# End-To-End Deep Learning Architecture for Continuous Blood Pressure Estimation Using Attention Mechanism

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## Abstract

**:**

## 1. Introduction

- BP can be estimated using only raw signals with minimal preprocessing.
- All combinations of signals were used as input, and their performance studied.
- By using the attention mechanism, the performance of the model was improved and its applicability as an analytical metric for BP estimation verified.

## 2. Materials and Methods

#### 2.1. Data Acquisition

#### 2.2. Data Preprocessing

#### 2.3. Deep Learning Model

#### 2.3.1. Convolutional Neural Network

#### 2.3.2. Bidirectional Gated Recurrent Unit

#### 2.3.3. Attention Mechanism

#### 2.4. Proposed Model

#### 2.4.1. Model Architecture

#### 2.4.2. Training Setting

## 3. Results

#### 3.1. Performance Comparison by Signal Combination

#### 3.2. Attention Mechanism Performance

#### 3.3. Comparison to the Multiple Linear Regression Model

## 4. Discussion

#### 4.1. Main Contributions

#### 4.2. Result Interpretation from Global Standard Perspective of BP Monitoring

#### 4.3. Comparison Result With Related Works

#### 4.4. Limitations of the Study

## 5. Conclusions and Future Work

## Author Contributions

## Funding

## Conflicts of Interest

## Abbreviations

BP | Blood pressure |

SBP | Systolic blood pressure |

DBP | Diastolic blood pressure |

ABP | Arterial blood pressure |

ICU | Intensive care unit |

PWV | Pulse wave velocity |

PTT | Pulse transit time |

ECG | Electrocardiogram |

PPG | Photoplethysmogram |

BCG | Ballistocardiogram |

PVDF | Polyvinylidene fluoride |

RJI | R-J interval |

ANN | Artificial neural network |

MLR | Multiple linear regression |

RRI | R-R interval |

CNN | Convolutional neural network |

Bi-GRU | Bidirectional gated unit |

ReLU | Rectified linear unit |

RNN | Recurrent neural network |

LSTM | Long short term memory |

MLP | Multilayer perceptron |

MSE | Mean squared error |

MIMIC | Medical Information Mart for Intensive Care |

RMSE | Root mean square error |

MAE | Mean absolute error |

SD | Standard deviation |

ANOVA | Analysis of variance |

LOA | Limits of agreement |

AAMI | US Association for the Advancement of Medical Instrumentation |

BHS | British Hypertension Society |

LOSO | Leave-one-subject-out |

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**Figure 9.**Left: Sample of the estimated BP with and without attention mechanism; Right: Heat map of the weights of the attention mechanism at the point where the error was low. The darker color denotes higher attention weight.

**Figure 11.**Bland–Altman plot of DBP and SBP. The orange line denotes the limit of agreement (LOA) and the blue line denotes the mean of difference error between reference and estimation.

**Figure 12.**Comparison between estimated and reference BP. (

**a**) is the best case; (

**b**) is the worst case.

**Figure 13.**(

**a**) example of the calculation of PTT and RJI in one cardiac cycle; (

**b**) example of the excluded peaks. Red dots denote each characteristic point, and the red shaded region shows the area where peaks were not detected.

**Figure 14.**Scatter plots between PTT and Systolic BP of (

**a**) good case and (

**b**) bad case. The black line indicates a fitting line.

Signal | HPF (Hz) | LPF (Hz) |
---|---|---|

ECG | 0.5 | 35 |

BCG | 4 | 15 |

PPG | 0.5 | 15 |

Network | Layer | Shape | Out | Padding | Stride | Kernel |
---|---|---|---|---|---|---|

CNN | Conv | $625\times 3$ | 64 | Same | 1 | 3 |

BN + ReLU | ||||||

Conv | $625\times 64$ | 64 | Same | 1 | 3 | |

BN + ReLU | ||||||

Maxpool (size = 3) | $625\times 64$ | - | Same | 3 | - | |

Conv | $209\times 64$ | 128 | Same | 1 | 3 | |

BN + ReLU | ||||||

Conv | $209\times 128$ | 128 | Same | 1 | 3 | |

BN + ReLU | ||||||

Maxpool (size = 3) | $209\times 128$ | - | Same | 3 | - | |

Conv | $70\times 128$ | 256 | Same | 1 | 3 | |

BN + ReLU | ||||||

Conv | $70\times 256$ | 256 | Same | 1 | 3 | |

BN + ReLU | ||||||

Conv | $70\times 256$ | 256 | Same | 1 | 3 | |

BN + ReLU | ||||||

Maxpool (size = 3) | $70\times 256$ | - | Same | 3 | - | |

Conv | $24\times 256$ | 512 | Same | 1 | 3 | |

BN + ReLU | ||||||

Conv | $24\times 512$ | 512 | Same | 1 | 3 | |

BN + ReLU | ||||||

Conv | $24\times 512$ | 512 | Same | 1 | 3 | |

BN + ReLU | ||||||

Maxpool (size = 3) | $24\times 512$ | - | Same | 3 | - | |

Bi-GRU | Forward | $8\times 512$ | 64 | - | ||

Backward | $8\times 512$ | 64 | - | |||

Concatenation | ||||||

Attention | 1-layer perceptron | $8\times 128$ | 1 | - | ||

Activation tanh | ||||||

Softmax | ||||||

Weighted sum | ||||||

1-layer perceptron | 128 | 2 | - |

**Table 3.**Performance comparison for combinations of input signals without attention model and with attention. The 95% confidence interval is indicated below the error of the proposed model.

