Optimizing Input Feature Sets Using Catch-22 and Personalization for an Accurate and Reliable Estimation of Continuous, Cuffless Blood Pressure
Abstract
:1. Introduction
2. Methods
2.1. Human Data Ethical Statement
2.2. Patient Data
2.3. Feature Extraction
2.4. Method of Analysis—Machine Learning Model Training
2.5. Method of Analysis—Personalized and Calibration-Free Model Testing
2.6. Statistical Analysis
3. Results
4. Discussion
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A. Catch-22 Detailed Description
# | Feature Name | Description |
---|---|---|
I. | Distribution-related features | |
1 | DN_HistogramMode_5 | Mode of z-scored distribution (5-bin histogram) |
2 | DN_HistogramMode_10 | Mode of z-scored distribution (10-bin histogram) |
II. | Simple temporal statistics | |
3 | SB_BinaryStats_mean_longstretch1 | Longest period of consecutive values above the mean |
4 | DN_OutlierInclude_p_001_mdrmd | Time intervals between successive extreme events above the mean |
5 | DN_OutlierInclude_n_001_mdrmd | Time intervals between successive extreme events below the mean |
III. | Linear autocorrelation | |
6 | CO_flecac | First 1/e crossing of autocorrelation function |
7 | CO_FirstMin_ac | First minimum of autocorrelation function |
8 | SP_Summaries_welch_rect_area_5_1 | Total power in lowest fifth of frequencies in the Fourier power spectrum |
9 | SP_Summaries_welch_rect_centroid | Centroid of the Fourier power spectrum |
10 | FC_LocalSimple_mean3_stderr | Mean error from a rolling 3-sample mean forecasting |
IV. | Non-linear autocorrelation | |
11 | CO_trev_1_num | Time reversibility statistic |
12 | CO_HistogramAMI_even_2_5 | Automutual information, m = 2, r = 5 |
13 | IN_AutoMutualInfoStats_40_gaussian_fnni | First minimum of the automutual information function |
V. | Successive differences | |
14 | MD_hrv_classic_pnn40 | Proportion of successive differences exceeding 0.04σ |
15 | SB_BinaryStats_mean_longstretch0 | Longest period of successive incremental decreases |
16 | SB_MotifThree_quantile_hh | Shannon entropy of two successive letters in equiprobable 3-letter symbolization |
17 | FC_LocalSimple_mean3_stderr | Change in correlation length after iterative differencing |
18 | CO_embed2_Dist_tau_d_expfit_meandiff | Exponential fit to successive distances in 2-d embedding space |
VI. | Fluctuation analysis | |
19 | SC_FlucAnal_2_dfa_50_1_2_logi_prop_r1 | Proportion of slower timescale fluctuations that scale with DFA (50% sampling) |
20 | SC_FlucAnal_2_rsrangefit_50_1_logi_prop_r1 | Proportion of slower timescale fluctuations that scale with linearly rescaled range fits |
V | Others | |
21 | SB_TransitionMatrix_3ac_sumdiagcov | Trace of covariance of transition matrix between symbols in 3-letter alphabet |
22 | PD_PeriodicityWang_th0_01 | Periodicity measure |
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Authors | Year | Sensor Signal | Method Used | Sample Size | Data Source | Study Duration | Mean ± SD Errors (mm Hg) |
---|---|---|---|---|---|---|---|
Tanveer et al. | [10]/2019 | ECG, PPG | Artificial neural network-long short-term memory network | 39 (hemodynamically compromised) | Prospective | Short term (40 s) | SBP: 1.10 (MAE) DBP: 0.58 (MAE) |
Eom et al. | [11]/2020 | PPG | Convolution neural network and bi-directional gated recurrent network | 15 (healthy) | Prospective | Short term (<1 h) | SBP: −0.20 ± 5.83 DBP: −0.02 ± 4.91 |
