Figure 1.
Visual comparison of Photoplethysmograph (PPG) and Pulse Plethysmograph (PuPG) signals.
Figure 1.
Visual comparison of Photoplethysmograph (PPG) and Pulse Plethysmograph (PuPG) signals.
Figure 2.
Raw PuPG signals of Normal and Hypertension classes.
Figure 2.
Raw PuPG signals of Normal and Hypertension classes.
Figure 3.
Overall flow chart of the proposed design methodology for detection of hypertension through pulse plethysmograph signals.
Figure 3.
Overall flow chart of the proposed design methodology for detection of hypertension through pulse plethysmograph signals.
Figure 4.
Wavelet-based denoising.
Figure 4.
Wavelet-based denoising.
Figure 5.
Wavelet decomposition of raw PuPG signals.
Figure 5.
Wavelet decomposition of raw PuPG signals.
Figure 6.
Denoised version of PuPG signal for Normal and Hypertension through DWT.
Figure 6.
Denoised version of PuPG signal for Normal and Hypertension through DWT.
Figure 7.
EMD algorithm (flow chart).
Figure 7.
EMD algorithm (flow chart).
Figure 8.
EMD decomposition of raw PuPG signals.
Figure 8.
EMD decomposition of raw PuPG signals.
Figure 9.
Preprocessed signal using EMD.
Figure 9.
Preprocessed signal using EMD.
Figure 10.
Block diagram (feature selection and reduction method).
Figure 10.
Block diagram (feature selection and reduction method).
Figure 11.
Performance of accuracy for different feature sets in various classifiers for PuPG signal through method I.
Figure 11.
Performance of accuracy for different feature sets in various classifiers for PuPG signal through method I.
Figure 12.
Performance of sensitivity for different feature sets in various classifiers for PuPG signal through method I.
Figure 12.
Performance of sensitivity for different feature sets in various classifiers for PuPG signal through method I.
Figure 13.
Performance of specificity for different feature sets in various classifiers for PuPG signal through method I.
Figure 13.
Performance of specificity for different feature sets in various classifiers for PuPG signal through method I.
Figure 14.
Confusion matrix for method I.
Figure 14.
Confusion matrix for method I.
Figure 15.
Performance of accuracy for different feature sets in various classifiers for PuPG signal through method II.
Figure 15.
Performance of accuracy for different feature sets in various classifiers for PuPG signal through method II.
Figure 16.
Performance of sensitivity for different feature sets in various classifiers for PuPG signal through method II.
Figure 16.
Performance of sensitivity for different feature sets in various classifiers for PuPG signal through method II.
Figure 17.
Performance of specificity for different feature sets in various classifiers for PuPG signal through method II.
Figure 17.
Performance of specificity for different feature sets in various classifiers for PuPG signal through method II.
Figure 18.
Confusion matrix for method II.
Figure 18.
Confusion matrix for method II.
Figure 19.
Proposed EHDS block diagram.
Figure 19.
Proposed EHDS block diagram.
Figure 20.
Performance of method II in terms of accuracy, sensitivity, and specificity for 1 to 24 transformed features.
Figure 20.
Performance of method II in terms of accuracy, sensitivity, and specificity for 1 to 24 transformed features.
Table 1.
Categorization of blood pressure.
Table 1.
Categorization of blood pressure.
Class | Systolic (mmHg) | Diastolic (mmHg) |
---|
Optimal | Less than 120 | Less than 80 |
Normal | 120 to 129 | 80 to 84 |
High Normal | 130 to 139 | 85 to 89 |
Hypertension | More than or equal to 140 | More than or equal to 90 |
Table 2.
Difference between PPG and PuPG data acquisition.
Table 2.
Difference between PPG and PuPG data acquisition.
Type | Photoplethysmograph (PPG) Sensor | Pulse Plethysmograph (PuPG) Sensor |
---|
Input signal | Optical signal | Pressure changes |
Phenomenon | Blood volumetric changes are detected by measuring the amount of light transmitted or reflected by the sensor. | Blood volumetric changes are detected by the piezoelectric material of the sensor as pressure changes when the blood volume changes. |
Noise Impact | Light signal can be easily impacted by any external light changes. Dirty hand can distort the light intensities. | Piezoelectric material based sensors are normaly temperature sensitive. Dirty hands or foreign material on hand or fingers does not have significant impact. |
Table 3.
Summary of the self-collected PuPG data set.
Table 3.
Summary of the self-collected PuPG data set.
Data Class | Subjects | Age Group | Samples |
---|
Hypertension | Male: 29 | Male: 40–76 | 700 |
Female: 27 | Female: 39–59 | |
Normal | Male: 35 | Male: 21–63 | 709 |
Female: 30 | Female: 20–59 | |
Overall | 121 | 20–76 | 1409 |
Table 4.
Comparison of mean relative energies and frequency ranges of various decomposition levels for Normal and Hypertension classes.
Table 4.
Comparison of mean relative energies and frequency ranges of various decomposition levels for Normal and Hypertension classes.
Decomposition Levels | Frequency Range (Hz) | Mean Relative Energy (%) |
---|
Normal | Hypertension |
---|
| 250–500 | 0.07% | 0.59% |
| 122–256 | 0.09% | 0.32% |
| 61.1–128 | 0.19% | 0.35% |
| 30.6–63.9 | 0.46% | 0.49% |
| 15.3–31.9 | 1.93% | 3.10% |
| 7.65–16 | 14.77% | 13.31% |
| 3.84–7.97 | 31.03% | 21.49% |
| 1.94–3.99 | 29.11% | 19.05% |
| 1.03–1.99 | 21.11% | 26.77% |
| 0.594–0.958 | 0.16% | 7.65% |
| 0–0.431 | 1.08% | 6.88% |
| 0–31.2 | 99.19% | 98.26% |
Table 5.
Comparison of mean relative energies and frequency ranges of various intrinsic mode functions (EMD) for Normal and Hypertension classes. Bold font indicates the selected components.
Table 5.
Comparison of mean relative energies and frequency ranges of various intrinsic mode functions (EMD) for Normal and Hypertension classes. Bold font indicates the selected components.
Components | Normal | Hypertension |
---|
Mean Frequency Range (Hz) | Mean Relative Energy (%) | Mean Frequency Range (Hz) | Mean Relative Energy (%) |
---|
| 103–483 | 0.00 | 86.5–484 | 1.02 |
| 11.3–60.2 | 0.14 | 40.7–219 | 0.35 |
| 3.09–14 | 30.34 | 3.3–61 | 2.04 |
| 2.99–12.2 | 4.97 | 3.34–23.3 | 6.65 |
| 1.28–10 | 22.76 | 2.98–11.4 | 14.95 |
Residual | 0.129–5.55 | 41.78 | 0.0197–4.33 | 74.99 |
Table 6.
