## 4 Results and Discussion
### Experiment on the Training and Validation  
As explained in Section 3.1, the detected and segmented ECG beat was divided into two parts, i.e., training and validation. ECG beats for validation were conducted only after the training process was completed, by producing a weight file with the optimum value of all model parameters. There were a total of 33,226 and 30,496 parameters to be trained and optimized for the ECG-ID and MIT-BIH database, respectively. An example of ECG features extracted using the trained model can be seen in Figure 4. Referring to Table 2, layer 27 (dense_1 layer) produces 64 values which can be used to represent the ECG beat feature of each person. Figure 4: Eight ECG beats feature from 10 people represented by 64 neurons of the fully connected layer (layer 27, Table 2). Figure 4a shows that there were eight columns for each person, while Figure 4b shows that there were six columns for each person. The horizontal lines indicate the regularity of each person's ECG beat feature. Figure 4a shows that ECG beats from persons five, seven, and eight have visible horizontal lines, which indicate they are easier to identify. Figure 4b shows that the regular ECG beat feature was seen for the first until the sixth patient. Meanwhile, the seventh patient does not show clear horizontal lines, indicating that the seventh patient's ECG beats are rather difficult to identify. Table 3 shows the authentication prediction in more detail for eight and six ECG beats of the ECG-ID and MIT-BIH databases, respectively. For ECG-ID, the sixth person could be authenticated as the 6 th or 62 nd person. Meanwhile, for MIT-BIH, the seventh patient could be authenticated as the 7 th or 30 th patient. Based on the previous discussion, the ECG beat features in the 27 th layer could be utilized for selection of the training and validation beats. This layer could be further investigated to produce a biometric key. Each biometric key of these 64 features can be quantized into 6 bits only, because the number keys are integer numbers between \(-20\) to \(20\). Moreover, the number of bits and the number of features can be adjusted according to the number of persons to be authenticated. The summary of the authentication results of all validation beats can be seen in the confusion matrix shown in Figure 5. Figure 5a,b shows the confusion matrix of the authentication results for the 90 validation beats. These 90 beats were extracted from ECG records in the ECG-ID database. Each person is represented by eight beats or a total of 729 validation beats. Figure 5c,d is the confusion matrix of the authentication results for the 48 validation beats. These 48 patient beats were extracted from ECG records in the MIT-BIH database. Each patient is represented by six beats or a total of 288 validation beats. Figure 5a,c shows the confusion matrix for a single validation beat, i.e., the first beat of eight or six consecutive beats. Figure 5b,d shows the confusion matrix for multiple validation beats. From Figure 5, it can be shown that 100% accuracy can be obtained for the multiple validation beats, with six beats and eight beats for the ECG-ID and MIT-BIH databases, respectively. The optimum number of multiple beats will be evaluated in the next experiment. Nevertheless, Figure 5 shows that multiple ECG beats produced better accuracy compared to a single ECG beat.