|
| References | Method | Year | NS | DU (s) | Sen |
|
| Agrafioti and Hatzinakos [45] | Autocorrelation and discrete cosine transform (DCT) | 2006 | 14 | 10 | 100 |
| Wübbeler et al. [70] | Fiducial features and simple distance measure | 2007 | 74 | 10 | 99 |
| Agrafioti and Hatzinakos [71] | Normalized autocorrelation coefficients and K-nearest neighbors (K-NN) as a classifier | 2008 | 13 | 10 | 96.2 |
| Agrafioti and Hatzinakos [72] | Same approach as in [70] with feature level and decision level fusions | 2008 | 14 | 5 | 100 |
| Wang et al. [73] | Autocorrelation (AC) in conjunction with a discrete cosine transform (DCT) and K-NN as classifier | 2008 | 13 | NA | 84.61 |
| Fatemian and Hatzinakos [36] | Templet-based | 2009 | 13 | NA | 99.62 |
| Safie et al. [74] | Pulse active ratio (PAR) technique for feature extraction and Euclidean distance | 2011 | 112 | 30 | 93.60 |
| Zhao et al. [49] | Ensemble empirical mode decomposition, Welch spectral analysis to extract significant features, principal component analysis (PCA) for dimensionality reduction, and K-NN as classifier | 2013 | 25 | NA | 96.00 |
Tantawi et al. [75]
Test set 1
Test set 2 | Fiducial feature set (with 28 features) and four feature reduction methods (PCA, linear discriminant analysis (LDA), information gain ratio (IGR), and rough sets) and neural network as a classifier | 2013 | 14
6 | ~8 | 100
83.3 |
| Wang et al. [76] | Sparse representation and K-NN as classifier | 2013 | 100 | 2-4 | 99.5 |
| Jekova and Bortolan [77] | Correlation coefficient assessment, along with assessment of their linear and nonlinear combinations | 2015 | 14 | 10 | 92.9 |
| Brás and Pinho [78] | Information-theoretic data models for data compression and on similarity metrics related to the approximation of the Kolmogorov complexity | 2015 | 52 | 20 | 99.9 |
| Waili et al. [79] | Q-R-S feature points and multilayer perceptional neural network as a classifier | 2016 | 14 | 12
1.02 heartbeats | 96 |
| Paiva et al. [25] | Three features ST, RT, and QT and support vector machines as a classifier | 2017 | 10 | 30 | 97.5 |
| Dong et al. [80] | Deterministic learning | 2018 | 113 | NA | 92.8 |
| Labati et al. [58] | The deep convolutional neural networks are used to extract the features from QRS complexes and soft-max as a classifier. | 2018 | 52 | 10 | 100 |
| Alotaiby et al. [81] | Common spatial pattern and support vector machine as a classifier | 2019 | 200 | 7 | |
| Single-lead (I) | | | | | 95.15 |
| Single-lead (V3) | | | | | 98.92 |
| Proposed method | 11 statistical features, DWT, and random forest as a classifier | | 290 | 7 | |
| Single-lead (I) | | | | | 99.66 |
| Single-lead (aVF) | | | | | 99.17 |
| Single-lead (V1) | | | | | 99.52 |
| Single-lead (Vx) | | | | | 99.79 |
|
