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

Brophy, Eoin; Vos, Maarten De; Boylan, Geraldine B.; Ward, Tomás E.

doi:10.3390/s21186311

Cited by 33 publications

(21 citation statements)

References 20 publications

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“…The method was evaluated on 18 subjects in a single center and yielded an MAE of 2.54 mmHg for SBP and 1.48 mmHg for DBP. The network proposed by Brophy et al [ 85 ] was based on the GAN framework, which mainly consists of a generator with two layers of LSTM and a discriminator with four layers of CNN. Notably, the model is different from previous models in its ability to generate continuous ABP based on the PPG signal, rather than directly producing two values of SBP and DBP.…”

Section: Methodsmentioning

confidence: 99%

Advances in Cuffless Continuous Blood Pressure Monitoring Technology Based on PPG Signals

Qin

Wang

et al. 2022

BioMed Research International

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Objective. To review the progress of research on photoplethysmography- (PPG-) based cuffless continuous blood pressure monitoring technologies and prospect the challenges that need to be addressed in the future. Methods. Using Web of Science and PubMed as search engines, the literature on cuffless continuous blood pressure studies using PPG signals in the recent five years were searched. Results. Based on the retrieved literature, this paper describes the available open datasets, commonly used signal preprocessing methods, and model evaluation criteria. Early researches employed multisite PPG signals to calculate pulse wave velocity or time and predicted blood pressure by a simple linear equation. Later, extensive researches were dedicated to mine the features of PPG signals related to blood pressure and regressed blood pressure by machine learning models. Most recently, many researches have emerged to experiment with complex deep learning models for blood pressure prediction with the raw PPG signal as input. Conclusion. This paper summarized the methods in the retrieved literature, provided insight into the artificial intelligence algorithms employed in the literature, and concluded with a discussion of the challenges and opportunities for the development of cuffless continuous blood pressure monitoring technologies.

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Section: Methodsmentioning

confidence: 99%

Advances in Cuffless Continuous Blood Pressure Monitoring Technology Based on PPG Signals

Qin

Wang

et al. 2022

BioMed Research International

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“…Apart from the CNN-based architecture, researchers also adopted generative models for the signal translation task. Inspired by CycleGAN, the authors of [ 144 ] proposed the T2T-GAN model. Their work is capable of bidirectional signal translation between PPG and ABP signals.…”

Section: Contact-based Bp Measurement From Ppg Signalsmentioning

confidence: 99%

“…They smoothed the PPG and ECG signals and discarded the unacceptable signals. It is freely available on both Kaggle (the hyperlink to the Kaggle dataset is (accessed on 15 September 2022)) and from the UCI Machine Learning Repository [ 144 ]. The third dataset is the UQVS dataset (the hyperlink to the UQVS dataset is (accessed on 15 September 2022).)…”

Section: Future Directionsmentioning

confidence: 99%

Blood Pressure Measurement: From Cuff-Based to Contactless Monitoring

et al. 2022

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Blood pressure (BP) determines whether a person has hypertension and offers implications as to whether he or she could be affected by cardiovascular disease. Cuff-based sphygmomanometers have traditionally provided both accuracy and reliability, but they require bulky equipment and relevant skills to obtain precise measurements. BP measurement from photoplethysmography (PPG) signals has become a promising alternative for convenient and unobtrusive BP monitoring. Moreover, the recent developments in remote photoplethysmography (rPPG) algorithms have enabled new innovations for contactless BP measurement. This paper illustrates the evolution of BP measurement techniques from the biophysical theory, through the development of contact-based BP measurement from PPG signals, and to the modern innovations of contactless BP measurement from rPPG signals. We consolidate knowledge from a diverse background of academic research to highlight the importance of multi-feature analysis for improving measurement accuracy. We conclude with the ongoing challenges, opportunities, and possible future directions in this emerging field of research.

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“…Brophy used the generative adversarial network to explore the relationship between arterial blood pressure (ABP) and photoplethysmogram (PPG), then learned the time series to time series generative adversarial network model that generates ABP with PPG (Brophy et al , 2021). As measuring ABP is expensive, they have produced significant results and seemingly similar results to DML.…”

Section: Federated Learning In Medical Applicationsmentioning

confidence: 99%

Open problems in medical federated learning

Yoo

Jeong

Lee

et al. 2022

IJWIS

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Purpose This study aims to summarize the critical issues in medical federated learning and applicable solutions. Also, detailed explanations of how federated learning techniques can be applied to the medical field are presented. About 80 reference studies described in the field were reviewed, and the federated learning framework currently being developed by the research team is provided. This paper will help researchers to build an actual medical federated learning environment. Design/methodology/approach Since machine learning techniques emerged, more efficient analysis was possible with a large amount of data. However, data regulations have been tightened worldwide, and the usage of centralized machine learning methods has become almost infeasible. Federated learning techniques have been introduced as a solution. Even with its powerful structural advantages, there still exist unsolved challenges in federated learning in a real medical data environment. This paper aims to summarize those by category and presents possible solutions. Findings This paper provides four critical categorized issues to be aware of when applying the federated learning technique to the actual medical data environment, then provides general guidelines for building a federated learning environment as a solution. Originality/value Existing studies have dealt with issues such as heterogeneity problems in the federated learning environment itself, but those were lacking on how these issues incur problems in actual working tasks. Therefore, this paper helps researchers understand the federated learning issues through examples of actual medical machine learning environments.

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

Cited by 33 publications

References 20 publications

Advances in Cuffless Continuous Blood Pressure Monitoring Technology Based on PPG Signals

Advances in Cuffless Continuous Blood Pressure Monitoring Technology Based on PPG Signals

Blood Pressure Measurement: From Cuff-Based to Contactless Monitoring

Open problems in medical federated learning

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