. The cuff is inflated to a pressure that exceeds the systolic blood pressure. The heart pulse is monitored while the cuff is slowly deflated. The pressure at which the heart pulse sound can be detected corresponds to the systolic pressure and the pressure at which the heart pulse sound can no longer be detected corresponds to the diastolic pressure. This approach cannot be used on a continuous basis and has many disadvantages, including the fact that the system is cumbersome and causes discomfort to the patient, which may affect their blood pressure value. This thesis proposes two approaches to accurately measure the pulse transit time (PTT). The PTT can be used to estimate arterial blood pressure variations noninvasively, on a continuous basis without the use of a cuff. The correlation between the arterial blood pressure and PTT are verified using bio-signals from the MIMIC II database. The first approach for measuring the PTT relies on the electrocardiogram (ECG) and photo-plethysmograph (PPG) signals. Algorithms based on the empirical mode decomposition (EMD) method and the adaptive filtering techniques are proposed to enhance the corrupted ECG signal. Gaussian-based curve fitting algorithms are proposed to model and extract the embedded characteristic parameters from the original PPG signal.

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Cuff-Less Estimation of Blood Pressure Using Pulse Transit Time and Pre-ejection Period

Cited by 5 publications

References 0 publications

Accurate Fiducial Point Detection Using Haar Wavelet for Beat-by-Beat Blood Pressure Estimation

Accurate Fiducial Point Detection Using Haar Wavelet for Beat-by-Beat Blood Pressure Estimation

A noninvasive time-frequency-based approach to estimate cuffless arterial blood pressure

Signal Enhancement Applied to Pulse Transit Time Measurement

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