Quickly Convert Photoplethysmography to Electrocardiogram Signals by a Banded Kernel Ensemble Learning Method for Heart Diseases Detection

Ho, Wen-Hsien; Liao, C.-Y.; Chen, Yenming J.; Hwang, Kao-Shing; Tao, Yanyun

doi:10.1109/access.2022.3173176

Cited by 7 publications

(3 citation statements)

References 52 publications

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“…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%

“…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. In [17], the authors proposed a banded kernel ensemble method to convert low-quality sources (PPG) into high-quality targets (ECG). Unlike the solutions based on neural networks, this algorithm does not impose any computational burden in the transformation task after obtaining the trained model.…”

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: 99%

Section: Introductionmentioning

confidence: 99%

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

Guo,

Tang,

et al. 2024

Bioengineering

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“…But they are highly prone to infections. Thus cuff-less, non-invasive and continuous BP measurement system is essential [9]. At this juncture, Photoplethysmography (PPG) is an innovative option used for continuous monitoring of BP without the need for inflatable cuff.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Resnet and Bidirectional LSTM-Based Deep Learning Model for Cardiovascular Disease Detection Using PPG Signals

Balaraman,

S.P.

2023

JMC

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Hypertension is the major root cause of blood pressure (BP) which in turn causes different cardiovascular diseases (CVDs). Hence BP need to be regularly monitored for preventing CVDs since it can be diagnosed and controlled through constant observation. Photoplethysmography (PPG) is identified as an important low-cost technology for facilitating a convenient and effective process in the early detection of CVDs. Different cardiovascular parameters such as blood oxygen saturation, heart rate, blood pressure, etc can be determined using the PPG technology. These cardiovascular parameters when given as input to the deep learning model is determined to diagnosis CVDs with maximized accuracy to an expected level. In this paper, Hybrid ResNet and Bidirectional LSTM-based Deep Learning Model (HRBLDLM) is proposed for diagnosing CVDs from PPG signals with due help in supporting the physicians during the process of continuous monitoring. This deep learning model mainly concentrated on the diagnosis of stage 1 hypertension, stage 2 hypertension, prehypertension, and normal CVDs with maximized accuracy using PPG signals. The PPG signals determined from PPG-BP dataset for investigation were recorded using IoT-based wearable patient monitoring (WPM) devices during the physical activity that includes high intensity, medium and low intensity movements involved driving, sitting and walking. The experiments conducted for this proposed deep learning model using PPG-BP dataset confirmed a better classification accuracy of 99.62% on par with the baseline PPG-based deep learning models contributed for detecting CVDs.

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Deep Learning-Based Ensemble Model for Detection of Myocardial Infarction from ECG Data

Saha,

Mukherjee

et al. 2024

Lecture Notes in Networks and Systems

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Quickly Convert Photoplethysmography to Electrocardiogram Signals by a Banded Kernel Ensemble Learning Method for Heart Diseases Detection

Cited by 7 publications

References 52 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

Hybrid Resnet and Bidirectional LSTM-Based Deep Learning Model for Cardiovascular Disease Detection Using PPG Signals

Deep Learning-Based Ensemble Model for Detection of Myocardial Infarction from ECG Data

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