PP-Net: A Deep Learning Framework for PPG-Based Blood Pressure and Heart Rate Estimation

Panwar, Madhuri; Gautam, Arvind; Biswas, Dwaipayan; Acharyya, Amit

doi:10.1109/jsen.2020.2990864

Cited by 141 publications

(79 citation statements)

References 45 publications

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“…Other works develop a statistical feature extraction and selection process followed by a regression-based predictive model, all of which achieve high-quality BP estimation results from PPG data only [ 9 ]. Feature-free methods of BP estimation have also been completed previously through the use of deep-learning-based prediction techniques with good results [ 10 , 11 ]. However, these methods discussed thus far deal with BP prediction in discrete intervals, and we build on this through the generation of continuous BP waveforms and BP prediction.…”

Section: Related Workmentioning

confidence: 99%

Estimation of Continuous Blood Pressure from PPG via a Federated Learning Approach

Brophy

Vos

Boylan

et al. 2021

Sensors

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Ischemic heart disease is the highest cause of mortality globally each year. This puts a massive strain not only on the lives of those affected, but also on the public healthcare systems. To understand the dynamics of the healthy and unhealthy heart, doctors commonly use an electrocardiogram (ECG) and blood pressure (BP) readings. These methods are often quite invasive, particularly when continuous arterial blood pressure (ABP) readings are taken, and not to mention very costly. Using machine learning methods, we develop a framework capable of inferring ABP from a single optical photoplethysmogram (PPG) sensor alone. We train our framework across distributed models and data sources to mimic a large-scale distributed collaborative learning experiment that could be implemented across low-cost wearables. Our time-series-to-time-series generative adversarial network (T2TGAN) is capable of high-quality continuous ABP generation from a PPG signal with a mean error of 2.95 mmHg and a standard deviation of 19.33 mmHg when estimating mean arterial pressure on a previously unseen, noisy, independent dataset. To our knowledge, this framework is the first example of a GAN capable of continuous ABP generation from an input PPG signal that also uses a federated learning methodology.

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Section: Related Workmentioning

confidence: 99%

Estimation of Continuous Blood Pressure from PPG via a Federated Learning Approach

Brophy

Vos

Boylan

et al. 2021

Sensors

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“…There are survey papers covering different PPG applications that involve the use of wearable devices [ 20 , 21 ], atrial fibrillation detection [ 22 ], and blood pressure monitoring [ 23 ]. Papers have also been published which used deep learning for contact-based PPG, e.g., [ 24 , 25 , 26 , 27 ]. The previous survey papers on contact-based PPG methods are listed in Table 1 .…”

Section: Introductionmentioning

confidence: 99%

A Review of Deep Learning-Based Contactless Heart Rate Measurement Methods

Azarang

Kehtarnavaz

2021

Sensors

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The interest in contactless or remote heart rate measurement has been steadily growing in healthcare and sports applications. Contactless methods involve the utilization of a video camera and image processing algorithms. Recently, deep learning methods have been used to improve the performance of conventional contactless methods for heart rate measurement. After providing a review of the related literature, a comparison of the deep learning methods whose codes are publicly available is conducted in this paper. The public domain UBFC dataset is used to compare the performance of these deep learning methods for heart rate measurement. The results obtained show that the deep learning method PhysNet generates the best heart rate measurement outcome among these methods, with a mean absolute error value of 2.57 beats per minute and a mean square error value of 7.56 beats per minute.

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“…PPG is used in various systems ranging from smartphones to pulse oximeters, and has recently become more ubiquitous due to the expansion of the wearable devices industry [1][2][3][4] and growing number of healthcare applications in cardiovascular monitoring [5][6][7]. In conjunction with the explosion in deep neural network based biosignal processing techniques, recent studies have shown promising results on cardiac function analyses using PPG [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

“…Since 2016, studies on blood pressure estimation [9,11,12], biometric identification [13,14], and atrial fibrillation detection [15][16][17][18] from PPG signals using deep learning has become popular. Some of these studies were able to use readily available public databases for training the deep learning models [19][20][21], but others required conducting large-scale experiments to produce the necessary data [15,22,23].…”

Section: Introductionmentioning

confidence: 99%

Complementary Photoplethysmogram Synthesis From Electrocardiogram Using Generative Adversarial Network

Shin

Sun

Lee³

et al. 2021

IEEE Access

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Photoplethysmogram (PPG) is one of the most widely measured biosignals alongside electrocardiogram (ECG). Due to the simplicity of measurement and the advent of wearable devices, there have been growing interest in using PPG for a variety of healthcare applications such as cardiac function estimation. However, unlike ECG, there are not many large databases available for clinically significant analyses of PPG. To overcome this issue, a Generative Adversarial Network-based model to generate PPG using ECG as input is proposed. The network was trained using a large open database of biosignals measured from surgical patients and was externally validated using an alternative database sourced from another hospital. The generated PPG was compared with the reference PPG using percent root mean square difference (PRD) and Pearson correlation coefficient to evaluate the morphological similarity. Additionally, heart rate measured from the reference ECG, reference PPG, and generated PPG, and compared through repeated measure analysis of variance to test for any significant differences. The mean PRD was 3210% and the mean correlation coefficient was 0.950.05 in the test dataset. The HR from the three biosignals showed no significant difference with a p-value of 0.473. When the optimized GAN model was tested on atrial fibrillation ECG from a third dataset, the mean correlation coefficient between the generated PPG heart rate and the ECG heart rate was 0.940.15, with paired t-test resulting in p-value of 0.64. The results indicate that the proposed method may provide a valuable alternative to augmenting biosignal databases that are abundant in one signal while lacking in another.

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PP-Net: A Deep Learning Framework for PPG-Based Blood Pressure and Heart Rate Estimation

Cited by 141 publications

References 45 publications

Estimation of Continuous Blood Pressure from PPG via a Federated Learning Approach

Estimation of Continuous Blood Pressure from PPG via a Federated Learning Approach

A Review of Deep Learning-Based Contactless Heart Rate Measurement Methods

Complementary Photoplethysmogram Synthesis From Electrocardiogram Using Generative Adversarial Network

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