Model | Input | SBP (mmHg) | DBP (mmHg) | ||||||
---|---|---|---|---|---|---|---|---|---|

RMSE | MAE | SD | ${\mathbf{R}}^{\mathbf{2}}$ | RMSE | MAE | SD | ${\mathbf{R}}^{\mathbf{2}}$ | ||

CNN+Bi-GRU | ECG | 7.02 | 5.51 | 4.66 | 0.24 | 5.16 | 4.06 | 3.45 | 0.27 |

PPG | 6.88 | 5.34 | 4.60 | 0.28 | 5.73 | 4.45 | 4.09 | 0.14 | |

BCG | 7.24 | 5.59 | 5.03 | 0.20 | 5.29 | 4.06 | 3.71 | 0.22 | |

ECG, PPG | 5.83 | 4.46 | 4.06 | 0.46 | 4.74 | 3.70 | 3.37 | 0.38 | |

ECG, BCG | 6.74 | 5.30 | 4.60 | 0.31 | 4.82 | 3.74 | 3.27 | 0.34 | |

PPG, BCG | 6.44 | 4.86 | 4.50 | 0.36 | 5.04 | 3.88 | 3.62 | 0.27 | |

ECG, PPG, BCG | 5.87 | 4.51 | 4.14 | 0.48 | 4.73 | 3.71 | 3.39 | 0.40 | |

CNN+Bi-GRU+Attention(proposed model) | ECG, PPG, BCG | 5.42[1.97, 8.87] | 4.06[1.53, 6.59] | 4.04 | 0.52 | 4.30[0.94, 7.72] | 3.33[0.61, 6.05] | 3.42 | 0.49 |

**Table 4.**Mean values of RMSE, MAE, and ${R}^{2}$ when the input was a single signal and when it was a combination of multiple signals.

Input | SBP (mmHg) | DBP (mmHg) | ||||
---|---|---|---|---|---|---|

RMSE | MAE | mean${\mathbf{R}}^{\mathbf{2}}$ | RMSE | MAE | mean${\mathbf{R}}^{\mathbf{2}}$ | |

Single signal | 7.04 | 5.47 | 0.24 | 5.39 | 4.19 | 0.21 |

Multiple signals | 6.21 | 4.78 | 0.40 | 4.83 | 3.76 | 0.35 |

Inputs | ECG | PPG | BCG | ECG, PPG | ECG, BCG | BCG, PPG | ECG, BCG, PPG | Proposed Model |
---|---|---|---|---|---|---|---|---|

ECG | - | - | p < 0.05 | - | - | p < 0.05 | p < 0.05 | |

PPG | - | p < 0.05 | - | p < 0.05 | p < 0.05 | p < 0.05 | ||

BCG | p < 0.05 | - | p < 0.05 | p < 0.05 | p < 0.05 | |||

ECG, PPG | p < 0.05 | - | - | p < 0.05 | ||||

ECG, BCG | - | p < 0.05 | p < 0.05 | |||||

BCG, PPG | - | p < 0.05 | ||||||

ECG, BCG, PPG | p < 0.05 |

Inputs | ECG | PPG | BCG | ECG, PPG | ECG, BCG | BCG, PPG | ECG, BCG, PPG | Proposed Model |
---|---|---|---|---|---|---|---|---|

ECG | - | - | - | p < 0.05 | - | - | p < 0.05 | |

PPG | - | p < 0.05 | p < 0.05 | p < 0.05 | p < 0.05 | p < 0.05 | ||

BCG | - | - | - | - | p < 0.05 | |||

ECG, PPG | - | - | - | p < 0.05 | ||||

ECG, BCG | - | - | p < 0.05 | |||||

BCG, PPG | - | p < 0.05 | ||||||

ECG, BCG, PPG | p < 0.05 |

Model | SBP (mmHg) | DBP (mmHg) | ||||||
---|---|---|---|---|---|---|---|---|

RMSE | MAE | SD | ${\mathbf{R}}^{2}$ | RMSE | MAE | SD | ${\mathbf{R}}^{2}$ | |

Proposed model | 5.42 | 4.06 | 4.04 | 0.52 | 4.30 | 3.33 | 3.42 | 0.49 |

MLR | 6.40 | 5.19 | 3.45 | 0.26 | 4.75 | 3.85 | 2.69 | 0.22 |

Mean Error | Standard Deviation | ||
---|---|---|---|

AAMI standard | SBP, DBP | ≤ 5 (mmHg) | ≤ 8 (mmHg) |

Proposed model | SBP | −0.20 | 5.83 |

DBP | −0.02 | 4.91 |

Absolute Difference | Grade | ||||
---|---|---|---|---|---|

≤ 5 (mmHg) | ≤ 10 (mmHg) | ≤ 15 (mmHg) | |||

BHS standard | SBP, DBP | 60% | 85% | 95% | A |

50% | 75% | 90% | B | ||

40% | 65% | 80% | C | ||

Worse than C | D | ||||

Proposed model | SBP | 73% | 93% | 98% | A |

DBP | 80% | 96% | 99% | A |

Author | Data Size | Calibration | Model | Input | SBP (mmHg) | DBP (mmHg) | |
---|---|---|---|---|---|---|---|

