Measurement of Pulse Transit Time (PTT) Using Photoplethysmography

Lokharan, M.; Kumar, Kiran; Kumar, V H Vishnu; Kayalvizhi, N.; Aryalekshmi, R.

doi:10.1007/978-981-10-4220-1_24

Cited by 8 publications

(7 citation statements)

References 9 publications

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“…In the design, 505 nm PPG signal y1false(kfalse) is used as a reference, and the PTT of the i -th PPG signal is expressed as PTTi=(1fs)argmaxτ∈normalZfalse(Corrfalse(y1false(kfalse),yifalse(kfalse),τfalse)false), i=1,...,15 where Corrfalse(y1false(kfalse),yifalse(kfalse),τfalse)=true∑y1*false(kfalse)yifalse(k+τfalse), i=1,...,15 is the cross-correlation function between y1false(kfalse) and yifalse(kfalse), τ is the discrete index displacement, and fs is the sampling rate of the developed MW-PPG sensor. As reported in [31], skin is a layer structure and blood vessels are located in different layers, for example, small arteries are located in hypodermis layer which is the innermost layer of skin, arterioles are located in dermis layer and capillaries are located in the epidermis layer....…”

Section: Design Of the Developed Mw-ppg Sensormentioning

confidence: 99%

MW-PPG Sensor: An on-Chip Spectrometer Approach

Chang

Choi

et al. 2019

Sensors

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Multi-wavelength photoplethysmography (MW-PPG) sensing technology has been known to be superior to signal-wavelength photoplethysmography (SW-PPG) sensing technology. However, limited by the availability of sensing detectors, many prior studies can only use conventional bulky and pricy spectrometers as the detectors, and hence cannot bring the MW-PPG technology to daily-life applications. In this study we developed a chip-scale MW-PPG sensor using innovative on-chip spectrometers, aimed at wearable applications. Also in this paper we present signal processing methods for robustly extracting the PPG signals, in which an increase of up to 50% in the signal-to-noise ratio (S/N) was observed. Example measurements of saturation of peripheral blood oxygen (SpO2) and blood pressure were conducted.

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Section: Design Of the Developed Mw-ppg Sensormentioning

confidence: 99%

MW-PPG Sensor: An on-Chip Spectrometer Approach

Chang

Choi

et al. 2019

Sensors

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“…These were developed based on the known relationship between blood pressure and pulse arrival time (PAT). Therefore, blood pressure can be continuously estimated by continuously measuring changes in the PAT 6 , 7 . Proença et al expressed the relationship between PAT and blood pressure using a nonlinear equation 8 , and Whong and Poon estimated the relationship between blood pressure, PAT, and heart rate through linear regression 9 .…”

Section: Introductionmentioning

confidence: 99%

Combined deep CNN–LSTM network-based multitasking learning architecture for noninvasive continuous blood pressure estimation using difference in ECG-PPG features

Jeong

Lim

2021

Sci Rep

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The pulse arrival time (PAT), the difference between the R-peak time of electrocardiogram (ECG) signal and the systolic peak of photoplethysmography (PPG) signal, is an indicator that enables noninvasive and continuous blood pressure estimation. However, it is difficult to accurately measure PAT from ECG and PPG signals because they have inconsistent shapes owing to patient-specific physical characteristics, pathological conditions, and movements. Accordingly, complex preprocessing is required to estimate blood pressure based on PAT. In this paper, as an alternative solution, we propose a noninvasive continuous algorithm using the difference between ECG and PPG as a new feature that can include PAT information. The proposed algorithm is a deep CNN–LSTM-based multitasking machine learning model that outputs simultaneous prediction results of systolic (SBP) and diastolic blood pressures (DBP). We used a total of 48 patients on the PhysioNet website by splitting them into 38 patients for training and 10 patients for testing. The prediction accuracies of SBP and DBP were 0.0 ± 1.6 mmHg and 0.2 ± 1.3 mmHg, respectively. Even though the proposed model was assessed with only 10 patients, this result was satisfied with three guidelines, which are the BHS, AAMI, and IEEE standards for blood pressure measurement devices.

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“…These were developed based on the known relationship between blood pressure and pulse transit time (PTT). Therefore, blood pressure can be continuously estimated by continuously measuring changes in the PTT [6,7]. Proença et al expressed the relationship between PTT and blood pressure using a nonlinear equation [8], and Whong and Poon estimated the relationship between blood pressure, PTT, and heart rate through linear regression [9].…”

Section: Introductionmentioning

confidence: 99%

Combined Deep CNN–LSTM Network-based Multitasking Learning Architecture for Noninvasive Continuous Blood Pressure Estimation using Difference in ECG-PPG Features

Jeong

Lim

2021

Preprint

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The pulse transit time (PTT), which is the difference between the R-peak time of the electrocardiogram (ECG) signal and the systolic peak of the photoplethysmography (PPG) signal, is an indicator that enables noninvasive and continuous blood pressure estimation. However, it is difficult to accurately measure the PTT from the ECG and PPG signals because they have inconsistent shapes owing to patient-specific physical characteristics, pathological conditions, and movements. Accordingly, complex preprocessing is required to estimate blood pressure based on PTT. In this paper, as an alternative solution, we propose a noninvasive continuous algorithm using the difference between the ECG and PPG as a new feature that can include PTT information. The proposed algorithm is a deep CNN–LSTM-based multitasking machine learning model that outputs simultaneous prediction results of systolic (SBP) and diastolic blood pressures (DBP). The prediction accuracies of SBP and DBP using the proposed model were 0.017±1.624 mmHg and 0.164±1.297 mmHg, respectively. This result corresponded to Grade A according to the BHS and AAMI standards, which are the validation standards for blood pressure measuring devices.

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Measurement of Pulse Transit Time (PTT) Using Photoplethysmography

Cited by 8 publications

References 9 publications

MW-PPG Sensor: An on-Chip Spectrometer Approach

MW-PPG Sensor: An on-Chip Spectrometer Approach

Combined deep CNN–LSTM network-based multitasking learning architecture for noninvasive continuous blood pressure estimation using difference in ECG-PPG features

Combined Deep CNN–LSTM Network-based Multitasking Learning Architecture for Noninvasive Continuous Blood Pressure Estimation using Difference in ECG-PPG Features

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