# An Intelligent Multimodal Biometric Authentication Model for Personalised Healthcare Services
## 5 Experiment Design and Results Analysis
### Experiments and Results
#### 5.3.3 ECG-PPG Fusion Signal Based Authentication  
Consequently, experiments are conducted with ECG and PPG fused signals from the BIMDC dataset [57]. This dataset has over 6 min of these two signals for every 53 users. Several fusion techniques were used to validate which fusion signal will provide a better result as described in the data fusion section. At first, we simply add the amplitude of the ECG and PPG signals and apply classifier machine learning algorithms as follows: Firstly, from the fused signal we extract [PERSON]'s Entropy (SE), Wavelet variances (WV) and AR coefficients. Then we train and test the model to recognise a single authenticated user. The signal data of each user are processed and prepared to create smaller sample data points where each data point contained around 9 s length of signal (worth 11 heartbeats). Training and testing portions of the data are separated in a 70/30 proportion. Multiple training algorithms are used to train the model. To select the best-performing model, many tests are conducted on the trained model using the testing sample portion of the dataset. The extracted features based on Table 2 parameters are used to train multiple classifiers. However, the above-mentioned classifiers with time and time-frequency joint features have not achieved a promising result. The model was trained to apply the ten-fold cross-validation method. Additionally, EER and F1 scores were considered in selecting the best model. The best performing classification model trained using GAM achieved a precision of 44.4% and EER of 0.02. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & & & & \\ \hline KNN Fine & 75.6\% & 4.9\% & 79.8\% & 77.7\% & 0.20 \\ \hline Ensemble Bagged Trees & 97.6\% & 12.8\% & 91.0\% & 94.1\% & 0.09 \\ \hline Naive Bayes & 100\% & 6.8\% & 85.5\% & 92.1\% & 0.15 \\ \hline SVM & 56.1\% & 9.7\% & 92.3\% & 69.9\% & 0.08 \\ \hline GAM & 97.6\% & 11.3\% & 89.6\% & 93.4\% & 0.10 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: PPG Result Details—Single User Authentication. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline KNN Fine (3 Usr) & 85.5\% & 31.6\% & 90.9\% & 88.2\% & 0.09 \\ \hline Ensemble Bagged Trees (3 Usr) & 91.1\% & 42.9\% & 95.3\% & 93.3\% & 0.05 \\ \hline Naive Bayes (3 Usr) & 82.3\% & 43.2\% & 94.3\% & 88.1\% & 0.06 \\ \hline SVM (3 Usr) & 100.0\% & 7.7\% & 45.8\% & 60.3\% & 0.54 \\ \hline GAM (3 Usr) & 91.1\% & 52.6\% & 95.9\% & 93.5\% & 0.04 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: ECG Result Details—Multiuser Authentication. We have tried different fusion methods and extracted some new features to improve the result further. We also create smaller sampling points by reducing the frequency. The preprocessed data are in the proportion of 70/30 for the training and testing. We conduct the fusion using Square Root (SQRT), RMS, GM, AM and HM. A detailed discussion of these metrics has been provided in the data fusion section. All these metrics except HM produce good results for the fusion signal. For further fusion signal based experiments, we use RMS fusion as standard as it provided slightly better F1 scores than the other fusion approaches that were applied. We have extracted IF and SE features as described in [62]. These features are used in deep learning models: CNN and LSTM. LSTM model is specialized in finding patterns in a time series sequence. In the experiment of single-user authentication, LSTM has demonstrated precision and F1 score rate as 97.1% and EER 0.16. CNN has provided a precision rate of 92.1%, F1 score of 98.5% and EER of 0.21. Table 8 shows the comparison of the experiment results. After conducting the single-user experiments, we run the group-based authentication experiments. The data processing and feature extraction method remains the same as in previous experiments.