Statistical data of all extracted features for both methods.
Table 6.
Statistical data of all extracted features for both methods.
| Method I | Method II |
---|
Feature | Normal | Hypertension | Normal | Hypertension |
Mean | STD | Mean | STD | Mean | STD | Mean | STD |
Mean | 0.008 | 0.027 | 0.017 | 0.031 | 0.001 | 0.049 | 0.013 | 0.053 |
Standard Deviation | 0.253 | 0.032 | 0.250 | 0.046 | 0.254 | 0.032 | 0.248 | 0.045 |
Skewness | −1.959 | 0.522 | −2.144 | 0.651 | −1.997 | 0.576 | −2.220 | 0.641 |
Kurtosis | 6.993 | 1.499 | 8.083 | 3.864 | 7.139 | 1.688 | 8.297 | 3.944 |
Peak to Peak Value | 1.380 | 0.199 | 1.398 | 0.140 | 1.377 | 0.178 | 1.375 | 0.144 |
Root Mean Square | 0.255 | 0.033 | 0.252 | 0.047 | 0.258 | 0.038 | 0.254 | 0.046 |
Crest Factor | 1.659 | 0.939 | 1.635 | 0.381 | 1.587 | 0.991 | 1.523 | 0.536 |
Shape Factor | 1.484 | 0.134 | 1.458 | 0.159 | 1.522 | 0.163 | 1.465 | 0.167 |
Impulse Factor | 2.435 | 1.297 | 2.382 | 0.593 | 2.371 | 1.376 | 2.171 | 0.632 |
Margin Factor | 15.22 | 14.64 | 15.12 | 7.27 | 15.16 | 15.56 | 13.39 | 6.45 |
Energy | 389.6 | 209.7 | 437.4 | 207.6 | 393.9 | 207.4 | 448.5 | 222.5 |
Peak to RMS Value | 3.894 | 0.623 | 4.094 | 0.991 | 3.921 | 0.663 | 4.108 | 1.102 |
Root Sum of Squares | 18.933 | 5.600 | 20.292 | 5.089 | 19.069 | 5.525 | 20.480 | 5.412 |
Shannon Energy | 549.7 | 312.3 | 618.2 | 279.7 | 526.6 | 295.8 | 686.5 | 414.4 |
Log Energy | −27,888 | 15,515 | −34,569 | 18,644 | −28,509 | 16,289 | −32,613 | 16,387 |
Mean Absolute Deviation | 0.169 | 0.026 | 0.169 | 0.043 | 0.169 | 0.028 | 0.168 | 0.043 |
Median Absolute Deviation | 0.074 | 0.026 | 0.071 | 0.030 | 0.071 | 0.033 | 0.059 | 0.024 |
Average Frequency | 0.002 | 0.001 | 0.001 | 0.001 | 0.002 | 0.003 | 0.002 | 0.002 |
Jitter | 137.1 | 180.9 | 85.5 | 150.4 | 159.0 | 248.8 | 52.0 | 95.8 |
Spectral Mean | 3.144 | 5.928 | 0.771 | 1.960 | 8.623 | 15.700 | 2.764 | 6.372 |
Spectral Standard Deviation | 3.401 | 4.833 | 1.549 | 3.463 | 7.571 | 11.498 | 3.031 | 6.016 |
Spectral Skewness | 2.361 | 1.496 | 3.763 | 1.295 | 0.996 | 1.360 | 1.797 | 1.908 |
Specral Kurtosis | 11.331 | 8.235 | 20.190 | 9.589 | 5.588 | 4.947 | 10.564 | 8.751 |
Spectral Centroid | 9.442 | 0.202 | 9.915 | 1.481 | 9.771 | 1.576 | 10.768 | 2.216 |
Spectral Flux | 0.008 | 0.002 | 0.008 | 0.002 | 0.008 | 0.002 | 0.008 | 0.002 |
Spectral Roll-off | 91.699 | 1.010 | 96.587 | 8.383 | 97.036 | 10.371 | 136.257 | 44.372 |
Spectral Flatness | 0.025 | 0.014 | 0.051 | 0.035 | 0.063 | 0.065 | 0.171 | 0.138 |
Spectral Crest | 0.642 | 0.012 | 0.621 | 0.040 | 0.631 | 0.030 | 0.593 | 0.059 |
Spectral Decrease | −4.333 | 0.228 | −4.013 | 0.658 | −4.169 | 0.538 | −3.656 | 0.830 |
Spectral Slope | −0.023 | 0.003 | −0.023 | 0.003 | −0.024 | 0.003 | −0.023 | 0.005 |
Spectral Spread | 18.505 | 0.199 | 19.029 | 1.178 | 19.328 | 1.617 | 23.521 | 5.818 |
Mean Frequency | 4.347 | 0.949 | 4.999 | 3.451 | 4.989 | 3.063 | 6.446 | 4.440 |
Median Frequency | 3.574 | 0.803 | 2.972 | 0.880 | 3.811 | 3.573 | 2.846 | 0.753 |
Spurious-free Dynamic Range | 3.073 | 6.019 | 2.196 | 2.107 | 3.163 | 6.451 | 2.056 | 1.812 |
Signal to Noise Distortion | −0.885 | 6.041 | −2.036 | 3.101 | −0.755 | 6.819 | −2.097 | 3.030 |
Total Harmonic Distortions | −2.376 | 5.452 | −0.938 | 4.964 | −3.144 | 6.562 | −0.396 | 4.110 |
1st Coeffient of MFCC | −44.99 | 0.37 | −44.716 | 0.512 | −44.84 | 0.60 | −44.45 | 0.80 |
2nd Coeffient of MFCC | 6.268 | 0.523 | 6.661 | 0.714 | 6.480 | 0.846 | 7.028 | 1.122 |
3rd Coeffient of MFCC | 5.976 | 0.499 | 6.350 | 0.683 | 6.169 | 0.802 | 6.690 | 1.066 |
4th Coeffient of MFCC | 5.508 | 0.462 | 5.851 | 0.634 | 5.671 | 0.733 | 6.148 | 0.976 |
1st Coeffient of GFCC | −7.183 | 0.430 | −6.762 | 0.610 | −7.027 | 0.793 | −6.220 | 1.266 |
2nd Coeffient of GFCC | 1.844 | 0.063 | 1.869 | 0.071 | 1.522 | 0.419 | 1.119 | 0.574 |
3rd Coeffient of GFCC | 0.553 | 0.138 | 0.367 | 0.269 | 0.643 | 0.105 | 0.492 | 0.206 |
4th Coeffient of GFCC | 0.301 | 0.024 | 0.266 | 0.033 | 0.392 | 0.109 | 0.408 | 0.109 |
1st Coefficient of Chroma Vector | 0.383 | 0.235 | 0.750 | 0.501 | 0.653 | 0.532 | 2.126 | 1.930 |
2nd Coefficient of Chroma Vector | 0.416 | 0.258 | 0.773 | 0.518 | 0.663 | 0.546 | 2.130 | 1.948 |
