# Data Independent Acquisition Based Bi-Directional Deep Networks for Biometric ECG Authentication
## 4 Experimental Results and Discussion
### System Evaluation
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Dataset** & **Method** & **Acc** & **Sen** & **Spe** & **Ppr** & **F1** & **FM** \\ \hline \multirow{6}{*}{ECG-ID} & [PERSON] et al. [5] & 98.3 & 75.2 & 98.3 & 99.8 & 85.8 & 85.8 \\  & [PERSON] et al. [24] & 98.4 & 93 & 97.5 & 98.2 & 95.5 & 95.5 \\  & [PERSON] et al. [41] & 90.3 & 94.2 & 95.6 & 93.1 & 93.6 & 93.6 \\  & [PERSON] et al. [40] & 98.3 & 95.7 & 98.2 & 99.2 & 97.4 & 97.4 \\  & Proposed & 99.3 & 98.3 & 99.2 & 99.4 & 98.84 & 98.84 \\ \hline \multirow{6}{*}{MIT-BIH ECG} & [PERSON] et al. [5] & 98.6 & 95.2 & 97.3 & 89.5 & 92.2 & 92.2 \\  & [PERSON] et al. [24] & 99.4 & 95.8 & 99.7 & 97.8 & 96.8 & 96.8 \\  & [PERSON] et al. [41] & 80.1 & 82.8 & 89.1 & 84.4 & 83.59 & 83.59 \\  & [PERSON] et al. [40] & 80.1 & 82.8 & 89.1 & 84.4 & 83.59 & 83.59 \\  & Proposed & 99.5 & 99.2 & 98.8 & 99.2 & 99.2 & 99.2 \\ \hline \multirow{6}{*}{STAFF-III} & [PERSON] et al. [5] & 98.1 & 86.6 & 99.3 & 96.2 & 91.2 & 91.2 \\  & [PERSON] et al. [24] & 98.7 & 91.3 & 97.4 & 97.8 & 94.4 & 94.4 \\  & [PERSON] et al. [41] & 89.4 & 88.7 & 92.3 & 89.6 & 89.14 & 89.14 \\  & [PERSON] et al. [40] & 98.6 & 94.6 & 99.2 & 98.5 & 96.5 & 96 \\  & Proposed & 99.3 & 95.5 & 97.9 & 99.2 & 97.31 & 97.07 \\ \hline \multirow{6}{*}{LT-AF} & [PERSON] et al. [5] & 97.6 & 95.8 & 95.3 & 96.4 & 96 & 96 \\  & [PERSON] et al. [24] & 99.1 & 99.4 & 98.7 & 98.5 & 98.6 & 98.6 \\  & [PERSON] et al. [41] & 89.4 & 88.4 & 90.2 & 93.2 & 90.7 & 90.76 \\  & [PERSON] et al. [40] & 99.4 & 99.2 & 98.4 & 97.6 & 98.39 & 98.39 \\  & Proposed & 99.2 & 99.6 & 98.2 & 99.5 & 99.5 & 99.5 \\ \hline \multirow{6}{*}{AFDB} & [PERSON] et al. [5] & 89.6 & 91.3 & 90.5 & 88.7 & 90 & 90 \\  & [PERSON] et al. [24] & 83.1 & 88.4 & 89.3 & 88.3 & 88.34 & 88.34 \\  & [PERSON] et al. [41] & 79.1 & 81.2 & 88.4 & 86.5 & 83.8 & 83.8 \\  & [PERSON] et al. [40] & 90.2 & 89.8 & 92.5 & 90.3 & 90.4 & 90.4 \\  & Proposed & 98.5 & 97.3 & 99.1 & 98.6 & 97.94 & 97.94 \\ \hline \multirow{6}{*}{AHA} & [PERSON] et al. [5] & 84.3 & 81.5 & 83.6 & 88.4 & 84.87 & 84.87 \\  & [PERSON] et al. [24] & 85.4 & 86.4 & 88.3 & 83.4 & 84.8 & 84.8 \\ \cline{1-1}  & [PERSON] et al. [41] & 76.5 & 81.2 & 83.2 & 88.2 & 84.62 & 84.62 \\ \cline{1-1}  & [PERSON] et al. [40] & 87.5 & 88.3 & 89.2 & 83.6 & 85.91 & 85.91 \\ \cline{1-1}  & Proposed & 97.3 & 96.4 & 97.2 & 98.4 & 97.39 & 97.39 \\ \hline \end{tabular}
\end{table} 
Table 4: Comparison of the proposed method with recent state-of-the-art works in terms of classification accuracy of 20 classes over four different datasets (ECG-ID, MIT-BIH, STAFF-III, LT-AF, AFDB, and AHA). This is consistent with the conceptual experiment that establishes that poor local minima are hardly an issue in deep neural networks with many layers consisting of a large number of parameters. Instead, the landscape of the object function is packed with a variation of valleys, which seems to typically have local minima with similar values. Therefore, the randomness in the Adam optimization-based method parameter tuning process often results in only small fluctuations in the convergence curve in the training process.