## 4 Results
### Comparative Analysis  
Table 14 discusses the experimental results of the proposed model with the previous related work. In [22], that work, signals are decomposed to the multilevel for analysis and then 1D-CNN is applied to the ECG-ID dataset with 10 subjects containing two records and NSRDB [4] with 18 subjects. In [56], the MIT-BIH arrhythmia database as well as the delayed long short-term memory (DLSTM) were used to predict heartbeats from five classes. The model delays until the cross-ponding measure values are acquired. This model is only for five classes, but our proposed model identifies 156 subjects in a combined dataset. In [42], a method was proposed for converting ECG signals into scalogram images using the continuous wavelet transform (CWT) and then extracting two-dimensional features using the discrete wavelet transform (DWT) and passing them to the CNN model. Ref. [25], in their work, generated an electrocardiogram(EKM), which is a matrix that is composed of several aligned R peaks from an EKG trace and then transformed. Furthermore, afterwards, CNN is applied for further evaluation. The method in [33] was introduced, namely bi-directional gated recurrent units, which is a variant of LSTM. BGRU gives good performance on time series data. The nine consecutive heartbeats are passed through the model as input. However, as compared to our proposed method's single sample with PQRS and T points, it is passed as input to the network. Thus, the method in [33] is not \begin{table}
\begin{tabular}{c c c c} \hline
**Author** & **Dataset** & **Model Type** & **Performance** \\ \hline [PERSON] et al. [22] & NSRDB (number of subjects: 18) & 1D-CNN + DWT & 96.93\% \\  & MIT-BIH & & 96.82 \% \\  & ECG-ID (number of subject: 10) & & 100 \% \\ \hline [PERSON] et al. [52] & ECG-ID & 1D local difference pattern (1D-LDP) & 91.11\% \\ \hline [PERSON] et al. [42] & NSRDB & CWT + DWT + Deep learning & 100\% \\  & PTB & & 99.5\% \\  & MIT-BIH arrhythmia & & 98.1\% \\  & STDB & & 98.2\% \\ \hline [PERSON] et al. [25] & MITBIH & EKM + CNN & 98.84\% \\  & NSRDB [4] & & 99.53\% \\  & PTB & & 89.8\% \\ \hline [PERSON] et al. [53] & ECG-ID & GNMF-based feature extraction & 100\% \\  & MITBIH & Non-fudicial method & 100\% \\ \hline [PERSON] et al. [54] & MIT-BIH & Heartbeat interval & 87.37\% \\  & IIT(BIH) & Features & 92.88\% \\ \hline [PERSON] et al. [32] & PTB-ECG & PCANET & 98.54\% \\  & CU-ECG & & 90.42\% \\  & MIT-BIH & & 84.78\% \\ \hline [PERSON] et al. [55] & ECG-ID (two recordings) & DWT + S-AEs & 95.1\% \\  & ECG-ID (all recordings) & & 92.3\% \\  & MIT-BIH & & 96.82\% \\ \hline [PERSON] et al. [19] & ECG-ID (only five heartbeats) & PCANET-features & 94.4\% \\ \hline [PERSON] et al. [56] & MIT-BIH (5 no. of Classes) & DULSTM-WS2 & 99.25\% \\  & MIT-BIH (5 no. of classes) & DBLSTM-WS3 & 99.39\% \\ \hline [PERSON] et al.[40] & NSRDB [4] & Multiscale 1D-CNN & 95.1\% \\  & MIT-BIH & & 91.1\% \\ \hline [PERSON] et al. [21] & Single-arm-ECG & Based on (DCNN) & 98.4\% \\  & Ear-ECG & & 91.1\% \\ \hline [PERSON] et al. [33] & ECG-ID (9-consecutive heartbeat input) & BGRU (Bi-directional gated) & 98.60\% \\  & MIT-BIH (9-consecutive heartbeat input) & Recurrent units & 98. 40\% \\ \hline Proposed & ECD-ID & 1D-CRNN+ DWT & 80.64\% \\ Methodology & MIT-BIH & & 98.81\% \\  & NSRDB & & 99.62\% \\  & Combined dataset & & 98.24\% \\ \hline \end{tabular}
\end{table} 
Table 14: Comparison of the proposed approach with the previous study. efficient and takes a significant amount of time for identification compared to our proposed method. In Ref. [34], a method was introduced that extracts parallel features from LSTM and 1D-CNN, and then both features extracted are merged and transferred to the classifier as a heatmap representation of an image.