3rd Coefficient of Chroma Vector | 0.433 | 0.269 | 0.842 | 0.575 | 0.742 | 0.672 | 2.129 | 2.144 |
4th Coefficient of Chroma Vector | 0.623 | 0.378 | 1.297 | 0.942 | 0.700 | 0.568 | 2.011 | 2.046 |
5th Coefficient of Chroma Vector | 0.564 | 0.337 | 1.212 | 0.872 | 0.691 | 0.534 | 2.044 | 1.935 |
6th Coefficient of Chroma Vector | 0.527 | 0.320 | 1.230 | 0.987 | 0.748 | 0.563 | 2.227 | 2.267 |
7th Coefficient of Chroma Vector | 0.483 | 0.296 | 1.069 | 0.760 | 0.705 | 0.524 | 2.107 | 1.893 |
8th Coefficient of Chroma Vector | 0.451 | 0.279 | 0.982 | 0.696 | 0.686 | 0.528 | 2.071 | 1.863 |
9th Coefficient of Chroma Vector | 0.429 | 0.268 | 0.908 | 0.638 | 0.679 | 0.529 | 2.099 | 1.929 |
10th Coefficient of Chroma Vector | 0.400 | 0.251 | 0.878 | 0.609 | 0.651 | 0.537 | 2.087 | 1.848 |
11th Coefficient of Chroma Vector | 0.373 | 0.232 | 0.776 | 0.537 | 0.668 | 0.522 | 2.106 | 1.954 |
12th Coefficient of Chroma Vector | 0.348 | 0.225 | 0.705 | 0.474 | 0.622 | 0.522 | 2.078 | 1.890 |
Enhanced Mean Absolute Value | 0.297 | 0.039 | 0.302 | 0.055 | 0.294 | 0.052 | 0.301 | 0.049 |
Enhanced Wavelength | 236.4 | 133.5 | 413.7 | 319.8 | 284.3 | 198.6 | 665.7 | 515.0 |
Wavelength | 36.83 | 21.94 | 85.59 | 96.94 | 54.42 | 46.94 | 193.47 | 186.80 |
Slope Sign Change | 45.1 | 86.9 | 508.4 | 549.0 | 1039.5 | 1657.6 | 3463.1 | 2792.1 |
Average Amplitude Change | 0.006 | 0.003 | 0.010 | 0.009 | 0.009 | 0.009 | 0.021 | 0.018 |
Difference Absolute Std. Dev. | 0.009 | 0.003 | 0.013 | 0.010 | 0.014 | 0.010 | 0.027 | 0.021 |
Log Detector | 0.108 | 0.030 | 0.118 | 0.037 | 0.107 | 0.043 | 0.117 | 0.036 |
Modified Mean Absolute Value | 0.130 | 0.025 | 0.133 | 0.034 | 0.130 | 0.033 | 0.132 | 0.030 |
Modified Mean Absolute Value 2 | 0.083 | 0.022 | 0.089 | 0.026 | 0.084 | 0.027 | 0.087 | 0.020 |
Pulse Percentage Rate | 0.939 | 0.029 | 0.953 | 0.027 | 0.937 | 0.045 | 0.957 | 0.035 |
Simple Square Integral | 389.6 | 209.7 | 437.4 | 207.6 | 393.9 | 207.4 | 448.5 | 222.5 |
Willison Amplitude | 1153.6 | 765.6 | 2670.8 | 2603.9 | 1836.0 | 1707.6 | 4551.3 | 3502.4 |
Maximum Fractal Length | −0.463 | 0.384 | −0.136 | 0.740 | −0.174 | 0.594 | 0.428 | 1.121 |
Root Squared Zero Order Moment | 2.592 | 0.032 | 2.600 | 0.026 | 2.593 | 0.031 | 2.601 | 0.028 |
Root Squared 2nd Order Moment | 2.068 | 0.066 | 2.036 | 0.062 | 1.984 | 0.115 | 1.913 | 0.130 |
Root Squared 4th Order Moment | 2.045 | 0.077 | 2.001 | 0.078 | 1.891 | 0.159 | 1.794 | 0.205 |
Sparseness | 0.535 | 0.064 | 0.582 | 0.086 | 0.655 | 0.137 | 0.747 | 0.188 |
Irregularity Factor | −0.464 | 0.037 | −0.445 | 0.047 | −0.446 | 0.058 | −0.406 | 0.061 |
Waveform Length Ratio | −0.065 | 0.703 | −0.354 | 0.230 | −0.648 | 0.604 | −0.721 | 0.727 |
Complexity | 0.502 | 0.222 | 0.706 | 0.253 | 0.897 | 0.524 | 1.314 | 0.515 |
Mobility | 0.038 | 0.011 | 0.057 | 0.038 | 0.055 | 0.035 | 0.115 | 0.079 |
Higuchi’s Fractal Dimension | 1.054 | 0.052 | 1.149 | 0.119 | 1.183 | 0.240 | 1.490 | 0.401 |
Katz Fractal Dimension | 1.000 | 0.000 | 1.000 | 0.000 | 1.000 | 0.000 | 1.000 | 0.000 |
Lyapunov Exponent | 437.4 | 49.5 | 394.8 | 49.3 | 362.9 | 78.8 | 239.1 | 92.2 |
Approximate Entropy | 0.104 | 0.049 | 0.155 | 0.134 | 0.127 | 0.080 | 0.316 | 0.312 |
Correlation Dimension | 1.687 | 0.168 | 1.733 | 0.191 | 1.676 | 0.202 | 1.731 | 0.300 |
1st Coefficient of LTP | 259.6 | 128.8 | 291.5 | 122.1 | 259.6 | 128.8 | 291.5 | 122.1 |
2nd Coefficient of LTP | 40.790 | 30.943 | 56.809 | 35.998 | 40.790 | 30.943 | 56.809 | 35.998 |
3rd Coefficient of LTP | 24.574 | 23.717 | 41.723 | 32.083 | 24.574 | 23.717 | 41.723 | 32.083 |
4th Coefficient of LTP | 16.381 | 15.761 | 30.738 | 22.667 | 16.381 | 15.761 | 30.738 | 22.667 |
5th Coefficient of LTP | 18.472 | 15.755 | 33.411 | 24.881 | 18.472 | 15.755 | 33.411 | 24.881 |
6th Coefficient of LTP | 25.205 | 21.607 | 45.518 | 33.715 | 25.205 | 21.607 | 45.518 | 33.715 |
7th Coefficient of LTP | 17.699 | 16.352 | 29.035 | 22.609 | 17.699 | 16.352 | 29.035 | 22.609 |
8th Coefficient of LTP | 26.261 | 23.075 | 39.645 | 28.386 | 26.261 | 23.075 | 39.645 | 28.386 |
9th Coefficient of LTP | 47.483 | 32.844 | 58.163 | 35.132 | 47.483 | 32.844 | 58.163 | 35.132 |
