Continuous Blood Pressure Estimation Based on Multi-Scale Feature Extraction by the Neural Network With Multi-Task Learning

Jiang, Hengbing; Zou, Lili; Huang, Dequn; Feng, Qianjin

doi:10.3389/fnins.2022.883693

Cited by 8 publications

(6 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some were deployed in continuous BP measurement and showed improvement in both accuracy and stability ( Radha et al, 2019 ; El-Hajj and Kyriacou, 2021 ; Yen et al, 2021 ). Most of them used time-dependent information, such as the long- and short-term memory (LSTM) network ( Monte-Moreno, 2011 ; Radha et al, 2019 ; Harfiya et al, 2021 ; Li et al, 2021 ; Pu et al, 2021 ; Wang et al, 2021 ; Ali and Atef, 2022 ; Meng et al, 2022 ), system identification ( Allen and Murray, 1999 ), auto-regression ( Acciaroli, 2018 ), multi-stage feature extraction ( Ali and Atef, 2022 ; Jiang et al, 2022 ), dynamic compliance ( Gupta et al, 2022 ), or simple heart rate variability(HRV) ( Mejía-Mejía et al, 2022 ). These algorithms performed better than those without dynamic features ( Radha et al, 2019 ; Harfiya et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Temporal complexity in photoplethysmography and its influence on blood pressure

Xing,

Huang,

Hao

et al. 2023

Front. Physiol.

View full text Add to dashboard Cite

Objective: The temporal complexity of photoplethysmography (PPG) provides valuable information about blood pressure (BP). In this study, we aim to interpret the stochastic PPG patterns with a model-based simulation, which may help optimize the BP estimation algorithms.Methods: The classic four-element Windkessel model is adapted in this study to incorporate BP-dependent compliance profiles. Simulations are performed to generate PPG responses to pulse and continuous stimuli at various timescales, aiming to mimic sudden or gradual hemodynamic changes observed in real-life scenarios. To quantify the temporal complexity of PPG, we utilize the Higuchi fractal dimension (HFD) and autocorrelation function (ACF). These measures provide insights into the intricate temporal patterns exhibited by PPG. To validate the simulation results, continuous recordings of BP, PPG, and stroke volume from 40 healthy subjects were used.Results: Pulse simulations showed that central vascular compliance variation during a cardiac cycle, peripheral resistance, and cardiac output (CO) collectively contributed to the time delay, amplitude overshoot, and phase shift of PPG responses. Continuous simulations showed that the PPG complexity could be generated by random stimuli, which were subsequently influenced by the autocorrelation patterns of the stimuli. Importantly, the relationship between complexity and hemodynamics as predicted by our model aligned well with the experimental analysis. HFD and ACF had significant contributions to BP, displaying stability even in the presence of high CO fluctuations. In contrast, morphological features exhibited reduced contribution in unstable hemodynamic conditions.Conclusion: Temporal complexity patterns are essential to single-site PPG-based BP estimation. Understanding the physiological implications of these patterns can aid in the development of algorithms with clear interpretability and optimal structures.

show abstract

Section: Introductionmentioning

confidence: 99%

Temporal complexity in photoplethysmography and its influence on blood pressure

Xing,

Huang,

Hao

et al. 2023

Front. Physiol.

View full text Add to dashboard Cite

show abstract

“…Several previous studies used a discrete wavelet decomposition (DWT) filter to minimize PPG signal artifacts and baseline drift [19][20][21]. A one-dimensional (1D) DWT decomposition divides a signal into two frequency components: the low-frequency (LPF) component and the high-frequency (HPF) component.…”

Section: Preprocessingmentioning

confidence: 99%

PPG Signals-Based Blood-Pressure Estimation Using Grid Search in Hyperparameter Optimization of CNN–LSTM

