P2e-Wgan

Vo, Khuong; Naeini, Emad Kasaeyan; Naderi, Amir Mohammad; Jilani, Daniel; Rahmani, Amir M.; Dutt, Nikil; Cao, Hung

doi:10.1145/3412841.3441979

Cited by 19 publications

(13 citation statements)

References 10 publications

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“…The models proposed in the first two studies targeted specific subjects and could not be generalized to multiple subjects, representing a limitation. In [13], the correlation coefficient between the reference and reconstructed electrocardiogram was only 0.835. In [14][15][16], the authors used other evaluation metrics to verify the model performance and did not calculate the correlation coefficient between the reference and reconstructed electrocardiograms.…”

Section: Introductionmentioning

confidence: 95%

“…The accuracy of these methods depends on the accuracy of the R wave in ECG and contraction seam extraction algorithms in PPG, which can reduce the accuracy of ECG reconstruction. The computational parametric model [8], lightweight neural network [9], deep learning models based on encoder-decoder [10], BiLSTM [11], PPG2ECGps [12], P2E-WGAN [13], CardioGAN [14], Performer [15], transformed attentional neural network [16], and banded kernel ensemble method [17] have been proposed for reconstructing electrocardiograms from PPG based on deep learning methods. In [8], the author proposed a computational parametric model that extracts features from PPG to predict ECG parameters.…”

Section: Introductionmentioning

confidence: 99%

“…However, cycle alignment and segmentation result in loss of temporal information, such as pulse transit time and heart rate variability, which are essential clinical factors. Some studies [11][12][13][14][15][16] used segment reconstruction of ECG signals from PPG as a basis. The models proposed in the first two studies targeted specific subjects and could not be generalized to multiple subjects, representing a limitation.…”

Section: Introductionmentioning

confidence: 99%

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Reconstruction of Missing Electrocardiography Signals from Photoplethysmography Data Using Deep Neural Network

Guo,

Tang,

et al. 2024

Bioengineering

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ECG helps in diagnosing heart disease by recording heart activity. During long-term measurements, data loss occurs due to sensor detachment. Therefore, research into the reconstruction of missing ECG data is essential. However, ECG requires user participation and cannot be used for continuous heart monitoring. Continuous monitoring of PPG signals is conversely low-cost and easy to carry out. In this study, a deep neural network model is proposed for the reconstruction of missing ECG signals using PPG data. This model is an end-to-end deep learning neural network utilizing WNet architecture as a basis, on which a bidirectional long short-term memory network is added in establishing a second model. The performance of both models is verified using 146 records from the MIMIC III matched subset. Compared with the reference, the ECG reconstructed using the proposed model has a Pearson’s correlation coefficient of 0.851, root mean square error (RMSE) of 0.075, percentage root mean square difference (PRD) of 5.452, and a Fréchet distance (FD) of 0.302. The experimental results demonstrate that it is feasible to reconstruct missing ECG signals from PPG.

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Section: Introductionmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Reconstruction of Missing Electrocardiography Signals from Photoplethysmography Data Using Deep Neural Network

Guo,

Tang,

et al. 2024

Bioengineering

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“…46 passes the PPG signal acquired from a pulse oximeter through a block that computes the discrete cosine transform, followed by ridge regression, in order to get the single-lead ECG signal. 47 also attempts to translate a PPG signal (acquired by a pulse oximeter) to a single-lead ECG signal, but by using a conditional generative adversarial network (c-GAN).…”