10th Coefficient of LTP | 207.278 | 94.769 | 201.809 | 72.537 | 207.278 | 94.769 | 201.809 | 72.537 |
11th Coefficient of LTP | 269.5 | 137.7 | 302.1 | 121.7 | 269.5 | 137.70 | 302.1 | 121.7 |
12th Coefficient of LTP | 41.733 | 31.354 | 59.773 | 38.092 | 41.733 | 31.354 | 59.773 | 38.092 |
13th Coefficient of LTP | 24.006 | 23.096 | 41.071 | 31.294 | 24.006 | 23.096 | 41.071 | 31.294 |
14th Coefficient of LTP | 16.784 | 15.900 | 30.199 | 23.471 | 16.784 | 15.900 | 30.199 | 23.471 |
15th Coefficient of LTP | 18.506 | 15.140 | 33.390 | 25.583 | 18.506 | 15.140 | 33.390 | 25.583 |
16th Coefficient of LTP | 26.148 | 22.755 | 44.177 | 31.682 | 26.148 | 22.755 | 44.177 | 31.682 |
17th Coefficient of LTP | 16.898 | 16.559 | 30.149 | 21.910 | 16.898 | 16.559 | 30.149 | 21.910 |
18th Coefficient of LTP | 25.790 | 22.892 | 39.312 | 26.784 | 25.790 | 22.892 | 39.312 | 26.784 |
19th Coefficient of LTP | 46.534 | 33.045 | 56.298 | 33.647 | 46.534 | 33.045 | 56.298 | 33.647 |
20th Coefficient of LTP | 197.773 | 85.090 | 191.858 | 74.487 | 197.773 | 85.090 | 191.858 | 74.487 |
Table 7.
List of features extracted for method I and sorted with respect to mean rank (MR) value. Bold font indicates the top 24 ranked features.
Table 7.
List of features extracted for method I and sorted with respect to mean rank (MR) value. Bold font indicates the top 24 ranked features.
Feature | TT | KLD | BD | ROC | MWT | MRMR | RRF | MR |
---|
3rd Coefficient of LTP | 12 | 93 | 93 | 83 | 32 | 102 | 98 | 73.29 |
6th Coefficient of Chroma Vector | 38 | 98 | 76 | 79 | 78 | 94 | 49 | 73.14 |
Lyapunov Exponent | 93 | 92 | 92 | 70 | 81 | 10 | 70 | 72.57 |
Sparseness | 70 | 90 | 90 | 30 | 62 | 79 | 84 | 72.14 |
Jitter | 78 | 58 | 58 | 74 | 77 | 85 | 74 | 72.00 |
9th Coefficient of LTP | 57 | 83 | 83 | 75 | 82 | 92 | 24 | 70.86 |
Spectral Decrease | 62 | 28 | 61 | 85 | 75 | 93 | 83 | 69.57 |
4th Coefficient of MFCC | 96 | 41 | 41 | 95 | 40 | 72 | 99 | 69.14 |
Irregularity | 35 | 94 | 100 | 57 | 88 | 90 | 13 | 68.14 |
1st Coeffient of MFCC | 99 | 23 | 23 | 100 | 37 | 96 | 97 | 67.86 |
Waveform Length Ratio | 13 | 100 | 94 | 91 | 9 | 100 | 66 | 67.57 |
3rd Coeffient of MFCC | 79 | 97 | 97 | 84 | 39 | 55 | 17 | 66.86 |
1st Coefficient of Chroma Vector | 71 | 86 | 86 | 98 | 26 | 48 | 53 | 66.86 |
Spectral Roll-off | 81 | 43 | 79 | 66 | 56 | 59 | 81 | 66.43 |
Spectral Crest | 61 | 80 | 60 | 26 | 92 | 71 | 75 | 66.43 |
6th Coefficient of Chroma Vector | 100 | 76 | 99 | 31 | 61 | 45 | 52 | 66.29 |
7th Coefficient of Chroma Vector | 37 | 99 | 98 | 1 | 96 | 42 | 86 | 65.57 |
Median Frequency | 88 | 35 | 78 | 71 | 102 | 78 | 2 | 64.86 |
2nd Coefficient of Chroma Vector | 89 | 7 | 96 | 89 | 41 | 81 | 51 | 64.86 |
Spectral Centroid | 76 | 79 | 43 | 39 | 86 | 69 | 58 | 64.29 |
Difference Absolute Std. Dev. Value | 84 | 38 | 37 | 81 | 79 | 63 | 64 | 63.71 |
Shape Factor | 59 | 53 | 53 | 54 | 72 | 80 | 73 | 63.43 |
Spectral Mean | 43 | 77 | 77 | 96 | 46 | 40 | 61 | 62.86 |
Simple Square Integral | 4 | 72 | 72 | 22 | 89 | 89 | 92 | 62.86 |
3rd Coefficient of GFCC | 95 | 29 | 88 | 94 | 34 | 53 | 45 | 62.57 |
4th Coefficient of Chroma Vector | 40 | 96 | 30 | 87 | 85 | 52 | 47 | 62.43 |
Root Mean Square | 45 | 56 | 56 | 77 | 43 | 87 | 69 | 61.86 |
Signal to Noise Distortion | 97 | 69 | 69 | 47 | 74 | 31 | 42 | 61.29 |
9th Coefficient of Chroma Vector | 72 | 12 | 95 | 88 | 27 | 76 | 57 | 61.00 |
Mean Absolute Deviation | 58 | 48 | 48 | 49 | 76 | 82 | 62 | 60.43 |
Root Squared 2nd Order Moment | 91 | 82 | 71 | 8 | 59 | 20 | 91 | 60.29 |
Root Squared 4th Order Moment | 101 | 71 | 82 | 82 | 4 | 66 | 14 | 60.00 |
10th Coefficient of Chroma Vector | 36 | 95 | 85 | 28 | 73 | 41 | 56 | 59.14 |
12th Coefficient of Chroma Vector | 90 | 89 | 89 | 59 | 10 | 37 | 38 | 58.86 |
1st Coefficient of GFCC | 69 | 87 | 87 | 61 | 66 | 38 | 3 | 58.71 |
Mean | 80 | 26 | 26 | 80 | 80 | 68 | 44 | 57.71 |
Enhanced Mean Absolute Value | 21 | 73 | 73 | 62 | 58 | 54 | 63 | 57.71 |
Root Sum of Squares | 53 | 49 | 52 | 99 | 18 | 58 | 72 | 57.29 |
2nd Coefficient of LTP | 82 | 70 | 70 | 35 | 36 | 12 | 95 | 57.14 |
Katz Fractal Dimension | 67 | 3 | 3 | 67 | 90 | 75 | 90 | 56.43 |
Table 8.
