Table 1.
Comparison with existing literature.
| Ref | Year | Dataset | Classes | Features | Classifier | Results |
|---|---|---|---|---|---|---|
| [9] | 2016 | Physionet Challenge 2016 [39] | Normal(2488), Abnormal(665) | Time-frequency, Wavelet and statistical |
LogitBoost, Random Forest | Acc: 84.48% |
| [11] | 2016 | Physionet Challenge 2016 [39] | Normal(2575), Abnormal(665) | Dynamic time warping | SVM | Acc: 82.4% |
| [15] | 2016 | Physionet Challenge 2016 | Normal(2575), Abnormal(665) | 124 Time-frequency features | Adaboost, CNN | Acc: 89% |
| [12] | 2016 | Self-collected | Normal(132), Abnormal seven classes(131) | Arash-Band | SVM | Acc: 87.45% |
| [36] | 2017 | Self-collected | Small VSD(60), Large VSD(60) | Statistical, DWT features | Multilayer Perceptron (MLP) | Acc: 96.6% |
| [35] | 2017 | Self-collected | Normal, VSD | (STFT), MFCC | KNN | Acc: 93.2% |
| [13] | 2018 | PhysioNet Computing in Cardiology Challenge | Normal(2575), Abnormal(665) | GFCC | Weighted SVM | Sen: 90.3% Spec: 89% |
| [17] | 2018 | UoC-murmur database, PhysioNet-2016 | Normal(336), CHD(130), Normal/Abnormal(2435) | Mel-Spectrogram, MFFC and sub-band envelopes | CNN | Acc: 81.5% Sen: 84.5% |
| [10] | 2018 | PhysioNet Computing in Cardiology Challenge-2016 | Normal(50), Abnormal(50) | Cepstrum Analysis | SVM | Acc: 95% |
| [38] | 2018 | Self-collected | Normal(40), Abnormal(58) | CBFE, FEUAP, FSDA, DDE | KNN | Acc: 84.39% |
| [32] | 2019 | Self-collected | Normal(175), Abnormal(108) | MFCC, normalized average Shannon energy |
SVM | Acc: 92.6% |
| This work | 2020 | Self-collected | Normal(140), Abnormal(140) | MFCC + 1D-LTPs | SVM | Acc: 95.63% |
| This work | 2020 | Self-collected | Normal(140), ASD(85), VSD(55) | MFCC + 1D-LTPs | SVM | Acc: 95.24% |