## 3 Feature-Engineering
### Feature Fine-Tuning  
After defining three different categories of features, it is necessary to fine-tune the proposed features in order to obtain the most efficient combination and representation of them. As will be shown in Section 4, the error performance can drop after combining features from different categories. One interpretation of this drop is that some features are strictly efficient for a group of patients and confusing for the rest. In order to limit this effect a fine-tuning step is performed.  
The feature fine-tuning phase is based on the selection of vital signs instead of the selection of dimensions which is in contrast with existing automatic and conventional feature-selection techniques. Indeed, the rows of the input matrix of our data correspond with the different subjects in the study and contain the different features calculated on multiple windows (e.g., the statistical feature of mean is extracted from \(m\) vital signs within \(n\) time-windows resulting in \(mn\) columns for each subject). Conventional feature-selection techniques select the columns of the matrix that are most representative for the study. However, in this way feature values within a specific time-window can be excluded leading to features that are hard to interpret. For this reason, we propose a backward selection approach where a feature (corresponding to multiple columns in the input matrix) can be excluded from the set of features. Moreover, prior knowledge is used in order to reduce the randomness in the selection process of the features. For instance, we will exclude the statistical and dynamic features of the \(HR\) guided by the prior knowledge that the heart is a main actuator in the control system of a human body that responds to different excitations (e.g., medication), not only critical events [34]. The effect on the performance score of this selection will be discussed in Section 4.  
The procedure of feature fine-tuning that we propose in this work starts with exploring whether statistical and dynamic features are providing high performance when extracted from all vital signs or strictly from a subset of these vital signs. Moreover, we assess the effect on the classification performance of using aggregate features which are calculated on a group of vital signs together rather than on individual vital signs. Furthermore, feature values can be presented as either real or absolute. This procedure is applied exhaustively to the statistical and dynamic features and is assessed by the error performance. The resulting fine-tuning (FT) steps are as follows:  
1. FT1: For \(HR\) extracted features, it is found that excluding both statistical and dynamic features enhances the error performance.
2. FT2: The _correlation coefficients_ feature is found more efficient when presented in both real and absolute values.
3. FT3:_Outlier-occurrence count_, is found most efficient when applied to \(SBP\), \(MAP\), \(RR\) and \(PP\) excluding \(DBP\) and \(SpO_{2}\). Moreover, the _outlier-occurrence count_ is found more efficient when presented in an aggregate form instead of individually except for the vital sign \(SBP\).  
FT4: The _correlation coefficients_ feature is providing the best performance when computed only between \(HR\) and \(SBP\). Together with considering the features _low-\(SpO_{2}\) count_ and _low-RR count_ the classification performance is improved.
5. FT5: _crossing-the-mean count_ is found more efficient when applied only to \(SBP\) and \(RR\) and represented in the aggregate form.
6. FT6: The dynamic feature of _outlier indicator_ is more efficient when applied only to \(SBP\) and \(DBP\).
7. FT7: Ultimately, considering the physiological feature of \(CVD\) enhanced the performance.