List of features extracted for method II and sorted with respect to MR value. Bold font indicates the top 24 ranked features.
Table 8.
List of features extracted for method II and sorted with respect to MR value. Bold font indicates the top 24 ranked features.
Feature | TT | KLD | BD | ROC | MWT | MRMR | RRF | MR |
---|
7th Coefficient of Chroma Vector | 100 | 98 | 98 | 26 | 96 | 97 | 55 | 81.43 |
4th Coefficient of Chroma Vector | 89 | 96 | 76 | 73 | 85 | 52 | 96 | 81.00 |
Mobility | 93 | 64 | 64 | 67 | 68 | 91 | 95 | 77.43 |
Spectral Centroid | 68 | 78 | 78 | 77 | 45 | 98 | 88 | 76.00 |
Enhanced Mean Absolute Value | 71 | 95 | 95 | 28 | 98 | 56 | 87 | 75.71 |
9th Coefficient of LTP | 64 | 101 | 101 | 64 | 93 | 79 | 19 | 74.43 |
1st Coefficient of GFCC | 95 | 35 | 97 | 98 | 38 | 99 | 54 | 73.71 |
7th Coefficient of LTP | 57 | 93 | 93 | 57 | 101 | 24 | 89 | 73.43 |
Slope Sign Change | 74 | 73 | 89 | 79 | 89 | 89 | 13 | 72.29 |
Maximum Fractal Length | 3 | 72 | 74 | 91 | 90 | 90 | 70 | 70.00 |
3rd Coefficient of MFCC | 38 | 97 | 22 | 95 | 102 | 80 | 53 | 69.57 |
6th Coefficient of Chroma Vector | 69 | 80 | 96 | 90 | 27 | 68 | 56 | 69.43 |
8th Coefficient of Chroma Vector | 61 | 99 | 99 | 69 | 66 | 49 | 40 | 69.00 |
Enhanced Wavelength | 81 | 85 | 85 | 68 | 94 | 16 | 50 | 68.43 |
Pulse Percentage Rate | 4 | 94 | 100 | 42 | 73 | 76 | 77 | 66.57 |
Root Squared Zero Order Moment | 63 | 84 | 84 | 30 | 69 | 72 | 46 | 64.00 |
Crest Factor | 51 | 68 | 68 | 80 | 72 | 32 | 72 | 63.29 |
Modified Mean Absolute Value 2 | 42 | 90 | 90 | 7 | 88 | 64 | 62 | 63.29 |
Spectral Crest | 87 | 58 | 45 | 40 | 47 | 85 | 75 | 62.43 |
2nd Coeffient of MFCC | 37 | 69 | 69 | 84 | 77 | 48 | 51 | 62.14 |
1st Coeffient of MFCC | 77 | 9 | 9 | 100 | 99 | 38 | 102 | 62.00 |
Average Frequency | 80 | 29 | 54 | 47 | 54 | 86 | 82 | 61.71 |
4th Coefficient of LTP | 34 | 65 | 65 | 65 | 42 | 74 | 84 | 61.29 |
Willison Amplitude | 91 | 74 | 72 | 15 | 16 | 77 | 83 | 61.14 |
Spectral Spread | 99 | 47 | 58 | 85 | 60 | 9 | 68 | 60.86 |
3rd Coefficient of LTP | 65 | 91 | 91 | 5 | 65 | 2 | 101 | 60.00 |
Root Squared 4th Order Moment | 70 | 66 | 71 | 14 | 97 | 62 | 36 | 59.43 |
Lyapunov Exponent | 32 | 63 | 63 | 8 | 62 | 81 | 100 | 58.43 |
2nd Coeffient of GFCC | 40 | 87 | 87 | 89 | 40 | 50 | 14 | 58.14 |
3rd Coeffient of GFCC | 73 | 22 | 10 | 66 | 39 | 96 | 99 | 57.86 |
Correlation Dimension | 14 | 70 | 70 | 101 | 57 | 1 | 91 | 57.71 |
Root Squared 2nd Order Moment | 101 | 5 | 66 | 36 | 31 | 100 | 61 | 57.14 |
5th Coefficient of Chroma Vector | 20 | 76 | 80 | 97 | 4 | 36 | 86 | 57.00 |
2nd Coefficient of Chroma Vector | 75 | 41 | 41 | 88 | 78 | 73 | 2 | 56.86 |
11th Coefficient of Chroma Vector | 66 | 39 | 39 | 74 | 95 | 47 | 37 | 56.71 |
Log Energy | 45 | 75 | 52 | 53 | 19 | 88 | 60 | 56.00 |
10th Coefficient of Chroma Vector | 90 | 38 | 38 | 19 | 100 | 69 | 38 | 56.00 |
5th Coefficient of LTP | 8 | 82 | 82 | 83 | 5 | 101 | 30 | 55.86 |
1st Coefficient of LTP | 11 | 92 | 92 | 24 | 83 | 57 | 31 | 55.71 |
Table 9.
Consolidated result analysis of feature sets (, , ) for method I with various classifiers.
Table 9.
Consolidated result analysis of feature sets (, , ) for method I with various classifiers.
