Whale Optimization Algorithm with a Hybrid Relation Vector Machine: A Highly Robust Respiratory Rate Prediction Model Using Photoplethysmography Signals
Abstract
:1. Introduction
2. Materials and Methods
2.1. Database
2.2. Pre-Extracted Respiratory Wave and Respiratory Rate by the EEMD-PCA Method
2.3. Signal Quality Index Calculation
2.3.1. PPG Signal Quality Index (SQI) Calculation
2.3.2. Respiratory Quality Index (RQI) Calculation
2.4. WOA-HRVM Model
2.5. Performance Measurement
3. Results
4. Discussion
References | Method | Length (sec) | Subjects | Overlap (sec) | MAE | RMSE | Dis (%) |
---|---|---|---|---|---|---|---|
This study | WOA-HRVM | 32 | 53 | 24 | 1.24 | 1.79 | 6.6% |
Adami [18] | EMD and DWT + EKF | 60 | 53 | 30 | 0.73 | - | - |
Pongpanut [25] | RRWaveNet | 32 | 53 | - | 1.62 | - | 1.9% |
Sharma [20] | EEMD + KF | 32 | 53 | 29 | 1.90 | - | - |
Aqajari [33] | CycleGAN | 30 | 53 | - | 1.90 | - | - |
Lee [26] | CAGBA | 32 | 20 | 0 | 1.94 | 0.61 | 62.26% |
Bian [32] | Deep learning | 60 | 53 | 59 | 2.50 | - | - |
Karlen [12] | SmartQualityFusion method | 60 | 53 | - | 2.60 | - | - |
Birrenkott [14] | RQI calculation and fusion | 32 | 53 | 17 | 3.12 | 4.39 | 23.2% |
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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References | Database | Subjects | Methodology | Innovation | Drawbacks |
---|---|---|---|---|---|
Nakajima [11] | Self-collection | 11 | Digital filtering technique | Real-time PPG-based respiration rate detection | Lack of universal applicability |
Karlen [12] | CapnoBase | 94 | Smart fusion method | Set discard thresholds with PPG signal quality metrics | Discard 45% of data |
Birrenkott [14] | CapnoBase | 42 | RQI calculation and fusion | Adjustable threshold value to change accuracy | Inaccurate estimation of low-quality PPG signals |
Selvakumar [3] | CapnoBase | 42 | RIAV based on FFT | Respiration rate detection on low-cost hardware | Low accuracy in detecting too-fast breathing |
Sharma [20] | BIDMC | 53 | EEMD + KF | Kalman filtering is applied to the reconstructed signal | KF is not suitable for non-linear PPG signals |
Adami [18] | BIDMC | 53 | CEEMDAN + DWT + EKF | Leverage time and frequency domain information | Framework calculation is too complicated |
Mohammad [19] | MIMIC | 121 | EMD family and PCA | Free from parameter selection | Sensitivity to high-amplitude noise in the respiratory range |
Shuzan [24] | VORTAL | 39 | Machine-learning model | Hyperparameter optimization | Tested only on resting young people |
Pongpanut [25] | BIDMC | 53 | RRWaveNet | Improve model robustness using transfer learning | Discarded low-quality signals by SQI metric |
Dataset | Method | Mean | Mean +1.96SD | Mean −1.96SD |
---|---|---|---|---|
training set | This study | 0 | 1.930 | −1.930 |
EEMD-PCA | 0.070 | 5.278 | −5.138 | |
test set | This study | −0.015 | 3.533 | −3.564 |
EEMD-PCA | 0.121 | 4.906 | −4.660 |
Dataset | Method | MAE | RMSE |
---|---|---|---|
training set | This study | 0.71 | 0.99 |
EEMD-PCA | 1.99 | 2.66 | |
test set | This study | 1.24 | 1.79 |
EEMD-PCA | 1.86 | 2.44 |
RRI (bpm) | Training Set | Test Set | ||||
---|---|---|---|---|---|---|
N | MAEthis study [MAEEEMD-PCA] | RMSEthis study [RMSEEEMD-PCA] | N | MAEthis study [MAEEEMD-PCA] | RMSEthis study [RMSEEEMD-PCA] | |
below 12 | 61 | 1.08 [6.40] | 1.56 [6.57] | 16 | 2.68 [6.25] | 3.52 [6.34] |
12–16 | 563 | 0.79 [2.34] | 1.08 [2.65] | 202 | 1.45 [2.21] | 1.90 [2.50] |
17–20 | 1118 | 0.63 [1.05] | 0.82 [1.36] | 407 | 0.91 [1.03] | 1.21 [1.32] |
21–24 | 211 | 0.75 [3.72] | 1.16 [3.89] | 88 | 1.80 [3.41] | 2.45 [3.56] |
above 24 | 40 | 0.98 [7.17] | 2.14 [7.47] | 13 | 4.28 [6.62] | 5.01 [6.77] |
Subject | R1 | R2 | R3 | R4 |
---|---|---|---|---|
Subject 01 | 0.14 | 0.70 | −0.24 | −0.70 |
Subject 02 | 0.30 | −0.10 | 0.07 | 0.08 |
Subject 03 | −0.22 | 0.07 | −0.27 | −0.54 |
Subject 04 | −0.07 | 0.43 | −0.16 | −0.43 |
… | … | … | … | … |
Subject 53 | −0.32 | −0.29 | −0.29 | −0.16 |
Average | 0.28 | 0.24 | 0.27 | 0.28 |
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Dong, X.; Wang, Z.; Cao, L.; Chen, Z.; Liang, Y. Whale Optimization Algorithm with a Hybrid Relation Vector Machine: A Highly Robust Respiratory Rate Prediction Model Using Photoplethysmography Signals. Diagnostics 2023, 13, 913. https://doi.org/10.3390/diagnostics13050913
Dong X, Wang Z, Cao L, Chen Z, Liang Y. Whale Optimization Algorithm with a Hybrid Relation Vector Machine: A Highly Robust Respiratory Rate Prediction Model Using Photoplethysmography Signals. Diagnostics. 2023; 13(5):913. https://doi.org/10.3390/diagnostics13050913
Chicago/Turabian StyleDong, Xuhao, Ziyi Wang, Liangli Cao, Zhencheng Chen, and Yongbo Liang. 2023. "Whale Optimization Algorithm with a Hybrid Relation Vector Machine: A Highly Robust Respiratory Rate Prediction Model Using Photoplethysmography Signals" Diagnostics 13, no. 5: 913. https://doi.org/10.3390/diagnostics13050913
APA StyleDong, X., Wang, Z., Cao, L., Chen, Z., & Liang, Y. (2023). Whale Optimization Algorithm with a Hybrid Relation Vector Machine: A Highly Robust Respiratory Rate Prediction Model Using Photoplethysmography Signals. Diagnostics, 13(5), 913. https://doi.org/10.3390/diagnostics13050913