Combined Deep CNN–LSTM Network-based Multitasking Learning Architecture for Noninvasive Continuous Blood Pressure Estimation using Difference in ECG-PPG Features

Jeong, Da Un; Lim, Ki Moo

doi:10.21203/rs.3.rs-137994/v1

Cited by 8 publications

(10 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Data were performed in accordance with Health Insurance Portability and Accountability Act standards and the electronic records were integrated with relational database software 42 . The Institutional Review Boards of Beth Israel Deaconess Medical Center (Boston, MA) and the Massachusetts Institute of Technology (Cambridge, MA) approved the establishment of MIMIC-II 42 – 44 . In addition, the research had no therapeutic implication and all data were de-identified to approve patient’s confidentiality.…”

Section: Methodsmentioning

confidence: 99%

Concatenated convolutional neural network model for cuffless blood pressure estimation using fuzzy recurrence properties of photoplethysmogram signals

Malayeri

Khodabakhshi

2022

Sci Rep

View full text Add to dashboard Cite

Due to the importance of continuous monitoring of blood pressure (BP) in controlling hypertension, the topic of cuffless BP estimation has been widely studied in recent years. A most important approach is to explore the nonlinear mapping between the recorded peripheral signals and the BP values which is usually conducted by deep neural networks. Because of the sequence-based pseudo periodic nature of peripheral signals such as photoplethysmogram (PPG), a proper estimation model needed to be equipped with the 1-dimensional (1-D) and recurrent layers. This, in turn, limits the usage of 2-dimensional (2-D) layers adopted in convolutional neural networks (CNN) for embedding spatial information in the model. In this study, considering the advantage of chaotic approaches, the recurrence characterization of peripheral signals was taken into account by a visual 2-D representation of PPG in phase space through fuzzy recurrence plot (FRP). FRP not only provides a beneficial framework for capturing the spatial properties of input signals but also creates a reliable approach for embedding the pseudo periodic properties to the neural models without using recurrent layers. Moreover, this study proposes a novel deep neural network architecture that combines the morphological features extracted simultaneously from two upgraded 1-D and 2-D CNNs capturing the temporal and spatial dependencies of PPGs in systolic and diastolic BP estimation. The model has been fed with the 1-D PPG sequences and the corresponding 2-D FRPs from two separate routes. The performance of the proposed framework was examined on the well-known public dataset, namely, multi-parameter intelligent in Intensive Care II. Our scheme is analyzed and compared with the literature in terms of the requirements of the standards set by the British Hypertension Society (BHS) and the Association for the Advancement of Medical Instrumentation (AAMI). The proposed model met the AAMI requirements, and it achieved a grade of A as stated by the BHS standard. In addition, its mean absolute errors and standard deviation for both systolic and diastolic blood pressure estimations were considerably low, 3.05 ± 5.26 mmHg and 1.58 ± 2.6 mmHg, in turn.

show abstract

Section: Methodsmentioning

confidence: 99%

Concatenated convolutional neural network model for cuffless blood pressure estimation using fuzzy recurrence properties of photoplethysmogram signals

Malayeri

Khodabakhshi

2022

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Deep Learning Model Structures. The recent advances in deep learning have brought the surge of fully data-driven models that take raw physiological signals as inputs, such as electrocardiogram (ECG), photoplethysmogram (PPG), ballistocardiogram (BCG), or the combination of them, which can automatically learn representations from these signals without handcrafted feature design [14][15][16][17][18]. Various deep learning models have been proposed for cuffless BP estimation in recent years such as deep neural network (DNN) [19] and one-dimensional (1D) convolutional neural network (CNN) [20], etc.…”

Section: Related Workmentioning

confidence: 99%

UTransBPNet: A General Deep Learning Model for Cuffless Blood Pressure Estimation under Activities

