## 4 Results
### Conventional DWT Feature Extraction  
The discrete wavelet transform (DWT) is constructed to demonstrate signals in both the time and frequency domains. The fundamental wavelet's interpretation and extension shape it. The original signal has been approximated using the change coefficients. The analysis range in time is smaller when using a smaller scale; even so, within the frequency domain, it is equal to the use of the excessive frequency wavelets analysis method, i.e., using high-frequency wavelet decomposition for precise observation. A large scale allows for a wider analysis range in the time domain, but it is equivalent to employing a low-frequency wavelet for observations in the frequency domain. The DWT equation is presented in Section 3.3.4, Equation (5).  
Figure 9: The basic architecture of the Inception V3 model containing 12 blocks with multiple layers.  
The maximum frequency of the signal, based on the sampling theorem, is \(f_{s}/2\). When a signal is decomposed into order L using the Mallat algorithm, the corresponding frequency bands of the signal have decomposed into L + 1 subbands. The decomposition of the signal is shown in Figure 10.  
For feature extraction, the signal is decomposed to the order of 4. The coefficients of the decomposed signal are used as a feature to pass the classifier. The result of conventional DWT features on the different DWT families and the classifier is shown in Tables 3-5.  
After comparing the results of different classifiers for DWT families, it was found that the random forest classifier performed better than the others. This classifier has a hyperparameter called \"n estimators\" that determines the number of trees in the forest which was set to 100. Additionally, the random state option was used to ensure that the method produces the same results each time it is run, which is important for consistency. The random forest classifier is known for its high accuracy, ability to handle both categorical and continuous data. It also provides a ranking of features.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Database Name** & **Haar** & \(\mathbf{db2}\) & **bio6.8** & **sym5** & **Coif5** \\ \hline ECGID & 53\% & 54\% & 54\% & 54.51\% & 59\% \\ MIT-BIH & 88.93\% & 89.34\% & 89.71\% & 89.36\% & 89.96\% \\ NSRDB & 97.81\% & 97.97\% & 97.90\% & 97.97\% & 98\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of conventional DWT features using KNN classifier on databases such as ECG-ID, MITBIH and NSRDB.  
Figure 10: (**a**) The signal is decomposed into 4 levels and then all coefficients are concatenated for the ECG-ID database; (**b**) The signal is decomposed into 4 levels and then all coefficients are concatenated for the MIT-BIH database; (**c**) The signal is decomposed into 4 levels and then all coefficients are concatenated for the NSRDB database.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Database Name** & **Haar** & \(\mathbf{db2}\) & **bio6.8** & **sym5** & **Coif5** \\ \hline ECGID & 74.19\% & 73.06\% & 64.83\% & 70.80\% & 73.04\% \\ MIT-BIH & 97.70\% & 97.69\% & 96.67\% & 97.12\% & 97.60\% \\ NSRDB & 99.15\% & 98.93\% & 98.90\% & 98.03\% & 99.15\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of conventional DWT features using random forest classifier on databases such as ECG-ID, MITBIH and NSRDB.