Table 3.
Feature extraction and classification methods of PCG signals.
| Year | Author | Feature extraction methods | Classifier | Database | Result | ||
|---|---|---|---|---|---|---|---|
| 2019 | Shi et al. [33] | Feature extraction algorithm of Springer | AdaBoost | PhysioNet and PASCAL | ACC: 96.36% | ||
| 2019 | Nogueira et al. [34] | MFCC | SVM | PhysioNet | Sensitivity | Specificity | Accuracy |
| 91.87% | 82.05% | 97% | |||||
| 2019 | Cheng (without segmentation) [35] | Envelope autocorrelation | SVM | HSCT11 dataset | Accuracy all could reach to 100% | ||
| 2018 | Meintjes et al. [36] | CWT | SVM, kNN | PhysioNet | MAcc: 86% | ||
| 2018 | Hamidi et al. [37] | Curve fitting, MFCC | Euclidean distance | Dataset A from PhysioNet | MAcc: 92% | ||
| Dataset B from PhysioNet | MAcc: 81% | ||||||
| Dataset C from PhysioNet | MAcc: 98% | ||||||
| 2018 | Juniati et al. [38] | DWT | kNN, Fuzzy c-means clustering | 40 normal heart sounds, 40 extra systole, 40 murmurs | MAcc: 86.17% | ||
| 2017 | Kay et al. [39] | CWT, MFCC | BP neural networks | PhysioNet | MAcc: 85.2% | ||
| 2017 | Karar et al. [40] | DWT | Rule-based classification tree | 22 sets of heart sounds and noise data from the public database of the CliniSurf medical school | MAcc: 95.5% | ||
| 2017 | Zhang et al. [41] | Tensor decomposition | SVM | Dataset A: normal heart sounds, extra systole, murmurs, artificial heart sounds | MAcc: 76% | ||
| Dataset B: normal heart sounds, extra systole, murmurs | MAcc: 83% | ||||||
| Dataset C: normal heart sounds, abnormal heart sounds | MAcc: 88% | ||||||
| 2017 | Langley and Murray (without segmentation) [42] | / | Wavelet entropy | PhysioNet | Sensitivity | Specificity | Accuracy |
| 94% | 65% | 80% | |||||
| 2017 | Whitaker et al. [43] | Sparse coding | SVM | PhysioNet | Sensitivity | Specificity | MAcc |
| 84.3% | 77.2% | 80.7% | |||||
| 2017 | Li et al. [44] | FFT | BP neural networks | PhysioNet | Sensitivity | Specificity | MAcc |
| 68.36% | 94.01% | 88.56% | |||||
| Logistic regression | Sensitivity | Specificity | MAcc | ||||
| 75.68% | 87.71% | 72.56% | |||||
| 2016 | Deng and Han (without segmentation) [45] | DWT | SVM-DM | Dataset A from PASCAL | The highest total precision of 3.17 | ||
| Dataset B from PASCAL | The highest total precision of 2.03 | ||||||
| 2015 | Zheng et al. [46] | EMD | SVM | A dataset collected from the healthy volunteers and CHF patients | Sensitivity | Specificity | Accuracy |
| 96.59% | 93.75% | 95.39% | |||||
| 2015 | Safara [47] | Wavelet packet tree | Higher-order cumulants (HOC) | A set of 59 heart sounds from different categories: normal heart sounds, mitral regurgitation, aortic stenosis, and aortic regurgitation. | Best classification accuracies: 99.39% | ||
| 2011 | Yuenyong et al. (without segmentation) [48] | DWT | Neural network | Several on-line databases and recorded with an electronic stethoscope | Tenfold cross-validation: 0.92 for noise free case, 0.90 under white noise with 10 dB signal-to-noise ratio (SNR), and 0.90 under impulse noise up to 0.3 s duration | ||