PPG Signals-Based Blood-Pressure Estimation Using Grid Search in Hyperparameter Optimization of CNN–LSTM
Abstract
:1. Introduction
2. Materials and Methods
2.1. Dataset
2.2. Preprocessing
2.3. Hyperparameters Tuning for the Proposed Model
Optimizer, Learning Rate, and Batch Size
- The stochastic gradient descent (SGD) optimizer updates the parameters iteratively by subtracting the gradient multiplied by the learning rate, as described in Equation (3):
- b.
- Root mean square propagation (RMSprop): The RMSprop optimizer adapts the learning rate for each parameter based on the gradient changes in the previous iterations. RMSprop utilizes the average squared estimation of the previous gradients to adjust the learning rate at each parameter update step. The formula for RMSprop is described in Equations (4) and (5), where ρ represents the forgetting factor (set to 0.9) and t denotes the current time step:
- c.
- Adaptive moment estimation (Adam): Adam is the most commonly used optimization algorithm in deep learning for training models. The Adam optimizer combines momentum optimization concepts and RMSprop to effectively update model parameters during the training process. The Adam optimizer is widely employed in training deep learning models on time series datasets because it accelerates convergence and achieves superior results. The formula for the Adam optimizer is shown in Equation (6):
- d.
- Adadelta is an extension from AdaGrad, which is calculated by using Equation (12), where RMS is root mean square error:
2.4. Proposed Deep Learning Model for Estimating BP
2.4.1. Long Short Term-Memory (LSTM) Architecture
2.4.2. LSTM-Based Autoencoder
2.4.3. CNN–LSTM Architecture
2.5. Metrics and Evaluation
3. Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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BHS | Grade | CP | CP | CP | IEEE | Grade | MAD (mmHg) | AAMI | Grade | ME (mmHg) | SD (mmHg) |
A | 60% | 85% | 95% | A | ≤5 | Pass | ≤5 | ≤8 | |||
B | 50% | 75% | 90% | B | 5–6 | ||||||
C | 40% | 65% | 85% | C | 6–7 | ||||||
D | Lower than C | D | Lower than C |
Author | Method | Input | Dataset | SBP | DBP | ||
---|---|---|---|---|---|---|---|
MAE | SD | MAE | SD | ||||
Proposed work | LSTM | PPG | MIMIC III | 14.2 | 20.7 | 7.53 | 10.01 |
Proposed work | LSTM– Autoencoder | PPG | MIMIC III | 13.45 | 19.01 | 5.71 | 7.67 |
Proposed work | CNN + LSTM | PPG | MIMIC III | 3.64 | 7.04 | 2.39 | 3.79 |
[44] | SVR | PPG | MIMIC II | 8.54 | - | 4.34 | - |
[7] | SVM | PPG | Queensland | 11.6 | 8.2 | 7.6 | 6.7 |
[10] | Spectro-temporal ResNet | PPG | MIMIC III | 9.43 | - | 6.88 | - |
[13] | ANN | PPG | MIMIC II | 9.74 | 12.40 | 4.65 | 6.29 |
[12] | RNN | PPG | MIMIC III | 12.08 | 15.67 | 5.56 | 7.32 |
[11] | U-Net | PPG | MIMIC III | 5.73 | - | 3.45 | - |
[45] | CNN | PPG | Private dataset | - | 14.03 | - | - |
[8] | AdaBoost | PPG | MIMIC II | 8.22 | 10.38 | 4.17 | 4.22 |
[16] | CNN–BiLSTM | PPG | UCI (MIMIC II) | 7.85 | 8.41 | 4.42 | 4.80 |
Assessment Evaluation | IEEE Standard | AAMI Standard | BHS Standards | ||||||
---|---|---|---|---|---|---|---|---|---|
MAD (≤4 mmHg) | MAPD (%) | Grade | ME (<5 mmHg) | SD (<8 mmHg) | CP5 (>60%) | CP10 (>85%) | CP15 (>95%) | Grade | |
LSTM proposed model | |||||||||
SBP | 14.281 | 0.12 | D | −0.49 | 20.7 | 30.92 | 53.07 | 67.37 | D |
DBP | 7.53 | 0.133 | C | −0.21 | 10.01 | 45.14 | 72.02 | 86 | C |
LSTM–autoencoder proposed model | |||||||||
SBP | 26.94 | 0.11 | D | −0.93 | 19.01 | 27 | 52.5 | 68.70 | D |
DBP | 5.71 | 0.01 | B | −0.56 | 7.67 | 56.67 | 83.8 | 92.98 | B |
CNN–LSTM proposed model | |||||||||
SBP | 5.34 | 0.04 | B | 0.13 | 7.04 | 63.4 | 85.9 | 92.78 | B |
DBP | 2.89 | 0.05 | A | 0.48 | 3.79 | 81.70 | 98.28 | 100 | A |
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Mahardika T, N.Q.; Fuadah, Y.N.; Jeong, D.U.; Lim, K.M. PPG Signals-Based Blood-Pressure Estimation Using Grid Search in Hyperparameter Optimization of CNN–LSTM. Diagnostics 2023, 13, 2566. https://doi.org/10.3390/diagnostics13152566
Mahardika T NQ, Fuadah YN, Jeong DU, Lim KM. PPG Signals-Based Blood-Pressure Estimation Using Grid Search in Hyperparameter Optimization of CNN–LSTM. Diagnostics. 2023; 13(15):2566. https://doi.org/10.3390/diagnostics13152566
Chicago/Turabian StyleMahardika T, Nurul Qashri, Yunendah Nur Fuadah, Da Un Jeong, and Ki Moo Lim. 2023. "PPG Signals-Based Blood-Pressure Estimation Using Grid Search in Hyperparameter Optimization of CNN–LSTM" Diagnostics 13, no. 15: 2566. https://doi.org/10.3390/diagnostics13152566