Fuadah

Jeong

et al. 2023

Diagnostics

View full text Add to dashboard Cite

Researchers commonly use continuous noninvasive blood-pressure measurement (cNIBP) based on photoplethysmography (PPG) signals to monitor blood pressure conveniently. However, the performance of the system still needs to be improved. Accuracy and precision in blood-pressure measurements are critical factors in diagnosing and managing patients’ health conditions. Therefore, we propose a convolutional long short-term memory neural network (CNN–LSTM) with grid search ability, which provides a robust blood-pressure estimation system by extracting meaningful information from PPG signals and reducing the complexity of hyperparameter optimization in the proposed model. The multiparameter intelligent monitoring for intensive care III (MIMIC III) dataset obtained PPG and arterial-blood-pressure (ABP) signals. We obtained 75,226 signal segments, with 60,180 signals allocated for training data, 12,030 signals allocated for the validation set, and 15,045 signals allocated for the test data. During training, we applied five-fold cross-validation with a grid-search method to select the best model and determine the optimal hyperparameter settings. The optimized configuration of the CNN–LSTM layers consisted of five convolutional layers, one long short-term memory (LSTM) layer, and two fully connected layers for blood-pressure estimation. This study successfully achieved good accuracy in assessing both systolic blood pressure (SBP) and diastolic blood pressure (DBP) by calculating the standard deviation (SD) and the mean absolute error (MAE), resulting in values of 7.89 ± 3.79 and 5.34 ± 2.89 mmHg, respectively. The optimal configuration of the CNN–LSTM provided satisfactory performance according to the standards set by the British Hypertension Society (BHS), the Association for the Advancement of Medical Instrumentation (AAMI), and the Institute of Electrical and Electronics Engineers (IEEE) for blood-pressure monitoring devices.

show abstract

“…Some were deployed in continuous BP measurement and showed improvement in both accuracy and stability (Radha et al, 2019;Yen et al, 2021). Most of them used time-dependent information, such as the long-and short-term memory (LSTM) network (Monte-Moreno, 2011;Radha et al, 2019;Harfiya et al, 2021;Li et al, 2021;Pu et al, 2021;Wang et al, 2021;Ali and Atef, 2022;Meng et al, 2022), system identification Murray, 1999), auto-regression (Acciaroli, 2018), multi-stage feature extraction (Ali and Atef, 2022;Jiang et al, 2022), dynamic compliance (Gupta et al, 2022), or simple heart rate variability(HRV) . These algorithms performed better than those without dynamic features (Radha et al, 2019;Harfiya et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Advances in Basic and Applied Research in Photoplethysmography

2024

Frontiers Research Topics

View full text Add to dashboard Cite

Frontiers is more than just an open access publisher of scholarly articles: it is a pioneering approach to the world of academia, radically improving the way scholarly research is managed. The grand vision of Frontiers is a world where all people have an equal opportunity to seek, share and generate knowledge. Frontiers provides immediate and permanent online open access to all its publications, but this alone is not enough to realize our grand goals. Frontiers journal seriesThe Frontiers journal series is a multi-tier and interdisciplinary set of openaccess, online journals, promising a paradigm shift from the current review, selection and dissemination processes in academic publishing. All Frontiers journals are driven by researchers for researchers; therefore, they constitute a service to the scholarly community. At the same time, the Frontiers journal series operates on a revolutionary invention, the tiered publishing system, initially addressing specific communities of scholars, and gradually climbing up to broader public understanding, thus serving the interests of the lay society, too. Dedication to qualityEach Frontiers article is a landmark of the highest quality, thanks to genuinely collaborative interactions between authors and review editors, who include some of the world's best academicians. Research must be certified by peers before entering a stream of knowledge that may eventually reach the public -and shape society; therefore, Frontiers only applies the most rigorous and unbiased reviews. Frontiers revolutionizes research publishing by freely delivering the most outstanding research, evaluated with no bias from both the academic and social point of view. By applying the most advanced information technologies, Frontiers is catapulting scholarly publishing into a new generation. What are Frontiers Research Topics?Frontiers Research Topics are very popular trademarks of the Frontiers journals series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area.

show abstract

Continuous Blood Pressure Estimation Based on Multi-Scale Feature Extraction by the Neural Network With Multi-Task Learning

Cited by 8 publications

References 45 publications

Temporal complexity in photoplethysmography and its influence on blood pressure

Temporal complexity in photoplethysmography and its influence on blood pressure

PPG Signals-Based Blood-Pressure Estimation Using Grid Search in Hyperparameter Optimization of CNN–LSTM

Advances in Basic and Applied Research in Photoplethysmography

Contact Info

Product

Resources

About