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Table 4 Sensitivity, specificity, and accuracy of the proposed methodology compared to the literature for arrhythmia and AF classification, evaluated for MIT-BIH Normal Sinus Rhyth, MIT-BIH Arrhythmia, and MIT-BIH Atrial Fibrillation databases

From: Support system for classification of beat-to-beat arrhythmia based on variability and morphology of electrocardiogram

Autor (year) Featureα Classifierβ SENS % SPEC % ACC %
Arrhythmia
This work (2018) Voltage variation LDA 99.64 99.91 99.78
   k-NN 99.64 99.91 99.78
   SVM 99.64 99.91 99.78
Mihandoost et al. (2018) [3] Sparse decomposition SVM 91.47 85.88 99.11
Raj et al. (2018) [42] Spectral analysis SVM, k-NN 91.47 85.88 99.11
Jovic et al. (2017) [6] AlphEn. HRV Random forest 91.10 97.01 91.20
Kim et al. (2016) [7] HRV from 5s SVM 91.69
Elhaj et al. (2016) [8] PCA, DWT, ICA, HOS NN 98.90 98.90 98.90
Martis et al. (2012) [23] DWT, HOS NN 98.61 98.41 94.52
Atrial Fibrillation
This work (2018) P wave voltage variation LDA 100 100 100
   k-NN 100 100 100
   SVM 100 100 100
Andersen et al. (2019) [24] 30 R-R CNN, RNN 99.82 87.94 89.30
Xia et al. (2018) [25] STFT, SWT CNN 98.79 97.87 98.63
Kennedy et al. (2016) [26] CoSEn+CV+RMSSD+MAD Random forest 92.80 98.30
Orchard et al. (2016) [43] P wave absence Proposed algorithm 95.00 99.00  
Petrenas et al. (2015) [27] R-R Threshold 97.10 98.30
Zhou et al. (2014) [28] SD+SE Threshold 97.53 98.26 98.16
  1. The symbol (–) represent the values not specified in the works
  2. AlphEn alphabet entropy, HRV heart rate variability, PCA principal component analysis, DWT discrete wavelet transform, ICA independent component analysis, HOS higher order spectra, CoSEn coefficient of sample entropy, CV coefficient of variance, RMSSDroot mean square of the successive differences, MAD median absolute deviation, SD symbolic dynamics, SE Shannon entropy, RR R-R intervals, STFT short-term Fourier transform, SWT stationary wavelet transform, LDA linear discriminant analysis, k-NN k-nearest neighbors, SVM support vector machine, NN neural network, CNN convolutional neural network, RNN recurrent neural network