## 2 Methods
### ANC Test Enchanted by Machine Learning  
The final step to predict the systolic blood pressure category based on the defined signal morphology classification is to enchant the ANC Test results with a powerful detection tool. In this way multiple machine learning (ML) architectures have been evaluated using classification learner tools from Matlab(tm) Software. The tools contain the following models: Decision Trees, Logistic Regression, Discriminant Analysis, Naive Bayes, Support Vector Machine, Nearest Neighbor Classifiers, and Ensemble Classifier. We tested every model to obtain the best metrics. Since the aim of the present study was to detect systolic hypertensive persons, to fulfil Level 1--BP prediction, two categories were defined:  
* Class I: Recordings with associated systolic BP value from 80 to 129 mmHg;
* Class II: Recordings with associated systolic BP value over 130 mmHg.  
Therefore, in Class I are clustered systolic hypotensive and normotensive datasets while in Class II are clustered prehypertensive and hypertension ones where the threshold value is 130 mmHg. Multiple clustering for each known BP category was avoided due to the low number of datasets and for uneven distribution as it was shown in the histogram back in Figure 2.  
The input parameters for ML models are represented by the results of the ANC Test(tm) where the occurrence rate for each type and DtS parameter were computed automatically on PPG signal. The output is represented by the previous defined BP binary clusters. Therefore, given an input PPG waveform, the developed algorithm extracted ANC features and then fed them into the ML model. The last step should give the result if the given PPG signal comes from a normotensive or hypertensive patient.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Age Group** & **Total No.** & **Occ. of A [\%]** & **Occ. of N [\%]** & **Occ. of NC [\%]** & **Occ. of C [\%]** \\ \hline
0–9 & 15 & 0 & 13.33 & 60 & 26.66 \\
10–19 & 13 & 0 & 76.92 & 23.07 & 0 \\
30–39 & 4 & 0 & 50 & 50 & 0 \\
40–49 & 3 & 0 & 75 & 0 & 25 \\
50+ & 6 & 0 & 0 & 83.33 & 16.66 \\ \hline \hline \end{tabular}
\end{table}
Table 6: ANC Test results vs. age groups for Dataset 3.  
For Dataset 1, characterized by a Gaussian distribution of systolic BP values, \(\Lambda\) feature was the most accurate predictor in detecting hypertensive patients with an accuracy value of 67.1% using SVM Gaussian model. The F1 score and area under learning curve (AUC) was also the best factors in respect to the remaining parameters. The next ranked parameter is DtS which represent numerically the DCW strength in respect to the current SYS one. The obtained accuracy was 58.4% but with a weaker F1 score than the previous one. The remaining features obtained an accuracy of 56.6% but is closer to the accuracy of a random choosing with a 50% rate. Therefore, the last ones do not bring any standalone benefits.  
For Dataset 2, built by a uniform BP value distribution, N feature was the most accurate predictor with a 68.1% rate also using SVM Gaussian model with an F1 score of 0.76. Additionally, it obtains the best AUC rate out of all parameters. Interestingly, the remaining features scored around 60% accuracy, also with a good F1 score. At this point, by comparing it, it can be concluded that a uniform distribution that feeds ML models gives a better performance than a Gaussian one.  
The last evaluation of standalone feature was done by merging the two datasets. Since there were two different BP value distribution results, also in a Gaussian type, the accuracy results in special seem to be a mean between Dataset 1 and Dataset 2. The best accuracy was obtained by A type with a rate of 61.3% using an SVM Quadratic model. Although intuitively feeding an ML model with more samples is to increase its prediction rate, in the current context, the distribution of the recordings among desired output plays an important role in metrics too.