Inputs | Signal | Error | Error | ||||

Chan et al. [14] | Unspecified | Cal-based | Linear regression | Feature (PTT) | ECG PPG | ME: 7.49 STD: 8.82 | ME: 4.08 STD: 5.62 |

Kachuee et al. [15] | 1000 subjects 10 min (MIMIC 3) | Cal-based | AdaBoost | Features | ECG PPG | MAE: 8.21 STD: 5.45 | MAE: 4.31 STD: 3.52 |

Cal-free | MAE: 11.17 STD: 10.09 | MAE: 5.35 STD: 6.14 | |||||

Kurylyak et al. [17] | 15,000 heartbeats | Cal-based | Deep learning (ANN) | Features | PPG | ME: 3.80 STD: 3.46 | ME: 2.21 STD: 2.09 |

Lee et al. [13] | 30 subjects | Cal-based | Deep learning (ANN) | Feature (IPD) | BCG | ME: 0.01 STD: 6.75 | ME: 0.05 STD: 5.83 |

Slapnivcar et al. [19] | 510 subjects 700 h (MIMIC 3) | Cal-based | Deep learning (ResNet) | Raw | PPG | MAE: 9.43 | MAE: 6.88 |

Cal-free | MAE: 15.41 | MAE: 12.38 | |||||

Su et al. [16] | 84 subjects 10 min | Cal-based | Deep learning (RNN) | Features | ECG PPG | RMSE: 3.73 | RMSE: 2.43 |

Tanveer et al. [20] | 39 subjects (MIMIC 1) | Cal-based | Deep learning (ANN+ LSTM) | Raw | ECG PPG | RMSE: 1.27 MAE: 0.93 | RMSE: 0.73 MAE: 0.52 |

Wang et al. [18] | 58,795 intervals of PPG (MIMIC 1) | Cal-based | Deep learning (ANN) | Features | PPG | MAE: 4.02 STD: 2.79 | MAE: 2.27 STD: 1.82 |

This study | 15 subjects 30 min | Cal-based | Deep learning (CNN+ Bi-GRU) | Raw | BCG | ME: −0.82 STD: 7.50 | ME: −0.97 STD: 5.36 |

ECG PPG | MAE: 4.46 STD: 4.06 | MAE: 3.70 STD: 3.37 | |||||

Deep learning (CNN+ Bi-GRU+ Attention) | ECG PPG BCG | MAE: 4.06 STD: 4.04 | MAE: 3.33 STD: 3.42 |

**Table 11.**Performance comparison between calibration-free and calibration-based methods using the proposed model.

Input | Method | SBP (mmHg) | DBP (mmHg) | ||||||
---|---|---|---|---|---|---|---|---|---|

RMSE | MAE | SD | ${\mathbf{R}}^{\mathbf{2}}$ | RMSE | MAE | SD | ${\mathbf{R}}^{\mathbf{2}}$ | ||

ECG, PPG, BCG | Cal-based | 5.42 | 4.06 | 4.04 | 0.52 | 4.3 | 3.33 | 3.42 | 0.49 |

Cal-free | 13.14 | 9.70 | 8.86 | 0.23 | 7.55 | 5.79 | 4.84 | 0.44 |

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## Share and Cite

**MDPI and ACS Style**

Eom, H.; Lee, D.; Han, S.; Hariyani, Y.S.; Lim, Y.; Sohn, I.; Park, K.; Park, C.
End-To-End Deep Learning Architecture for Continuous Blood Pressure Estimation Using Attention Mechanism. *Sensors* **2020**, *20*, 2338.
https://doi.org/10.3390/s20082338

**AMA Style**

Eom H, Lee D, Han S, Hariyani YS, Lim Y, Sohn I, Park K, Park C.
End-To-End Deep Learning Architecture for Continuous Blood Pressure Estimation Using Attention Mechanism. *Sensors*. 2020; 20(8):2338.
https://doi.org/10.3390/s20082338

**Chicago/Turabian Style**

Eom, Heesang, Dongseok Lee, Seungwoo Han, Yuli Sun Hariyani, Yonggyu Lim, Illsoo Sohn, Kwangsuk Park, and Cheolsoo Park.
2020. "End-To-End Deep Learning Architecture for Continuous Blood Pressure Estimation Using Attention Mechanism" *Sensors* 20, no. 8: 2338.
https://doi.org/10.3390/s20082338