Section: Single-lead Ecg Reconstructionmentioning

confidence: 99%

Your smartphone could act as a pulse-oximeter and as a single-lead ECG

Mehmood,

Sarouji,

Rahman

et al. 2023

Preprint

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In the post-covid19 era, every new wave of the pandemic causes an increased concern/interest among the masses to learnmore about their state of well-being. Therefore, it is the need of the hour to come up with ubiquitous, low-cost, non-invasivetools for rapid and continuous monitoring of body vitals that reflect the status of one’s overall health. In this backdrop, this workproposes a deep learning approach to turn a smartphone—the popular hand-held personal gadget—into a diagnostic tool tomeasure/monitor the three most important body vitals, i.e., pulse rate (PR), blood oxygen saturation level (aka SpO2), andrespiratory rate (RR). Furthermore, we propose another method that could extract a single-lead electrocardiograph (ECG)of the subject. The proposed methods include the following core steps: subject records a small video of his/her fingertip byplacing his/her finger on the rear camera of the smartphone, and the recorded video is pre-processed to extract the filteredand/or detrended video-photoplethysmography (vPPG) signal, which is then fed to custom-built convolutional neural networks(CNN), which eventually spit-out the vitals (PR, SpO2, and RR) as well as a single-lead ECG of the subject. To be precise,the contribution of this paper is two-fold: 1) estimation of the three body vitals (PR, SpO2, RR) from the vPPG data usingcustom-built CNNs, vision transformer, and most importantly by CLIP model (a popular image-caption-generator model); 2) anovel discrete cosine transform+feedforward neural network-based method that translates the recorded video-PPG signal to asingle-lead ECG signal. The significance of this work is two-fold: i) it allows rapid self-testing of body vitals (e.g., self-monitoringfor covid19 symptoms), ii) it enables rapid self-acquisition of a single-lead ECG, and thus allows early detection of atrialfibrillation (abormal heart beat or arrhythmia), which in turn could enable early intervention in response to a range of cardiovascular diseases,and could help save many precious lives. Our work could help reduce the burden on healthcare facilities and could lead toreduction in health insurance costs.

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“…Furthermore, while the RR intervals in the ECG were highly correlated with the onset-to-onset interval in the PPG [ 7 ], they were not the same, and certain diseases could make the RR interval differ from the systolic peak-to-systolic peak interval [ 21 ]. Three studies [ 22 , 23 , 24 ] using deep neural networks to reconstruct ECGs from PPGs did not require alignment steps in preprocessing. Two studies focused on the heart rate destination without emphasizing the quality of the ECG waveform, and one study [ 23 ] was not a subject-specific model.…”

Section: Introductionmentioning

confidence: 99%

PPG2ECGps: An End-to-End Subject-Specific Deep Neural Network Model for Electrocardiogram Reconstruction from Photoplethysmography Signals without Pulse Arrival Time Adjustments

et al. 2023

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Electrocardiograms (ECGs) provide crucial information for evaluating a patient’s cardiovascular health; however, they are not always easily accessible. Photoplethysmography (PPG), a technology commonly used in wearable devices such as smartwatches, has shown promise for constructing ECGs. Several methods have been proposed for ECG reconstruction using PPG signals, but some require signal alignment during the training phase, which is not feasible in real-life settings where ECG signals are not collected at the same time as PPG signals. To address this challenge, we introduce PPG2ECGps, an end-to-end, patient-specific deep-learning neural network utilizing the W-Net architecture. This novel approach enables direct ECG signal reconstruction from PPG signals, eliminating the need for signal alignment. Our experiments show that the proposed model achieves mean values of 0.977 mV for Pearson’s correlation coefficient, 0.037 mV for the root mean square error, and 0.010 mV for the normalized dynamic time-warped distance when comparing reconstructed ECGs to reference ECGs from a dataset of 500 records. As PPG signals are more accessible than ECG signals, our proposed model has significant potential to improve patient monitoring and diagnosis in healthcare settings via wearable devices.

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P2e-Wgan

Cited by 19 publications

References 10 publications

Reconstruction of Missing Electrocardiography Signals from Photoplethysmography Data Using Deep Neural Network

Reconstruction of Missing Electrocardiography Signals from Photoplethysmography Data Using Deep Neural Network

Your smartphone could act as a pulse-oximeter and as a single-lead ECG

PPG2ECGps: An End-to-End Subject-Specific Deep Neural Network Model for Electrocardiogram Reconstruction from Photoplethysmography Signals without Pulse Arrival Time Adjustments

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