# An Intelligent Multimodal Biometric Authentication Model for Personalised Healthcare Services
## 5 Experiment Design and Results Analysis
### Experiments and Results
#### 5.3.1 ECG Signal Based Authentication  
The first experiment for ECG signals has been conducted using the ECG signals of 53 users from the BIMDC dataset [57].  
The dataset was initially divided into two portions--\"TrainA\" and \"TestA\". One of the users, \"UserA\" from \"TrainA\", is marked as the authenticated user, and the rest of all the data from \"TrainA\" and \"TestA\" are considered intruders. The ECG signal data of \"UserA\" and other users is about 7 min long. These signals are processed to create multiple instances of an equal length of 9 s. The data of \"UserA\" was then further partitioned to 33% training and 67% testing data. The training samples of \"UserA\" are copied multiple times to increase the training instances that will allow overtraining of the authenticated user, and through this overtraining, it will enhance the security of the biometric model to identify the authenticated user.  
Additionally, it will improve data balance and symmetry during the training session. The dataset of the remaining users from \"TrainA\" and the users of \"TestA\" are also processed to create 9 s length samples. Multiple machine learning classifier algorithms are used and compared to find the best performer within the training set and tested with the test samples. The outcome of the test results of 2533 samples is presented in Table 3.  
To validate the scalability of the biometric model, subsequently, we run the second experiment for the ECG signal, adding the combination of three datasets which are: MIT-BIH Arrhythmia Database [59], MIT-BIH Normal Sinus Rhythm Database [58], and BIDMC Congestive Heart Failure Database [60]. We use the ECG records of 120 users. All these 120 users are regarded as intruders. The result of the test of 10,213 samples is presented in Table 4.  
Figure 11: The LSTM Model for Data-Fusion-Centric Authentication.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline KNN Fine & 75.6\% & 47.7\% & 98.3\% & 85.6\% & 0.013 \\ \hline Ensemble Bagged Trees & 95.1\% & 65.0\% & 99.1\% & 97.1\% & 0.03 \\ \hline Naive Bayes & 95.1\% & 100\% & 99.9\% & 97.5\% & 0.03 \\ \hline SVM & 100\% & 26.1\% & 95.4\% & 97.6\% & 0.02 \\ \hline GAM & 97.6\% & 41.7\% & 97.8\% & 97.7\% & 0.02 \\ \hline \hline \end{tabular}
\end{table}
Table 3: ECG Result Details.  
After performing the single-user authentication for ECG-only signals, we experimented with the ECG-only group authentication. For this experiment, a group of three is created, considering that they belong to the same personal healthcare network. ECG signals from these three users are regarded as authenticated users, and the others are considered intruders. We use the same strategies to process ECG signals, labelling, feature extractions, training and testing as single-user experiments.  
Multiple ML algorithms are used to find the best-performing model. Out of these algorithms, the SVM receives the given number of features in the learning phase and performs a grid search optimisation algorithm to find the optimal normalisation resolution factor \(\alpha\). Then the machine derives the normalisation resolution factor with the remaining features by comparing them with those being used in the learning phase. The code for our SVM template is as follows:  
template = templateSVM(...  'KernelFunction','polynomial',...  'KernelScale','auto',...  'BoxConstraint',1,...  'Standardize',true); model = fitececoc(...  trainFeatures,...  train_n_test_label,...  'Learners',template,...  'Coding','onevsone',...  'ClassNames',{'AUT','NAUTH'}); The source codes of all the experiments and related files are available in GitHub [56].  
The model using Naive Bayes did not perform well in the multiuser authentication model. Out of the algorithms used to train the model, GAM performed comparatively better with a precision of 52.6% and EER 0.04. The results are presented in Table 5.