Gao¹,

Liu²,

Hong³

et al. 2023

Preprint

View full text Add to dashboard Cite

Cuffless blood pressure (BP) monitoring has gained great attention in the past twenty years considering its significant benefits in cardiovascular healthcare. However, the main challenge of this technology is the inaccurate BP modeling under activities, i.e., existing work have either been inappropriately validated with sufficient intra-individual BP variations, or did not show promising estimation accuracy under activities. In this study, a novel deep learning model UTransBPNet, featured in short- and long-range feature representation, is proposed aiming to improve the estimation accuracy and tracking capability of intra-individual BP changes under activities. The model performance was comprehensively evaluated in three different datasets, i.e., one public dataset (Dataset_MIMIC) and two datasets under daily activities (Dataset_Drink and Dataset_Exercise). Under subject-independent validation, the model achieved state-of-the-art performance in the Dataset_MIMIC, with the mean absolute differences (MADs) for systolic BP (SBP) and diastolic BP (DBP) of 4.38 and 2.25 mmHg, respectively. In addition, the model achieved strong tracking capability of intra-individual BP variations under activities, with the individual Pearson’ correlation coefficients for SBP and DBP of 0.61±0.17 and 0.62±0.13 (Dataset_Drink), 0.82±0.11 and 0.72±0.18 (Dataset_Exercise), respectively. Moreover, this study for the first time tested the generalization capability across different activities, and showed that with small-sized scenario-specific data for finetuning, our model showed good cross-scenario generalization capability which however degraded significantly when there are differences in the BP distribution and variation patterns between the datasets. In conclusion, UTransBPNet is generally a very promising deep learning model for accurate cuffless BP estimation and tracking BP changes. Future work should further investigate the influence of BP distribution and variation patterns on the generalization capability for building effective training dataset.

show abstract

“…The PPG, ECG, and invasive blood pressure of 200 patients are selected from the publically available dataset, namely, Multi-Parameter Intelligent in Intensive Care II (MIMIC-II). Data were performed in accordance with Health Insurance Portability and Accountability Act standards and the electronic records were integrated with relational database software 49. The Institutional Review Boards of Beth Israel Deaconess Medical Center (Boston, MA) and the Massachusetts Institute of Technology (Cambridge, MA) approved the establishment of MIMIC-II [49][50][51] . In addition, the research had no therapeutic implication and all data were de-identified to approve patient's confidentiality.…”

Section: Datasetmentioning

confidence: 99%

Concatenated Convolutional Neural Network Model for Cuffless Blood Pressure Estimation Using Fuzzy Recurrence Properties of PPG Signals

Malayeri

Khodabakhshi

2022

Preprint

View full text Add to dashboard Cite

Due to the importance of continuous monitoring of blood pressure (BP) in controlling hypertension, the topic of cuffless blood pressure (BP) estimation has been widely studied in recent years. A most important approach is to explore the nonlinear mapping between the recorded peripheral signals and the BP values which is usually conducted by deep neural networks. Because of the sequence-based pseudo periodic nature of peripheral signals such as photoplethysmogram (PPG), a proper estimation model needed to be equipped with the 1-dimensional (1-D) and recurrent layers. This, in turn, limits the usage of 2-dimensional (2-D) layers adopted in convolutional neural networks (CNN) for embedding spatial information in the model. In this study, considering the advantage of chaotic approaches, the recurrence characterization of peripheral signals was taken into account by a visual 2-D representation of PPG in phase space through fuzzy recurrence plot (FRP). FRP not only provides a beneficial framework for capturing the spatial properties of input signals but also creates a reliable approach for embedding the pseudo periodic properties to the neural models without using recurrent layers. Moreover, this study proposes a novel deep neural network architecture that combines the morphological features extracted simultaneously from two upgraded 1-D and 2-D CNNs capturing the temporal and spatial dependencies of PPGs in systolic and diastolic BP estimation. The model has been fed with the 1-D PPG sequences and the corresponding 2-D FRPs from two separate routes. The performance of the proposed framework was examined on the well-known public dataset, namely, Multi-Parameter Intelligent in Intensive Care II. Our scheme is analyzed and compared with the literature in terms of the requirements of the standards set by the British Hypertension Society (BHS) and the Association for the Advancement of Medical Instrumentation (AAMI). The proposed model met the AAMI requirements, and it achieved a grade of A as stated by the BHS standard. In addition, its mean absolute errors (MAE) and standard deviation for both systolic and diastolic blood pressure estimations were considerably low, 3.05±5.26 mmHg and 1.58±2.6 mmHg, in turn.

show abstract

Combined Deep CNN–LSTM Network-based Multitasking Learning Architecture for Noninvasive Continuous Blood Pressure Estimation using Difference in ECG-PPG Features

Cited by 8 publications

References 22 publications

Concatenated convolutional neural network model for cuffless blood pressure estimation using fuzzy recurrence properties of photoplethysmogram signals

Concatenated convolutional neural network model for cuffless blood pressure estimation using fuzzy recurrence properties of photoplethysmogram signals

UTransBPNet: A General Deep Learning Model for Cuffless Blood Pressure Estimation under Activities

Concatenated Convolutional Neural Network Model for Cuffless Blood Pressure Estimation Using Fuzzy Recurrence Properties of PPG Signals

Contact Info

Product

Resources

About