Assessment of deep learning based blood pressure prediction from PPG and rPPG signals

Schrumpf, Fabian; Frenzel, Patrick; Aust, Christoph; Osterhoff, Georg; Fuchs, Mirco

doi:10.1109/cvprw53098.2021.00423

Cited by 37 publications

(27 citation statements)

References 48 publications

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“…In particular, attempts to find meaningful information from PPG using various deep learning models are continuously increasing. Representative applications of PPG analysis using deep learning include heart rate estimation ( Biswas et al, 2019 ; Reiss et al, 2019 ; Panwar et al, 2020 ; Chang et al, 2021 ; Mehrgardt et al, 2021 ), cuff-less blood pressure estimation ( Panwar et al, 2020 ; El-Hajj and Kyriacou, 2021a , b ; Schrumpf et al, 2021a , b ; Tazarv and Levorato, 2021 ), and arterial fibrillation prediction ( Poh et al, 2018 ; Kwon et al, 2019 ; Aschbacher et al, 2020 ; Cheng et al, 2020 ; Pereira et al, 2020 ). In addition, PPG-based deep learning models are being used for respiratory rate estimation ( Ravichandran et al, 2019 ), sleep monitoring ( Korkalainen et al, 2020 ), diabetes ( Avram et al, 2019 ), vascular aging estimation ( Dall’Olio et al, 2020 ), and peripheral arterial disease classification ( Allen et al, 2021 ).…”

Section: Discussionmentioning

confidence: 99%

Photoplethysmogram Analysis and Applications: An Integrative Review

et al. 2022

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Beyond its use in a clinical environment, photoplethysmogram (PPG) is increasingly used for measuring the physiological state of an individual in daily life. This review aims to examine existing research on photoplethysmogram concerning its generation mechanisms, measurement principles, clinical applications, noise definition, pre-processing techniques, feature detection techniques, and post-processing techniques for photoplethysmogram processing, especially from an engineering point of view. We performed an extensive search with the PubMed, Google Scholar, Institute of Electrical and Electronics Engineers (IEEE), ScienceDirect, and Web of Science databases. Exclusion conditions did not include the year of publication, but articles not published in English were excluded. Based on 118 articles, we identified four main topics of enabling PPG: (A) PPG waveform, (B) PPG features and clinical applications including basic features based on the original PPG waveform, combined features of PPG, and derivative features of PPG, (C) PPG noise including motion artifact baseline wandering and hypoperfusion, and (D) PPG signal processing including PPG preprocessing, PPG peak detection, and signal quality index. The application field of photoplethysmogram has been extending from the clinical to the mobile environment. Although there is no standardized pre-processing pipeline for PPG signal processing, as PPG data are acquired and accumulated in various ways, the recently proposed machine learning-based method is expected to offer a promising solution.

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Section: Discussionmentioning

confidence: 99%

Photoplethysmogram Analysis and Applications: An Integrative Review

et al. 2022

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“…4) Transfer Learning: Transfer learning utilizes knowledge gained from one domain of application and applies it to another, similar domain [18]. In machine learning, it is common practice to employ pretrained weights that have been trained on a large-scale dataset and to fine-tune the model using a target dataset [19]. This approach allows the final model to fine tune itself more rapidly to a specific application when data is sparse in the final target dataset.…”

Section: Related Workmentioning

confidence: 99%

“…Instead of time-series data, the training samples were captured as images from signal plots and trained on a network pretrained with the ImageNet dataset. [19] on the other hand, created a feature extractor that could be multi-purposed to use either rPPG or PPG for blood pressure assessment.…”

Section: Related Workmentioning

confidence: 99%

RRWaveNet: A Compact End-to-End Multi-Scale Residual CNN for Robust PPG Respiratory Rate Estimation

