Research Article
Machine Learning and Electrocardiography Signal-Based Minimum Calculation Time Detection for Blood Pressure Detection
| Nu | Ref | Year | Model methods | | Diastolic blood pressure performances | Systolic blood pressure performances [b] | | | | Signal | Features | Method | MAE | MAPE | MAD | MSE | RMSE | | | MAE | MAPE | MAD | MSE | RMSE | = | |
| 1 | [33] | 2019 | PPT-PIR | SSR-CHC | MARS | | 3.630 | | | | | | | | 7.830 | | | | | 2 | [29] | 2020 | Oscillometric waveforms | Graphical features | WkNN | 11.032 | | | 200.531 | 14.161 | 0.423 | 0.179 | 3.520 | | | 41.998 | 6.480 | 0.948 | 0.899 | 3 | [31] | 2020 | Auscultatory and oscillometric waveforms | Time domain | GMM-HMM | 2.900 | | | | | | | -0.9 | | | | | | | 4 | [34] | 2020 | PPG-ECG | Chaotic, time, and frequency domain | RNN | | | | 1.730 | 1.240 | 0.854 | 0.730 | | | | 1.210 | 0.780 | 0.849 | 0.720 | 5 | [30] | 2020 | Oscillometric waveforms | Chaotic, time, and frequency domain | GPR | 4.271 | 0.288 | | 28.843 | 5.371 | 0.891 | 0.794 | 3.636 | 0.114 | | 23.845 | 4.883 | 0.962 | 0.925 | 6 | [27] | 2020 | PPG-ECG | Time domain | RF | 5.48 | | | | 6.000 | 0.840 | 0.706 | 9.000 | | | | 13.830 | 0.850 | 0.723 | 7 | [32] | 2020 | Speech | Vowels | CNN-R | | | | | 0.350 | | | | | | | 0.236 | | | 8 | [28] | 2020 | PPG | PPG segment series | CNN-LSTM | 3.97 | | | | | 0.950 | 0.903 | | 0.670 | | | | 0.950 | 0.903 | 9 | [26] | 2021 | Peripheral signals | Hibrit | MLR | | | | | 3.000 | 0.970 | 0.941 | | | | | 3.000 | 0.970 | 0.941 | 10 | [20] | 2021 | PPG | Multitype feature | MTFF-ANN | 3.36 | | | | | | | 5.590 | | | | | | | 11 | | | Proposed model ECG 2-second | Time domain | EBT | | 3.310 | 2.430 | 16.520 | 4.060 | 0.970 | 0.930 | | 2.580 | 0.370 | 25.480 | 5.050 | 0.970 | 0.930 | 12 | | | Proposed model-ECG 14-second | Time domain | GPR/EBT | | 3.280 | 1.870 | 13.250 | 3.340 | 0.980 | 0.950 | | 2.000 | 2.680 | 19.190 | 4.380 | 0.980 | 0.950 | 13 | | | Proposed model-ECG 16-second | Time domain | GPR | | 2.440 | 1.830 | 9.640 | 3.100 | 0.980 | 0.950 | | 1.920 | 2.560 | 16.660 | 4.080 | 0.980 | 0.960 |
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CHC: current heart cycle; CNN-R: convolutional neural networks-regression; EBT: ensemble bagged tree; ECG: electrocardiography; GMM-HMM: Gaussian mixture models and hidden Markov; GPR: Gaussian process regression; LSTM: long-short-term memory; MAD: mean absolute difference; MAE: mean absolute error; MAPE: mean absolute percentage error; MAPE: mean absolute percentage error; MLR: multiple linear regression; MSE: mean square error; MTFF-ANN: multitype feature fusion artificial neural network (2 CNN+1 LSTM); PIR: photoplethysmogram intensity ratio; PPG: photoplethysmography; PPT: pulse transit time; RF: random forest; RMSE: root mean square error; RNN: recurrent neural networks; SE: standard error; SSR: state space reconstruction; MARS: multiadaptive regression spline; WkNN: weighted -near neighbor. |