Automatic classification of fatty acid amide hydrolase polymorphism genotype based on EEG signal

Epilepsy is a brain abnormality neurological disorder and is life-threatening, affecting the behavior and lifestyle of many people worldwide. Neurologists commonly use an electroencephalogram (EEG) to manually interpret the brain's electrical activity. Some patients respond differently to drugs as the effective doses will be ineffective in some patients or cause adverse drug reactions in others and genetic factors seem to be involved in variable responses in some cases. On the other hand, performing genetic tests to detect the genotype of patients is usually invasive, expensive, and time-consuming. Estimating the patient's genotype using data such as information obtained from EEG, can be considered a significant achievement. Genes involved in the functioning of the endocannabinoid system are known to be a critical element in the physiological function the brain and nerves perform, and the defects in the activity of this system have been confirmed in the pathobiology of diseases such as epilepsy. Levels of endocannabinoids can be influenced by gene polymorphisms such as fatty acid amide hydrolase (FAAH) gene single nucleotide polymorphism. Because the FAAH controls the endocannabinoids levels by hydrolyzing and terminating the activity of the anandamide within the central nervous system (CNS) and also the origin of seizures which is the electrical storm of brain neurons, it is thought that there can be a relation between the FAAH enzyme and the brain oscillations. Identifying genotypes that are related to EEG variation in epilepsy is crucial for clinical epilepsy monitoring and controlling brain oscillations. In this paper, we show the FAAH rs2295633 polymorphism can be detected (classified) using EEG signals (as a common diagnostic tool) following the convolutional neural network (CNN) classification method. The multichannel time series data of EEG collected through a sliding window technique was given as input into a deep CNN model to find a probable relationship between EEG and FAAH CC, CT, and TT genotype classes. The proposed method reached a precision of 94.15% (± 0.38%), accuracy of 94.09% (± 0.41%), sensitivity of 94.14% (± 0.38%), specificity 97.04% (± 0.20%), and F1 score 94.11% (± 0.40%) in detecting the rs2295633 polymorphism based on EEG patterns in epilepsy. Therefore, we can conclude that the FAAH rs2295633 polymorphism may modulate brain activity and EEG patterns. However, more extensive multifactorial studies are necessary to show the preci