Sadrawi et al. | [12]/2020 | PPG | Genetic deep convolution autoencoder | 18 (healthy, hemodynamically compromised) | Prospective | Short term (<6 h) | SBP: −1.66 ± 7.496 DBP: 0.66 ± 3.31 |
Athaya et al. | [13]/2021 | PPG | U-net deep learning architecture | 100 (hemodynamically compromised) | MIMIC, MIMIC III | Short term (3.4 h) | SBP: 3.68 ± 4.42 DBP: 1.97 ± 2.92 MAP: 2.17 ± 3.06 |
Jeong et al. | [14]/2021 | ECG, PPG | Convolution neural network-long short-term memory combination | 48 (healthy) | Prospective | Short Term (<800 s) | SBP: 0.2 ± 1.3 DBP: 0.0 ± 1.6 |
Fan et al. | [15]/2021 | ECG | Bi-layer long short-term memory network | 942 (hemodynamically compromised) | MIMIC II | Short Term (~230 s) | SBP: 7.69 ±10.83 DBP: 4.36 ± 5.90 MAP: 4.76 ± 6.47 |
Hu Q et al. | [16]/2022 | PPG | Convolution neural network with attention mechanism, multi-task learning | 1825 (hemodynamically compromised) | UC, Irvine database | Short Term (<20 min) | SBP: 0.97 ± 8.87 DBP: 0.55 ± 4.23 |
Ibtehaz et al. | [17]/2022 | PPG | Two-stage cascaded convolution neural network | 942 (healthy and hemodynamically compromised) | MIMIC III | Short Term (<30 min) | SBP: 5.7 ± 9.2 DBP: 3.4 ± 6.1 MAP: 2.3 ± 4.4 |
Jiang et al. | [18]/2022 | ECG, PPG | Neural network with multi-task learning | 3000 (hemodynamically compromised) | MIMIC-II | Short Term (60 h) | SBP: 4.04 ± 5.8 DBP: 2.29 ± 3.55 MAP: 2.46 ± 3.58 |
Mahmud et al. | [19]/2022 | ECG, PPG | Shallow one-dimensional auto-encoder (U-net architecture) | 942 (hemodynamically compromised) | MIMIC II | Short Term (<21 min) | SBP: 2.73 (MAE) DBP: 1.17 (MAE) |
Seok et al. | [20]/2021 | BCG | Convolution neural network | 30 (healthy) | Prospective | Short Term (<10 s) | SBP: 0.93 ± 6.24 DBP: 0.21 ± 5.42 |
Treebupachatsakul et al. | [21]/2022 | ECG, PPG | Fourier transformation followed by deep learning | >2500 (healthy and hemodynamically compromised) | Kachuee et al., 2015 [22] | Short Term (<30 min) | SBP: 7 DBP: 6 |
Mahardika et al. | [23]/2023 | PPG, ABP | Convolution neural network, long short-term memory network | 55 | MIMIC-III | Short Term (<5 min) | SBP: 0.13 ± 7.04 DBP: 0.48 ± 3.79 |
Vliet et al. | [24]/2024 | PPG | Machine learning algorithm—exact method not disclosed | 124 | Prospective | Short Term (<30 s) | SBP: ±3.7 ± 4.4 DBP: ±2.5 ± 3.7 |
Huang et al. | [25]/2024 | BCG | Deep learning UUNet | 40 (nighttime) | Kansas dataset | Short term (<30 min) | SBP: −0.19 ± 8.31 DBP: −0.04 ± 4.48 |
Liu et al. | [26]/2025 | BCG, IPG | Random forest, XGBoost | 17 | Prospective (healthy) | Short term (<18 min) | SBP, MAD: 3.54 DBP, MAD: 2.57 |
Lasso | Random Forest | ResNET | |
---|---|---|---|
Type | Machine Learning | Machine Learning | Deep Learning |
Principle | Based on least square multiple regression adjusted for overfitting | Based on an ensemble of decision trees based on bagging or boosting these trees | Based on multiple layers (18) of convolution neural networks with batch normalization and ReLU activation function |
Cross-validation | Yes—10-fold | Yes—10-fold | Yes—5-fold |
Complexity | Low | Medium | High |
Hyperparameter | Alpha—Lasso to ridge ratio | Learning rate, leaf size, learning cycles, splits and features to sample | Number of epochs, learning rate |
Computation time | Low | High | High |