Classifier | (5 Components) | (7 Components) | (10 Components) |
---|
Acc | Sp | Sen | Err | Acc | Sp | Sen | Err | Acc | Sp | Sen | Err |
---|
DT | 0.874 | 0.89 | 0.89 | 0.126 | 0.924 | 0.92 | 0.93 | 0.076 | 0.934 | 0.94 | 0.93 | 0.066 |
LD | 0.688 | 0.3 | 1 | 0.312 | 0.688 | 0.3 | 1 | 0.312 | 0.669 | 0.3 | 0.97 | 0.331 |
LR | 0.688 | 0.3 | 1 | 0.312 | 0.688 | 0.3 | 1 | 0.312 | 0.688 | 0.3 | 1 | 0.312 |
NBG | 0.688 | 0.3 | 1 | 0.312 | 0.688 | 0.3 | 1 | 0.312 | 0.59 | 1 | 0.26 | 0.41 |
NBK | 0.804 | 0.91 | 0.72 | 0.196 | 0.83 | 0.92 | 0.76 | 0.17 | 0.893 | 0.91 | 0.88 | 0.107 |
SVM-L | 0.479 | 0.56 | 0.41 | 0.521 | 0.587 | 0.11 | 0.97 | 0.413 | 0.498 | 0.37 | 0.6 | 0.502 |
SVM-Q | 0.527 | 0.65 | 0.43 | 0.473 | 0.527 | 0.08 | 0.89 | 0.473 | 0.546 | 0.41 | 0.65 | 0.454 |
SVM-C | 0.47 | 0.4 | 0.53 | 0.53 | 0.555 | 0.03 | 0.98 | 0.445 | 0.524 | 0 | 0.94 | 0.476 |
SVM-FG | 0.688 | 0.3 | 1 | 0.312 | 0.688 | 0.30 | 1 | 0.312 | 0.688 | 0.3 | 1 | 0.312 |
SVM-MG | 0.688 | 0.3 | 1 | 0.312 | 0.688 | 0.30 | 1 | 0.312 | 0.688 | 0.3 | 1 | 0.312 |
KNN-F | 0.937 | 0.9 | 0.97 | 0.063 | 0.972 | 0.96 | 0.98 | 0.028 | 0.984 | 0.97 | 0.99 | 0.016 |
KNN-M | 0.792 | 0.68 | 0.88 | 0.208 | 0.864 | 0.81 | 0.91 | 0.136 | 0.905 | 0.86 | 0.94 | 0.095 |
KNN-Cos | 0.685 | 0.3 | 0.99 | 0.315 | 0.681 | 0.3 | 0.99 | 0.319 | 0.685 | 0.3 | 0.99 | 0.315 |
KNN-C | 0.672 | 0.68 | 0.66 | 0.328 | 0.871 | 0.83 | 0.9 | 0.129 | 0.896 | 0.84 | 0.94 | 0.104 |
KNN-W | 0.921 | 0.88 | 0.95 | 0.079 | 0.965 | 0.96 | 0.97 | 0.035 | 0.978 | 0.97 | 0.98 | 0.022 |
Eboost | 0.918 | 0.89 | 0.94 | 0.082 | 0.864 | 0.74 | 0.96 | 0.136 | 0.555 | 0 | 1 | 0.445 |
EBT | 0.688 | 0.3 | 1 | 0.312 | 0.972 | 0.95 | 0.99 | 0.028 | 0.943 | 0.93 | 0.95 | 0.057 |
ESD | 0.94 | 0.92 | 0.95 | 0.06 | 0.688 | 0.3 | 1 | 0.312 | 0.681 | 0.3 | 0.99 | 0.319 |
ESKNN | 0.915 | 0.91 | 0.91 | 0.085 | 0.984 | 0.98 | 0.99 | 0.016 | 0.981 | 0.97 | 0.99 | 0.019 |
Table 10.
Consolidated result analysis of feature sets (, , ) for method I with various classifiers. Bold font indicates best results.
Table 10.
Consolidated result analysis of feature sets (, , ) for method I with various classifiers. Bold font indicates best results.
Classifier | (12 Components) | (15 Components) | (17 Components) |
---|
Acc | Sp | Sen | Err | Acc | Sp | Sen | Err | Acc | Sp | Sen | Err |
---|
DT | 0.959 | 0.96 | 0.96 | 0.041 | 0.959 | 0.94 | 0.98 | 0.041 | 0.972 | 0.96 | 98 | 0.028 |
LD | 0.581 | 0.3 | 0.99 | 0.419 | 0.662 | 0.3 | 0.95 | 0.338 | 0.691 | 0.32 | 0.99 | 0.309 |
LR | 0.688 | 0.3 | 1 | 0.312 | 0.688 | 0.3 | 1 | 0.312 | 0.675 | 0.35 | 0.94 | 0.325 |
NBG | 0.59 | 1 | 0.26 | 0.41 | 0.625 | 1 | 0.32 | 0.375 | 0.631 | 1 | 0.34 | 0.369 |
NBK | 0.868 | 0.85 | 0.89 | 0.132 | 0.877 | 0.92 | 0.84 | 0.123 | 0.88 | 0.91 | 0.95 | 0.12 |
SVM-L | 0.524 | 0.26 | 0.74 | 0.476 | 0.543 | 0.12 | 0.88 | 0.457 | 0.536 | 0.07 | 0.91 | 0.464 |
SVM-Q | 0.552 | 0.22 | 0.82 | 0.448 | 0.536 | 0.33 | 0.7 | 0.464 | 0.546 | 0.02 | 0.97 | 0.454 |
SVM-C | 0.524 | 0.8 | 0.88 | 0.476 | 0.517 | 0 | 0.93 | 0.483 | 0.514 | 0.05 | 0.89 | 0.486 |
SVM-FG | 0.688 | 0.3 | 1 | 0.312 | 0.688 | 0.3 | 1 | 0.312 | 0.7 | 0.33 | 1 | 0.3 |
SVM-MG | 0.665 | 0.3 | 1 | 0.335 | 0.688 | 0.3 | 1 | 0.312 | 0.694 | 0.31 | 1 | 0.306 |
KNN-F | 0.981 | 0.97 | 0.99 | 0.019 | 0.975 | 0.97 | 0.98 | 0.025 | 0.915 | 0.88 | 0.94 | 0.085 |
KNN-M | 0.918 | 0.85 | 0.97 | 0.082 | 0.912 | 0.85 | 0.96 | 0.088 | 0.659 | 0.7 | 0.63 | 0.341 |
KNN-Cos | 0.688 | 0.3 | 1 | 0.312 | 0.688 | 0.3 | 1 | 0.312 | 0.685 | 0.3 | 0.99 | 0.315 |
KNN-C | 0.905 | 0.85 | 0.95 | 0.095 | 0.909 | 0.85 | 0.95 | 0.091 | 0.909 | 0.89 | 0.93 | 0.091 |
KNN-W | 0.981 | 0.98 | 0.98 | 0.019 | 0.975 | 0.97 | 0.98 | 0.025 | 0.978 | 0.97 | 0.98 | 0.022 |
Eboost | 0.555 | 0 | 1 | 0.445 | 0.555 | 0 | 1 | 0.445 | 0.555 | 0 | 1 | 0.445 |
EBT | 0.965 | 0.94 | 0.98 | 0.035 | 0.972 | 0.97 | 0.97 | 0.028 | 0.94 | 0.92 | 0.95 | 0.06 |
ESD | 0.688 | 0.3 | 1 | 0.312 | 0.666 | 0.3 | 0.96 | 0.334 | 0.681 | 0.3 | 0.99 | 0.319 |
ESKNN | 0.984 | 0.97 | 0.99 | 0.016 | 0.975 | 0.97 | 0.98 | 0.025 | 0.981 | 0.99 | 0.99 | 0.019 |
Table 11.
