ECG Reconstruction via PPG: A Pilot Study

Zhu, Qiang; Tian, Xin; Wong, Chau-Wai; Wu, Min

doi:10.1109/bhi.2019.8834612

Cited by 29 publications

(37 citation statements)

References 10 publications

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“…Because of this close relationship, attempts have been made in previous studies to reconstruct ECG signals from the pulse wave signals of BF. [51] Although many external factors can affect the consistency between PRV and HRV, the physiological nature of BF and ECG signals leads to inevitable differences between them. One critical intrinsic factor is the pulse transit time, which is the time required for the blood to travel from the heart to the peripheral site where the BF is measured.…”

Section: 2mentioning

confidence: 99%

Dynamics of peripheral blood flow across sleep stages

Fan

Suzuki

Jiang

et al. 2021

Preprint

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Study ObjectivesPulse rate variability (PRV) derived from peripheral blood flow has been reported as a surrogate parameter for heart rate variability (HRV). However, there are currently no studies reporting systematic comparisons of PRV with HRV in a normal sleep state. Whether PRV can provide similar information regarding sleep stages remains unclear. Peripheral blood flow may also be modulated differently across sleep stages. Thus, we aimed to investigate blood flow dynamics and compare PRV with HRV across sleep stages to see if blood flow can provide further information about sleep stages.MethodsWe performed electrocardiography and simultaneously measured blood flow from the right index finger and ear concha of 45 healthy participants (13 women; mean age, 22.5 ± 3.4 years) during one night of sleep. Time-domain, frequency-domain, and non-linear indices of PRV/HRV, and time- and frequencydomain blood flow parameters were calculated.ResultsFinger-PRV results showed similar patterns to HRV results for most parameters. Finger-blood flow parameters in the time and frequency domains also showed information about the different sleep stages. Further, both finger- and ear-blood flow results showed 0.2–0.3 Hz oscillations that varied with sleep stages, with a significant increase in N3, suggesting a modulation (of respiration) within this frequency band.ConclusionsThese results suggest that PRV can provide as much information as HRV for different sleep stages. Furthermore, the results show that blood flow + PRV could be more advantageous than HRV alone in the assessment of the sleep state and related autonomic nervous activity.Statement of SignificanceThe research provides comprehensive information on peripheral blood flow (BF) activity across sleep stages for the first time, as the major novelty of our work. The second contribution is a systematic study comparing BF-derived pulse rate variability (PRV) with heart rate variability across sleep stages in a normal sleep state. We believe that our work makes a significant contribution to the literature because it provides comprehensive information on the potential of BF+PRV as a new biomarker for assessing the sleep state. Further, this study contributes to developing a more convenient method of assessing the sleep state in the clinical and home/work setting.

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Section: 2mentioning

confidence: 99%

Dynamics of peripheral blood flow across sleep stages

Fan

Suzuki

Jiang

et al. 2021

Preprint

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“…In many studies, Sen and Spe are frequently used. From (8) and (9), it is known that the high Sen is equivalent to the low leakage rate of the AF detector, and the high Spe is equivalent to a low…”

Section: B Model With Hyperparametersmentioning

confidence: 99%

“…In addition, PPG has the same source of excitation (heart) as ECG. Zhu et al [9] also studied the relationship between ECG and PPG in 2019 and derived ECG waveforms using PPG. These studies show a strong correlation between ECG and PPG.…”

Section: Introductionmentioning

confidence: 99%

Atrial Fibrillation Identification With PPG Signals Using a Combination of Time-Frequency Analysis and Deep Learning

Cheng

Chen

et al. 2020

IEEE Access

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Atrial fibrillation (AF) is the most common persistent arrhythmia and is likely to cause strokes and damage to heart function in patients. Electrocardiogram (ECG) is the gold standard for detecting AF. However, ECGs have short boards with short monitoring cycles and problems with gathering. It is also difficult to detect a burst AF through ECG. In contrast, photoplethysmography (PPG) is easy to perform and suitable for long-term monitoring. In this study, we propose a method that combines timefrequency analysis with deep learning and identifies AF based on PPG. The advantage of the method is that there is no need for the noise filtering and feature extraction of PPG, and it has a high generalization capability. The data for the experiment came from three publicly accessible databases. The first part of the experimental method uses data augmentation to convert the 10 s PPG segment into a time-frequency chromatograph by means of time-frequency analysis. The second part inputs the chromatograph into a hybrid framework that combines a convolutional neural network (CNN) and long short-term memory (LSTM) for AF/nonAF classification. The experimental results show that the method has a high classification accuracy, sensitivity, specificity, and F1 score, which are equal to 98.21%, 98.00%, 98.07% and 98.13%, respectively. The area under the receiver operating characteristic curve (AUC) is 0.9959. The model we propose not only aids doctors in diagnosing AF but also provides a method for identifying AF through portable wearable devices. INDEX TERMS Atrial fibrillation, photoplethysmography (PPG), time-frequency analysis, convolutional neural networks (CNN), long short-term memory (LSTM).

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“…The method achieved a 90% accuracy on a benchmark hospital dataset having clean PPG signals. Furthermore, the authors in [106] proposed a signal model to linearly reconstruct the entire ECG from PPG waveforms. The method reached a subject-wise accuracy of 98% in averaged correlation on a benchmark dataset of subjects with different ages and weights.…”

Section: Future Directionsmentioning

confidence: 99%

Continuous Non-Invasive Blood Pressure Monitoring: A Methodological Review on Measurement Techniques

Ellington

Lee

et al. 2020

IEEE Access

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Continuous measurement of blood pressure is crucial to the assessment of many medical conditions. However, the current clinical gold standard involving an arterial catheter, occluding cuff, and other invasive procedures are performed in hospital settings while home-based devices can provide only intermittent measurement and are not as reliable. Therefore, there is a significant need for continuous noninvasive blood pressure (cNIBP) monitoring in the daily life. Pulse transit time (PTT)/pulse arrival time (PAT)-based blood pressure measurement has proven its potential to address this need. In this article, we present state-of-the-art devices and recent literature related to measurement technologies used in PTT/PAT-based methods for cNIBP monitoring. Various physiological signals which could be used to enable cNIBP in the home setting are categorized into two groups (i.e., proximal waveforms and distal waveforms) and are thoroughly discussed and compared. Given insightful analysis of these waveforms, we highlight their combinations to derive PTT/PAT values for BP measurement then discuss challenges presented from the cuffless and PTT/PAT-based nature of these devices. Finally, we conclude with future directions needed for home-based cNIBP adaptation and present societal broader impacts.

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ECG Reconstruction via PPG: A Pilot Study

Cited by 29 publications

References 10 publications

Dynamics of peripheral blood flow across sleep stages

Dynamics of peripheral blood flow across sleep stages

Atrial Fibrillation Identification With PPG Signals Using a Combination of Time-Frequency Analysis and Deep Learning

Continuous Non-Invasive Blood Pressure Monitoring: A Methodological Review on Measurement Techniques

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