Input feature type | Binary, numerical | Binary, numerical | Binary, numerical, and categorical |
Fit parameters | 100 | 492 | 840 |
Data amount (>10 times fit parameter) | Can work with relatively less data | Needs more data for effective performance | Needs large amounts of data |
Descriptor | Training Subjects | Validation Subjects | Test Subjects |
---|---|---|---|
Male | 751 (58%) | 69 (59%) | 69 (59%) |
Female | 542 (42%) | 47 (41%) | 47 (41%) |
Age (>40 years) | 1137 (88%) | 103 (89%) | 103 (89%) |
Total | 1293 | 116 | 116 |
(a) | ||||||
Lasso | Random Forest | ResNET | ||||
μ ± SD | (MAE) | μ ± SD | (MAE) | μ ± SD | (MAE) | |
SBP | −2.11 ± 18.78 | 14.25 | −1.08 ± 18.76 | 14.20 | −1.86 ± 19.55 | 14.69 |
DBP | −0.68 ± 11.77 | 9.18 | −0.24 ± 11.06 | 8.56 | 0.11 ± 11.55 | 9.06 |
(b) | ||||||
Lasso | Random Forest | ResNET | ||||
μ ± SD | (MAE) | μ ± SD | (MAE) | μ ± SD | (MAE) | |
SBP * | −1.51 ± 8.04 | 4.88 | −1.32 ± 7.97 | 4.95 | −1.31 ± 7.91 | 4.83 |
DBP * | −0.52 ± 4.69 | 2.62 | −0.43 ± 4.62 | 2.60 | 0.10 ± 4.59 | 2.65 |
(c) | ||||||
Absolute Difference | ||||||
≤5 mm Hg | ≤10 mm Hg | ≤15 mm Hg | Grade | |||
BHS Standard | SBP, DBP | 60% | 85% | 95% | A | |
50% | 75% | 90% | B | |||
40% | 65% | 80% | C | |||
Worse than C | D | |||||
Proposed Model: Personalized, ResNET | SBP | 65.8% | 91.7% | 95% | A | |
DBP | 85.9% | 96.8% | 98.6% | A |
Method | SBP | DBP |
---|---|---|
F-Statistic, p-Value | F-Statistic, p-Value | |
Machine Learning Algorithm | F(1) = 0.00, 0.99 | F(1) = 0.19, 0.82 |
Method (Calibration-free vs. Personalized) | F(2) = 45.9, 0.00 | F(2) = 46.9, 0.00 |
Method | Systolic BP Estimation Errors | Diastolic BP Estimation Errors | ||||
---|---|---|---|---|---|---|
Bias | SD | MAE | Bias | SD | MAE | |
Estimated by algorithm (in mm Hg) | −1.31 | 7.91 | 4.83 | 0.10 | 4.59 | 2.65 |
Estimated using ”ground truth” (in mm Hg) | −1.21 | 19.46 | 14.75 | −0.40 | 13.68 | 9.87 |
p-values and F-statistic, bias (ANOVA) | F = 1.28, p = 0.26 | F = 2.06, p = 0.15 | ||||
p-values and F-statistic, variance (Levene’s test) | F = 91.42, p = 0.00 | F = 99.04, p = 0.00 |
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Kasbekar, R.S.; Radhakrishnan, S.; Ji, S.; Goel, A.; Clancy, E.A. Optimizing Input Feature Sets Using Catch-22 and Personalization for an Accurate and Reliable Estimation of Continuous, Cuffless Blood Pressure. Bioengineering 2025, 12, 493. https://doi.org/10.3390/bioengineering12050493
Kasbekar RS, Radhakrishnan S, Ji S, Goel A, Clancy EA. Optimizing Input Feature Sets Using Catch-22 and Personalization for an Accurate and Reliable Estimation of Continuous, Cuffless Blood Pressure. Bioengineering. 2025; 12(5):493. https://doi.org/10.3390/bioengineering12050493
Chicago/Turabian StyleKasbekar, Rajesh S., Srinivasan Radhakrishnan, Songbai Ji, Anita Goel, and Edward A. Clancy. 2025. "Optimizing Input Feature Sets Using Catch-22 and Personalization for an Accurate and Reliable Estimation of Continuous, Cuffless Blood Pressure" Bioengineering 12, no. 5: 493. https://doi.org/10.3390/bioengineering12050493
APA StyleKasbekar, R. S., Radhakrishnan, S., Ji, S., Goel, A., & Clancy, E. A. (2025). Optimizing Input Feature Sets Using Catch-22 and Personalization for an Accurate and Reliable Estimation of Continuous, Cuffless Blood Pressure. Bioengineering, 12(5), 493. https://doi.org/10.3390/bioengineering12050493