Application of ANFIS-based Non-Linear Regression Modelling to Predict Concentration Level in Concentration Grid Test as Early Detection of ADHD in Children

Sayyidul Istighfar Ittaqillah (Biomedical Engineering Study Program, Department of Physics, Faculty of Science and Technology, Universitas Airlangga, Indonesia)
Delfina Amarissa Sumanang (Biomedical Engineering Study Program, Department of Physics, Faculty of Science and Technology, Universitas Airlangga, Indonesia)
Quinolina Thifal (Biomedical Engineering Study Program, Department of Physics, Faculty of Science and Technology, Universitas Airlangga, Indonesia)
Akila Firdausi Harahap (Biomedical Engineering Study Program, Department of Physics, Faculty of Science and Technology, Universitas Airlangga, Indonesia)
Akif Rahmatillah (1Biomedical Engineering Study Program, Department of Physics, Faculty of Science and Technology, Universitas Airlangga, Indonesia)
Alfian Pramudita Putra (Biomedical Engineering Study Program, Department of Physics, Faculty of Science and Technology, Universitas Airlangga, Indonesia)
Riries Rulaningtyas (Biomedical Engineering Study Program, Department of Physics, Faculty of Science and Technology, Universitas Airlangga, Indonesia)
Osmalina Nur Rahma, S.T., M.Si. (Biomedical Engineerig, Department of Physics, Faculty of Science and Technology, Universitas Airlangga, Indonesia)

Indonesian Applied Physics Letters
Vol. 4 No. 1 (2023): June

Concentration is the main asset for students and serves as an indicator of successful learning implementation. One of the abnormal disturbances that can occur in a child's concentration development is attention deficit hyperactivity disorder (ADHD). The prevalence of ADHD in Indonesia in 2014 reached 12.81 million people due to delayed management in addressing ADHD. Therefore, early detection of ADHD is necessary for prevention. ADHD detection can be done by testing the level of concentration using a concentration grid. However, a method is needed that can be applied to uncooperative young children who are not familiar with numbers. Therefore, research was conducted with an innovative approach using a combination of EEG-ECG to classify concentration levels. The data used in this study were primary data from 4 participants with 5 repetitions. The data were processed in the preprocessing stage, which involved noise filtering and Butterworth filtering. The features used in this study were BPM (beats per minute), alpha, theta, and beta EEG signals, which would later become inputs for the Adaptive Neuro-Fuzzy Inference System (ANFIS). The output shows that the combination of EEG-ECG has the potential to predict concentration test results. Using BPM, alpha, theta, and beta signals can serve as parameters for predicting the concentration grid test values using ANFIS effectively. In the ANFIS model with 4 features, an accuracy of 99.997% was obtained for the training data and 80.2142% for the testing data. This result could be developed for early detection of ADHD based on concentration levels so the learning implementation could be more effective.