PPG Sensor Contact Pressure Should Be Taken Into Account for Cuff-Less Blood Pressure Measurement

Chandrasekhar, Anand; Yavarimanesh, Mohammad; Natarajan, Keerthana; Hahn, Jin‐Oh; Mukkamala, Ramakrishna

doi:10.1109/tbme.2020.2976989

Cited by 77 publications

(46 citation statements)

References 25 publications

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“…This finding is not surprising, because the time delay between the foot and ensuing peak of the PPG waveform is largely determined by ventricular properties. In a recent study, we showed that PPG sensor contact pressure, which was controlled in the present study, impacts finger PAT detected via the PPG waveform peak twice as much as finger PAT detected via the PPG waveform foot 15 . Taken together, the pair of studies indicate that PTT should be detected specifically via the feet of PPG waveforms.…”

Section: Discussionsupporting

confidence: 46%

Conventional pulse transit times as markers of blood pressure changes in humans

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Yavarimanesh

Natarajan

et al. 2020

Sci Rep

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Pulse transit time (PTT) represents a potential approach for cuff-less blood pressure (BP) monitoring. Conventionally, PTT is determined by (1) measuring (a) ECG and ear, finger, or toe PPG waveforms or (b) two of these PPG waveforms and (2) detecting the time delay between the waveforms. The conventional PTTs (cPTTs) were compared in terms of correlation with BP in humans. Thirty-two volunteers [50% female; 52 (17) (mean (SD)) years; 25% hypertensive] were studied. The four waveforms and manual cuff BP were recorded before and after slow breathing, mental arithmetic, cold pressor, and sublingual nitroglycerin. Six cPTTs were detected as the time delays between the ECG R-wave and ear PPG foot, R-wave and finger PPG foot [finger pulse arrival time (PAT)], R-wave and toe PPG foot (toe PAT), ear and finger PPG feet, ear and toe PPG feet, and finger and toe PPG feet. These time delays were also detected via PPG peaks. The best correlation by a substantial extent was between toe PAT via the PPG foot and systolic BP [− 0.63 ± 0.05 (mean ± SE); p < 0.001 via one-way ANOVA]. Toe PAT is superior to other cPTTs including the popular finger PAT as a marker of changes in BP and systolic BP in particular.

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Section: Discussionsupporting

confidence: 46%

Conventional pulse transit times as markers of blood pressure changes in humans

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Yavarimanesh

Natarajan

et al. 2020

Sci Rep

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“…Note that we did not employ any hyperparameter tuning which might further improve our results, but certainly only on a gradual scale. In contrast, other authors who did not explicitly mention a subject-based dataset split reported substantially lower prediction errors [33][34][35]62].…”

Section: Discussionmentioning

confidence: 94%

“…The variability of the pulse morphology among subjects, e.g., caused by age and cardiovascular diseases, certainly affects the association of the signal shape to a particular BP. The equipment in a hospital setting differs as well and, more importantly, the contact pressure of a PPG sensor can affect the pulse morphology [ 33 , 34 , 35 ]. Therefore the ability of the learning algorithm to generalize well may be impaired.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Non-Invasive Blood Pressure Prediction from PPG and rPPG Signals Using Deep Learning

Schrumpf

Frenzel

Aust

et al. 2021

Sensors

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Exploiting photoplethysmography signals (PPG) for non-invasive blood pressure (BP) measurement is interesting for various reasons. First, PPG can easily be measured using fingerclip sensors. Second, camera based approaches allow to derive remote PPG (rPPG) signals similar to PPG and therefore provide the opportunity for non-invasive measurements of BP. Various methods relying on machine learning techniques have recently been published. Performances are often reported as the mean average error (MAE) on the data which is problematic. This work aims to analyze the PPG- and rPPG based BP prediction error with respect to the underlying data distribution. First, we train established neural network (NN) architectures and derive an appropriate parameterization of input segments drawn from continuous PPG signals. Second, we use this parameterization to train NNs with a larger PPG dataset and carry out a systematic evaluation of the predicted blood pressure. The analysis revealed a strong systematic increase of the prediction error towards less frequent BP values across NN architectures. Moreover, we tested different train/test set split configurations which underpin the importance of a careful subject-aware dataset assignment to prevent overly optimistic results. Third, we use transfer learning to train the NNs for rPPG based BP prediction. The resulting performances are similar to the PPG-only case. Finally, we apply different personalization techniques and retrain our NNs with subject-specific data for both the PPG-only and rPPG case. Whilst the particular technique is less important, personalization reduces the prediction errors significantly.

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“…Constant monitoring of blood pressure is important for many patients, especially those with a family history of hypertension. In recent years, cuffless blood pressure [1][2][3][4][5][6][7] has become a more popular method to monitor cardiovascular health and many of them use the optical technique, photoplethysmography (PPG). The PPG technique can be found in many commercial health monitoring devices [8] because it is inexpensive and simple to use.…”

Section: Introductionmentioning

confidence: 99%

External factors that affect the photoplethysmography waveforms

2021

SN Appl. Sci.

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Photoplethysmography (PPG) is a simple and inexpensive technology used in many smart devices to monitor cardiovascular health. The PPG sensors use LED lights to penetrate into the bloodstream to detect the different blood volume changes in the tissue through skin contact by sensing the amount of light that hits the sensor. Typically, the data are displayed on a graph and it forms the pulse waveform. The information from the produced pulse waveform can be useful in calculating measurements that help monitor cardiovascular health, such as blood pressure. With many more people beginning to monitor their health status on their smart devices, it is extremely important that the PPG signal is accurate. Designing a simple experiment with standard laboratory equipment and commercial sensors, we wanted to find how external factors influence the results. In this study, it was found that external factors, touch force and temperature, can have a large impact on the resulting waveform, so the effects of those factors need to be considered in order for the information to become more reliable.

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PPG Sensor Contact Pressure Should Be Taken Into Account for Cuff-Less Blood Pressure Measurement

Cited by 77 publications

References 25 publications

Conventional pulse transit times as markers of blood pressure changes in humans

Conventional pulse transit times as markers of blood pressure changes in humans

Assessment of Non-Invasive Blood Pressure Prediction from PPG and rPPG Signals Using Deep Learning

External factors that affect the photoplethysmography waveforms

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