Literature DB >> 24722801

Characters available in photoplethysmogram for blood pressure estimation: beyond the pulse transit time.

Yanjun Li1, Zengli Wang, Lin Zhang, Xianglin Yang, Jinzhong Song.   

Abstract

The continuous and noninvasive blood pressure (BP) measurement based on pulse transit time (PTT) doesn't need cuff and could monitor BP in real time for a long period. However, PTT is just a time index derived from electrocardiogram (ECG) and photoplethysmogram (PPG), while BP-related information within the PPG waveform has seldom been taken into consideration. We hypothesized that PPG waveform feature might be useful for BP estimation. Nine healthy subjects took part in an exercise stress test, including baseline resting, exercise on bicycle ergometry and recovering resting. ECG of lead V5 and PPG from left finger were collected simultaneously, and systolic blood pressure (SBP) and diastolic blood pressure (DBP) were recorded from a cuff sphygmometer on the right wrist. The correlation coefficients were obtained between BP (SBP, DBP and pulse pressure (PP)) and PPG morphological indices (total 15 indices in terms of waveform amplitude, time span and area ratio). Five PPG indices were correlated with both SBP and PP (absolute value of correlation coefficient |r| > 0.6) and were further tested for the capability to BP estimation, which were: (1) PTTA, time delay between the R peak of ECG and the foot point of PPG; (2) RSD, time ratio of systole to diastole; (3) RtArea, area ratio of systole to diastole; (4) TmBB, time span of PPG cycle; (5) TmCA, diastolic duration. Comparisons were made between the measured BP and the estimated BP by regression lines and quadratic curve fitting, respectively. As a result, the mean errors of SBP liner fitting with RSD, RtArea, TmBB and TmCA respectively were 5.5, 5.4, 5.2, 5.1 mmHg, which were smaller than that with PTTA of 5.8 mmHg. And the mean errors of SBP quadratic curve fitting with RSD, RtArea, TmBB and TmCA were all 5.1 mmHg, which were smaller than that with PTTA of 5.7 mmHg. The mean errors of multiple regression for SBP, PP and DBP was 4.7, 4.7, 3.5 mmHg respectively, which were more accurate than the regression with single PTTA of 5.8, 5.3, 5.2 mmHg respectively. However, PPG-based SBP and DBP could under estimate cuff pressure by 8 mmHg and over estimate by 10 mmHg respectively, which is a clinically significant error. In conclusion, the combination of time span (PTT, time ratio of systole to diastole, time span of PPG cycle and diastolic duration) and waveform morphology (area ratio of systole to diastole) could improve the performance of PPG-based BP estimation.

Entities:  

Mesh:

Year:  2014        PMID: 24722801     DOI: 10.1007/s13246-014-0269-6

Source DB:  PubMed          Journal:  Australas Phys Eng Sci Med        ISSN: 0158-9938            Impact factor:   1.430


  9 in total

1.  The Relevance of Calibration in Machine Learning-Based Hypertension Risk Assessment Combining Photoplethysmography and Electrocardiography.

Authors:  Jesús Cano; Lorenzo Fácila; Juan M Gracia-Baena; Roberto Zangróniz; Raúl Alcaraz; José J Rieta
Journal:  Biosensors (Basel)       Date:  2022-05-01

2.  Feasibility study for the non-invasive blood pressure estimation based on ppg morphology: normotensive subject study.

Authors:  Hangsik Shin; Se Dong Min
Journal:  Biomed Eng Online       Date:  2017-01-10       Impact factor: 2.819

3.  Increasing accuracy of pulse transit time measurements by automated elimination of distorted photoplethysmography waves.

Authors:  Marit H N van Velzen; Arjo J Loeve; Sjoerd P Niehof; Egbert G Mik
Journal:  Med Biol Eng Comput       Date:  2017-03-30       Impact factor: 2.602

4.  Cuffless Blood Pressure Estimation Using Pressure Pulse Wave Signals.

Authors:  Zeng-Ding Liu; Ji-Kui Liu; Bo Wen; Qing-Yun He; Ye Li; Fen Miao
Journal:  Sensors (Basel)       Date:  2018-12-02       Impact factor: 3.576

5.  How Effective Is Pulse Arrival Time for Evaluating Blood Pressure? Challenges and Recommendations from a Study Using the MIMIC Database.

Authors:  Yongbo Liang; Derek Abbott; Newton Howard; Kenneth Lim; Rabab Ward; Mohamed Elgendi
Journal:  J Clin Med       Date:  2019-03-11       Impact factor: 4.241

6.  Baroreflex Sensitivity Measured by Pulse Photoplethysmography.

Authors:  Jesús Lázaro; Eduardo Gil; Michele Orini; Pablo Laguna; Raquel Bailón
Journal:  Front Neurosci       Date:  2019-04-18       Impact factor: 4.677

7.  Photoplethysmography and Deep Learning: Enhancing Hypertension Risk Stratification.

Authors:  Yongbo Liang; Zhencheng Chen; Rabab Ward; Mohamed Elgendi
Journal:  Biosensors (Basel)       Date:  2018-10-26

8.  Generalized Deep Neural Network Model for Cuffless Blood Pressure Estimation with Photoplethysmogram Signal Only.

Authors:  Yan-Cheng Hsu; Yung-Hui Li; Ching-Chun Chang; Latifa Nabila Harfiya
Journal:  Sensors (Basel)       Date:  2020-10-04       Impact factor: 3.576

Review 9.  Assessing hemodynamics from the photoplethysmogram to gain insights into vascular age: a review from VascAgeNet.

Authors:  Peter H Charlton; Birutė Paliakaitė; Kristjan Pilt; Martin Bachler; Serena Zanelli; Dániel Kulin; John Allen; Magid Hallab; Elisabetta Bianchini; Christopher C Mayer; Dimitrios Terentes-Printzios; Verena Dittrich; Bernhard Hametner; Dave Veerasingam; Dejan Žikić; Vaidotas Marozas
Journal:  Am J Physiol Heart Circ Physiol       Date:  2021-12-24       Impact factor: 4.733

  9 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.