Pre-ejection period, the reason why the electrocardiogram Q-wave is an unreliable indicator of pulse wave initialization

Balmer, Joel; Pretty, Christopher G.; Davidson, Shaun; Desaive, Thomas; Kamoi, Shun; Pironet, Antoine; Morimont, Philippe; Janssen, Nathalie; Lambermont, Bernard; Shaw, Geoffrey M.; Chase, J. Geoffrey

doi:10.1088/1361-6579/aada72

Cited by 26 publications

(21 citation statements)

References 47 publications

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“…As mentioned above, in practice, the R-peak location in ECG signal is often used as a surrogate to aortic valve opening due to simpler measurement. However, using PATs gives only mediocre accuracy compared to PTT due to the physiological variations in PEP [7,8]. Our finding are similar, see for example, Fig.…”

Section: Predictions Of Apwvsupporting

confidence: 85%

“…To overcome this difficulty, PTT is often approximated using pulse arrival time (PAT) which uses the R-wave of electrocardiogram (ECG) as a reference timing [6]. However, there exists controversy in the clinical accuracy of using PAT in the predictions due to variations in pre-ejection period (PEP) from the R-wave to aortic valve opening [7,8]. An alternative approach is to approximate the reference with a measurement from another distal site near aorta.…”

Section: Introductionmentioning

confidence: 99%

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Pulse transit time estimation of aortic pulse wave velocity and blood pressure using machine learning and simulated training data

2019

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Recent developments in cardiovascular modelling allow us to simulate blood flow in an entire human body. Such model can also be used to create databases of virtual subjects, with sizes limited only by computational resources. In this work, we study if it is possible to estimate cardiovascular health indices using machine learning approaches. In particular, we carry out theoretical assessment of estimating aortic pulse wave velocity, diastolic and systolic blood pressure and stroke volume using pulse transit/arrival timings derived from photopletyshmography signals. For predictions, we train Gaussian process regression using a database of virtual subjects generated with a cardiovascular simulator. Simulated results provides theoretical assessment of accuracy for predictions of the health indices. For instance, aortic pulse wave velocity can be estimated with a high accuracy ( r > 0.9) when photopletyshmography is measured from left carotid artery using a combination of foot-to-foot pulse transmit time and peak location derived for the predictions. Similar accuracy can be reached for diastolic blood pressure, but predictions of systolic blood pressure are less accurate ( r > 0.75) and the stroke volume predictions are mostly contributed by heart rate.

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Section: Predictions Of Apwvsupporting

confidence: 85%

Section: Introductionmentioning

confidence: 99%

Pulse transit time estimation of aortic pulse wave velocity and blood pressure using machine learning and simulated training data

2019

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“…When the time is measured relative to the ECG QRS complex then it is generally used to define the term pulse arrival time (PAT), an interchangeably measure of PTT. Despite that, both timings could implicate poor correlation due to the variability of the pre-ejection period (PEP), which is from the related ECG QRS complex to the aortic valve opening [ 17 ]. This article follows the usage in previous literature [ 3 , 6 , 16 ], and the term PTT is also adopted to avoid confusion.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Cuffless Continuous Blood Pressure Estimation Using Deep Learning Model

Harfiya

Purwandari

et al. 2020

Sensors

101

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Blood pressure monitoring is one avenue to monitor people’s health conditions. Early detection of abnormal blood pressure can help patients to get early treatment and reduce mortality associated with cardiovascular diseases. Therefore, it is very valuable to have a mechanism to perform real-time monitoring for blood pressure changes in patients. In this paper, we propose deep learning regression models using an electrocardiogram (ECG) and photoplethysmogram (PPG) for the real-time estimation of systolic blood pressure (SBP) and diastolic blood pressure (DBP) values. We use a bidirectional layer of long short-term memory (LSTM) as the first layer and add a residual connection inside each of the following layers of the LSTMs. We also perform experiments to compare the performance between the traditional machine learning methods, another existing deep learning model, and the proposed deep learning models using the dataset of Physionet’s multiparameter intelligent monitoring in intensive care II (MIMIC II) as the source of ECG and PPG signals as well as the arterial blood pressure (ABP) signal. The results show that the proposed model outperforms the existing methods and is able to achieve accurate estimation which is promising in order to be applied in clinical practice effectively.

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“…In such case, the time difference is referred to as pulse arrival time (PAT). Clinical accuracy is, however, questionable due to physiological variations in pre‐ejection period (from the R‐wave to aortic valve opening) . PTT and PAT have also been suggested to be used for the cuff‐less blood pressure monitoring (see, for example, previous studies), even though clinical reliability of such measurement is also questionable …”

Section: Introductionmentioning

confidence: 99%

“…Clinical accuracy is, however, questionable due to physiological variations in pre-ejection period (from the R-wave to aortic valve opening). 8,9 PTT and PAT have also been suggested to be used for the cuff-less blood pressure monitoring (see, for example, previous studies 7, 10,11 ), even though clinical reliability of such measurement is also questionable. 12 There are also many studies suggesting to use machine learning (ML) for predicting factors related to cardiac health.…”

Section: Introductionmentioning

confidence: 99%

Deep learning for prediction of cardiac indices from photoplethysmographic waveform: A virtual database approach

Huttunen

Kärkkäinen

Honkala

et al. 2020

Numer Methods Biomed Eng

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Deep learning methods combined with large datasets have recently shown significant progress in solving several medical tasks. However, collecting and annotating large datasets can be a very cumbersome and expensive task. We tackle these problems with a virtual database approach where training data is generated using computer simulations of related phenomena. Specifically, we concentrate on the following problem: can cardiovascular indices such as aortic elasticity, diastolic and systolic blood pressures, and blood flow from heart be predicted continuously using wearable photoplethysmographic sensors? We simulate the blood flow using a haemodynamic model consisting of the entire human circulation. Repeated evaluation of the simulator allows us to create a database of “virtual subjects” with size that is only limited by available computational resources. Using this database, we train neural networks to predict the cardiac indices from photoplethysmographic signal waveform. We consider two approaches: neural networks based on predefined input features and deep convolutional neural networks taking waveform directly as the input. The performance of the methods is demonstrated using numerical examples, thus carrying out a preliminary assessment of the approaches. The results show improvements in accuracy compared with the previous methods. The improvements are especially significant with indices related to aortic elasticity and maximum blood flow. The proposed approach would provide new means to measure cardiovascular health continuously, for example, with a simple wrist device.

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Pre-ejection period, the reason why the electrocardiogram Q-wave is an unreliable indicator of pulse wave initialization

Abstract: The overall analysis shows PAT is an unreliable measure of PTT and a poor surrogate under clinical interventions common in a critical care setting, due to intra- and inter- subject variability in PEP.

Cited by 26 publications

References 47 publications

Pulse transit time estimation of aortic pulse wave velocity and blood pressure using machine learning and simulated training data

Pulse transit time estimation of aortic pulse wave velocity and blood pressure using machine learning and simulated training data

Real-Time Cuffless Continuous Blood Pressure Estimation Using Deep Learning Model

Deep learning for prediction of cardiac indices from photoplethysmographic waveform: A virtual database approach

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