# ECG Arrhythmia Classification using High Order Spectrum and 2D Graph Fourier Transform
## 5 Conclusions  
The aim of the conducted research is to develop a new methodology that enables the efficient classification of cardiovascular disease. The ECG signals can be tool for the medical practitioners to detect various types of cardiac arrhythmia because abnormal heart electrical activity can cause irregular morphology changes which would be checked through ECG signals. In this paper, we proposed an ECG arrhythmia analysis method based on the combination of bispectrum and 2D-GFT. In the proposed method, we use high-order spectra to detect the basic features and then execute 2D-GFT on the result. And extract 2D spectral features. Support vector machine based on the RBF kernel are employed in the classification procedure. The experimental results depicted that our proposed model achieved a high classification accuracy of 96.2%. The novelty of this approach is to regard the bispectrum results as an image and apply 2D-GFT to the ECG signal. Compared with the previous references, such as [PERSON] [10] and [PERSON] [17], our advantage is that we can achieve effective classification using only three features, which greatly improves the classification efficiency. Furthermore, the methods used by [PERSON] et al. [10] and [PERSON] et al. [17] only made binary classification, while the proposed method can achieve multi-class categorization. Compared with [PERSON] et al. [41], who also used three features for classification, the accuracy of our method is 10% higher than theirs. ECG recordings of four arrhythmia types, shared by the MIT-BIH arrhythmia database, were used for evaluations of the algorithm performance. We used the cross-validation method for classification. Compared with bispectrum analysis, after 2D-GFT, all metrics are improved. By our designed method, the sensitivity, specificity and positive predictive rate of the S beats are improved 30.5%, 0.3% and 8.3%, those of the V beats are improved 26.9%, 9.7% and 9.7%, those of the F beats are improved 27.2%, 0.4% and 36.4%, respectively. Comparisons with peer works prove a marginal progress in heart arrhythmia classification performance. This study proves that the proposed method is an excellent model for the diagnosis of heart diseases based on ECG signals. In comparison with some references, the data pre-processing process in this paper is comparative to recent approaches, which may affect the classification effect of the ECG signals. Many methods have been proposed to remove noise, such as wavelet transform [46], empirical mode decomposition [47], \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Method**} & [7] & [42] & [45] & **Proposed** \\ \hline \multirow{2}{*}{_Sen_} & Class S & 53.3 & 79.1 & 60.8 & 36.1 \\ \cline{2-6}  & Class V & 67.7 & 85.5 & 81.5 & 52.7 \\ \hline \multirow{2}{*}{_Spe_} & Class S & 86.7 & 93.9 & 80.8 & 99.7 \\ \cline{2-6}  & Class V & 86.7 & 99.5 & 82.8 & 99.4 \\ \hline \multirow{2}{*}{_Ppr_} & Class S & / & 36.0 & / & 87.9 \\ \cline{2-6}  & Class V & / & 92.8 & / & 90.1 \\ \hline \multicolumn{2}{c}{Overall _Acc_} & 84.5 & 88.3 & 86.4 & 92.2 \\ \hline \multicolumn{6}{c}{The values in the table multiply the original values by 100.} \\ \end{tabular}
\end{table} 
Table 16: Comparison with other existing approaches on inter-patient scheme. independent component analysis [48; 49], and so on. In future work, we can improve the pre-processing method to achieve better classification effect. Deep learning is the focus of current research and has been successfully applied in the classification of ECG signals. For example, [PERSON] et al. [38] got an accuracy of 97.7% with a classifier of KNN. We can combine our method with neural networks to realize the classification of more types of ECG signals later. Furthermore, the approach proposed in this paper could be extended to solve the imbalance problem in other related fields.