Validation of the selected scheme of method I.
Table 11.
Validation of the selected scheme of method I.
Evaluation | Classes | Accuracy | True Positive Rate | False Negative Rate |
---|
5-Fold Cross-Validation | Healthy | 0.983 | 0.98 | 0.02 |
Hypertension | 0.99 | 0.01 |
10-Fold Cross-Validation | Healthy | 0.984 | 0.98 | 0.02 |
Hypertension | 0.99 | 0.01 |
15-Fold Cross-Validation | Healthy | 0.984 | 0.98 | 0.02 |
Hypertension | 0.99 | 0.01 |
20-Fold Cross-Validation | Healthy | 0.984 | 0.98 | 0.02 |
Hypertension | 0.99 | 0.01 |
20% Hold Out Validation | Healthy | 0.978 | 1 | 0 |
Hypertension | 0.94 | 0.06 |
25% Hold Out Validation | Healthy | 0.989 | 0.98 | 0.02 |
Hypertension | 1 | 0 |
Table 12.
Feature analysis table (, , ) for method II. Bold font indicates the best results.
Table 12.
Feature analysis table (, , ) for method II. Bold font indicates the best results.
Classifier | (5 Components) | (7 Components) | (10 Components) |
---|
Acc | Sp | Sen | Err | Acc | Sp | Sen | Err | Acc | Sp | Sen | Err |
---|
DT | 0.974 | 0.98 | 0.97 | 0.026 | 0.983 | 0.98 | 0.99 | 0.017 | 0.989 | 0.98 | 0.99 | 0.011 |
LD | 0.619 | 0.24 | 1 | 0.381 | 0.619 | 0.24 | 1 | 0.381 | 0.568 | 0.24 | 0.9 | 0.432 |
LR | 0.679 | 0.97 | 0.39 | 0.321 | 0.679 | 0.97 | 0.39 | 0.321 | 0.679 | 0.97 | 0.39 | 0.321 |
NBG | 0.619 | 0.24 | 1 | 0.381 | 0.619 | 0.24 | 1 | 0.381 | 0.268 | 1 | 0.26 | 0.732 |
NBK | 0.946 | 0.97 | 0.92 | 0.054 | 0.946 | 0.98 | 0.91 | 0.054 | 0.94 | 0.97 | 0.91 | 0.06 |
SVM-L | 0.48 | 0.49 | 0.47 | 0.52 | 0.497 | 0.59 | 0.41 | 0.503 | 0.523 | 0.52 | 0.53 | 0.477 |
SVM-Q | 0.51 | 0.53 | 0.5 | 0.49 | 0.511 | 0.24 | 0.78 | 0.489 | 0.497 | 0.46 | 0.53 | 0.503 |
SVM-C | 0.49 | 0.3 | 0.69 | 0.51 | 0.491 | 0.22 | 0.77 | 0.509 | 0.491 | 0.23 | 0.76 | 0.509 |
SVM-FG | 0.668 | 1 | 0.34 | 0.332 | 0.662 | 1 | 0.32 | 0.338 | 0.665 | 0.99 | 0.34 | 0.335 |
SVM-MG | 0.619 | 0.24 | 1 | 0.381 | 0.614 | 0.51 | 0.72 | 0.386 | 0.597 | 0.73 | 0.46 | 0.403 |
KNN-F | 0.99 | 0.99 | 0 | 0.01 | 0.893 | 0.98 | 0.984 | 0.107 | 0.991 | 0.99 | 0.99 | 0.009 |
KNN-M | 0.957 | 0.93 | 0.98 | 0.043 | 0.969 | 0.95 | 0.99 | 0.031 | 0.972 | 0.95 | 0.99 | 0.028 |
KNN-Cos | 0.631 | 0.27 | 0.99 | 0.369 | 0.639 | 0.3 | 0.98 | 0.361 | 0.636 | 0.3 | 0.98 | 0.364 |
KNN-C | 0.957 | 0.93 | 0.98 | 0.043 | 0.966 | 0.94 | 0.99 | 0.034 | 0.969 | 0.94 | 0.99 | 0.031 |
KNN-W | 0.994 | 0.992 | 0.996 | 0.006 | 0.986 | 0.94 | 0.99 | 0.014 | 0.992 | 0.99 | 0.99 | 0.008 |
Eboost | 0.489 | 0.39 | 0.59 | 0.511 | 0.489 | 0.39 | 0.59 | 0.511 | 0.489 | 0.39 | 0.59 | 0.511 |
EBT | 0.98 | 0.97 | 0.99 | 0.02 | 0.986 | 0.98 | 0.99 | 0.014 | 0.986 | 0.98 | 0.99 | 0.014 |
ESD | 0.619 | 0.24 | 1 | 0.381 | 0.619 | 0.24 | 1 | 0.381 | 0.571 | 0.24 | 0.9 | 0.429 |
ESKNN | 0.991 | 0.99 | 0.99 | 0.009 | 0.983 | 0.99 | 0.98 | 0.017 | 0.991 | 0.99 | 0.99 | 0.009 |
Table 13.
Feature analysis table (, , ) for method II.
Table 13.
Feature analysis table (, , ) for method II.