Osathitporn¹,

Sawadwuthikul²,

Thuwajit³

et al. 2022

Preprint

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Respiratory rate (RR) is an important biomarker as RR changes can reflect severe medical events such as heart disease, lung disease, and sleep disorders. Unfortunately, however, standard manual RR counting is prone to human error and cannot be performed continuously. This study proposes a method for continuously estimating RR, RRWaveNet. The method is a compact end-to-end deep learning model which does not require feature engineering and can use low-cost raw photoplethysmography (PPG) as input signal. RRWaveNet was tested subject-independently and compared to baseline in three datasets (BIDMC, CapnoBase, and WESAD) and using three window sizes (16, 32, and 64 seconds). RRWaveNet outperformed current state-ofthe-art methods with mean absolute errors at optimal window size of 1.66 ± 1.01, 1.59 ± 1.08, and 1.92 ± 0.96 breaths per minute for each dataset. In remote monitoring settings, such as in the WESAD dataset, we apply transfer learning to two other ICU datasets, reducing the MAE to 1.52 ± 0.50 breaths per minute, showing this model allows accurate and practical estimation of RR on affordable and wearable devices. Our study shows feasibility of remote RR monitoring in the context of telemedicine and at home.

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“…Also, it can be remotely estimated by a camera providing a remote-PPG (rPPG) signal. Whether being contact or remote PPG signal, it has been proved that PPG signal can be exploited for estimating important physiological vital signs such as blood pressure (BP) [3][4][5][6][7][8][9][10] oxygen saturation [11], and hemoglobin levels [12]. Even the multi-electrode electrocardiogram (ECG) signal, can be inferred via PPG signals [13].…”

Section: Introductionmentioning

confidence: 99%

“…These techniques guarantee the intended simplicity of non-clinical monitoring of health status. Thanks to the public availability of biomedical datasets [14][15][16] that enable training deep VOLUME XX, 2022 learning models [6]for inferring the relation between these data and the corresponding labeled physiological parameters. However, these huge datasets need to be cleaned effectively before any further deployment.…”

Section: Introductionmentioning

confidence: 99%

Beat-Based PPG-ABP Cleaning Technique for Blood Pressure Estimation

et al. 2022

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This work is supported in part by the Information Technology Industry Development Agency (ITIDA) under number ARP2019.R27.4.ABSTRACTA growing attention is given to exploiting Photoplethysmography (PPG) signals in noninvasively measuring many physiological vital signs. Many machine deep learning models were trained for predicting the continuous arterial blood pressure (ABP) or just the systolic and diastolic blood pressure (BP) values based on a public database. However, jointly cleaning the PPG-ABP dataset that is the most critical pre-processing step for training quality is still in need for more investigations. There is a considerable amount of anomaly data that has to be excluded before any training stage. This paper introduces a two-level joint PPG-ABP cleaning technique conducted at a signal level and per-beat level. Many quality metrics have been checked successively for excluding improper data. These metrics achieve a coarse cleaning step. Finally, principal component analysis (PCA) is exploited for fine cleaning the remaining data from the former stage. The cleaning efficiency is evaluated by measuring its impact on the deep-learning-based BP estimation models. The trained model based on our cleaned data shows performance enhancement in terms of prediction error and the correlation between the predicted and ground-truth BP. Segmented and cleaned PPG/ABP dataset will be publicly available at both signal level and beat level. Based on the simulation results, the proposed cleaning technique enhances the standard deviation of the prediction error of systolic and diastolic blood pressure by 11.68 % and 10.81 %, respectively. Also, it enhances the mean absolute error of the prediction of systolic and diastolic blood pressure by 14.79 % and 11.70 %, respectively.

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Assessment of deep learning based blood pressure prediction from PPG and rPPG signals

Cited by 37 publications

References 48 publications

Photoplethysmogram Analysis and Applications: An Integrative Review

Photoplethysmogram Analysis and Applications: An Integrative Review

RRWaveNet: A Compact End-to-End Multi-Scale Residual CNN for Robust PPG Respiratory Rate Estimation

Beat-Based PPG-ABP Cleaning Technique for Blood Pressure Estimation

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