# Constructing Features for Screening Neurodevelopmental Disorders Using Grammatical Evolution
## 2 Background Information
### The Proposed Method
The resulting transformed features, representing a subset of principal components, serve as inputs for training the MLP. The MLP, with its layered architecture, learns intricate patterns and relationships within the reduced-dimensional data [52]. This approach offers advantages such as mitigating the curse of dimensionality and potentially improving the MLP'sgeneralization to new, unseen instances [69]. Careful hyperparameter tuning, including selecting the optimal number of principal components and configuring the MLP architecture, is crucial for effective implementation. Overall, the MLP PCA algorithm integrates dimensionality reduction with the capacity of MLPs, presenting a solution for handling complex data with high dimensionality [52]. ## 3 Materials and Methods

This work was part of the project SmartSpeech, with the full title \"Smart Computing Models, Sensors, and Early diagnostic speech and language deficiencies indicators in Child Communication\" funded by the Region of Epirus and supported by the European Regional Development Fund (ERDF). The participants were recruited through private and public health and education institutions; they were mainly children, and their parents were informed thoroughly regarding the project's scope and procedures, and asked to provide written consent and details about their child's developmental and communication profile. Also, the parents were notified about the approval of this study from the University of Ioannina Research Ethics Committee with compliance to the General Data Protection Regulation (GDPR). The child's active role in this study involved playing the serious game (SG), part of the SmartSpeech system. The SG consists of children-specific activities with the objective of gathering data regarding the developmental skills of the child and biometric data such as heart rate and gaze responses. These biometric data were collected in order to investigate their role as bio-markers and their potential use for classification purposes. The interaction of the child with the game involved several activities, each with the goal to actively participate and help to overcome missions/tasks and advance to the next level. All the activities had visual context directed in a way that they were entertaining and attractive, as seen in Figure 1. Nevertheless, behind the scenes activities also served as clinical tools trained toward measuring several speech, language, and developmental skills. The child followed a narrated story and had to solve puzzles through its chapters, select or drag objects on the touchscreen, identify images and shapes, recall names and events, recognize emotions, and even answer questions verbally. Regarding the child verbal responses, for word recognition, we used the speech-to-text program CMUSphinx, version 5.0.0 [70], which is an open-source project that is free of charge, has cross-platform support for desktop and mobile systems, and can be used

Figure 1: Screenshots from the in-house SG. offline. Also, there exists a model for Greek language that was created and trained for this software [71]. For the biometric samples, we used a smartwatch with bio-sensors and a software-based eye-tracking module that monitors the gaze of the child while looking at the screen. The smartwatch the individual wore during the SG activities captured and sent their cardiac rhythm to the SmartSpeech database for analysis. Heart rate variables were computed for each activity, including HRV, which was estimated using heart rate standard deviation and range statistics due to challenges in directly calculating HRV from the wearable device's heart rate data. Thus, for each activity, we obtained three variables that were the mean, the standard deviation, and the range of the heart rate. SeeSo software, Unity mobile SDK version 2.4.4, was used for eye tracking [72] to determine where the user's eyes focused while performing certain activities on the mobile device. It recorded gaze points with X and Y coordinates of the screen at specific time intervals during SG activities.