Classifier | (12 Components) | (15 Components) | (17 Components) |
---|
Acc | Sp | Sen | Err | Acc | Sp | Sen | Err | Acc | Sp | Sen | Err |
---|
DT | 0.992 | 0.99 | 0.99 | 0.008 | 0.972 | 0.95 | 0.99 | 0.028 | 0.983 | 0.98 | 0.98 | 0.017 |
LD | 0.548 | 0.32 | 0.78 | 0.452 | 0.565 | 0.34 | 0.8 | 0.435 | 0.665 | 1 | 0.33 | 0.335 |
LR | 0.679 | 0.97 | 0.39 | 0.321 | 0.679 | 0.97 | 0.39 | 0.321 | 0.676 | 0.97 | 0.38 | 0.324 |
NBG | 0.636 | 0.97 | 0.39 | 0.364 | 0.662 | 1 | 0.32 | 0.338 | 0.665 | 1 | 0.33 | 0.335 |
NBK | 0.92 | 0.96 | 0.88 | 0.08 | 0.926 | 0.97 | 0.89 | 0.074 | 0.909 | 0.95 | 0.87 | 0.091 |
SVM-L | 0.531 | 0.27 | 0.79 | 0.469 | 0.486 | 0.23 | 0.74 | 0.514 | 0.5 | 0.24 | 0.76 | 0.5 |
SVM-Q | 0.503 | 0.19 | 0.82 | 0.497 | 0.469 | 0.15 | 0.79 | 0.531 | 0.514 | 0.15 | 0.88 | 0.486 |
SVM-C | 0.472 | 0 | 0.94 | 0.528 | 0.472 | 0.1 | 0.85 | 0.528 | 0.486 | 0 | 0.97 | 0.514 |
SVM-F | 0.662 | 1 | 0.32 | 0.338 | 0.662 | 1 | 0.32 | 0.338 | 0.696 | 1 | 0.39 | 0.304 |
SVM-MG | 0.665 | 1 | 0.33 | 0.335 | 0.662 | 1 | 0.32 | 0.338 | 0.696 | 1 | 0.39 | 0.304 |
KNN-F | 0.991 | 0.99 | 0.99 | 0.009 | 0.983 | 0.98 | 0.98 | 0.017 | 0.989 | 0.98 | 0.99 | 0.011 |
KNN-M | 0.949 | 0.94 | 0.96 | 0.051 | 0.96 | 0.94 | 0.98 | 0.04 | 0.94 | 0.97 | 0.91 | 0.06 |
KNN-Cos | 0.639 | 0.28 | 1 | 0.361 | 0.628 | 0.28 | 0.97 | 0.372 | 0.645 | 0.3 | 0.99 | 0.355 |
KNN-C | 0.946 | 0.94 | 0.95 | 0.054 | 0.963 | 0.94 | 0.98 | 0.037 | 0.94 | 0.98 | 0.9 | 0.06 |
KNN-W | 0.991 | 0.99 | 0.99 | 0.009 | 0.986 | 0.99 | 0.98 | 0.014 | 0.993 | 0.99 | 0.99 | 0.007 |
Eboost | 0.489 | 0.39 | 0.59 | 0.511 | 0.534 | 0.48 | 0.59 | 0.466 | 0.489 | 0.39 | 0.59 | 0.511 |
EBT | 0.989 | 0.98 | 0.99 | 0.011 | 0.966 | 0.97 | 0.96 | 0.034 | 0.986 | 0.99 | 0.98 | 0.014 |
ESD | 0.577 | 0.26 | 0.89 | 0.423 | 0.563 | 0.39 | 0.73 | 0.437 | 0.665 | 1 | 0.33 | 0.335 |
ESKNN | 0.991 | 0.99 | 0.99 | 0.009 | 0.983 | 0.98 | 0.98 | 0.017 | 0.991 | 0.99 | 0.99 | 0.009 |
Table 14.
Validation of the selected scheme of method II.
Table 14.
Validation of the selected scheme of method II.
Evaluation | Classes | Accuracy | True Positive Rate | False Negative Rate |
---|
5 Fold Cross-Validation | Healthy | 0.986 | 0.99 | 0.01 |
Hypertension | 0.98 | 0.02 |
10 Fold Cross-Validation | Healthy | 0.994 | 0.99 | 0.01 |
Hypertension | >0.99 | <0.01 |
15 Fold Cross-Validation | Healthy | 0.994 | 0.99 | 0.01 |
Hypertension | >0.99 | <0.01 |
20 Fold Cross-Validation | Healthy | 0.997 | 0.99 | 0.01 |
Hypertension | 1 | 0 |
20% Hold Out Validation | Healthy | 0.986 | 1 | 0 |
Hypertension | 0.97 | 0.03 |
25% Hold Out Validation | Healthy | 0.989 | 0.98 | 0.02 |
Hypertension | 1 | 0 |
Table 15.
Performance comparison of methods I and II.
Table 15.
Performance comparison of methods I and II.
Performance | Method I | Method II |
---|
Accuracy | 98.40% | 99.40% |
Sensitivity | 97.00% | 99.20% |
Specificity | 99.00% | 99.60% |
Error | 0.02% | 0.60% |
# of features | 12 | 5 |
Table 16.
Comparison with previous works.
Table 16.
Comparison with previous works.
Ref. | Modality | Preprocessing | Features | Feature Reduction | Classification | Data Set | Results |
---|
[12] | PPG | CWT | GoogLeNet | - | GoogLeNet | MIMIC | F1 score: 92.55% |
[13] | PPG and ECG | - | PAT and morphological features | - | KNN | MIMIC | F1 score: 94.84% |
[14] | HRV | - | Standard deviation of NN intervals | - | MIL | Self-collected data set Hypertension 24 and Normal: 19 | Accuracy: 85.47% |
[15] | ECG | SGF | Entropy features | - | SVM | Self-collected data set Hypertension: 61 and Normal: 67 | Accuracy: 93.33% |
[16] | HRV | - | Statistical, spectral, geometrical, wavelet, fractal, and non-linear features | PCA | QDA | Self-collected data set Hypertension: 41 Normal: 30 | Accuracy: 85.5% |
[17] | ECG | OWFB | Fractal dimension and energy features | Student’s t-test | Diagnosis index | PhysioNet database High-risk Hypertension: 17 subjects Low-risk Hypertension: 122 subjects Total: 139 subjects | 100% between low-risk and high-risk classes |
[18] | ECG | EMD | Entropy features | Student’s t-test | KNN classifier | MIT BIH Sinus rhythm database, SHAREE database: Normal: 18 signals Hypertension: 139 signals | Accuracy: 97.70% Sensitivity: 98.90% Specificity: 89.10% |
[19] | PPG | Chebyshev II | Time and morphological features | MRMR | KNN-W | Hypertension: 35 Normal: 48 Total: 83 | Positive Predictive Value: 100% Sensitivity: 85.71% F1-score: 92.31% |
[20] | BCG | | Morphological features | - | CAR | Self-collected data set Hypertension: 61 and Normal: 67 | Accuracy: 84.4% |
This study | PuPG | EMD | Time, frequency, cepstral, fractal, and chaotic features | HFSR | KNN-W | Self-collected data set Hypertension: 56 Normal: 65 | Accuracy: 99.7% Sensitivity: 99.2% Specificity: 99.4% |