Computer Aided Diagnosis System for multiple sclerosis disease based on phase to amplitude coupling in covert visual attention. (February 2019) - Ahmadi, Amirmasoud; Davoudi, Saeideh; Daliri, Mohammad Reza
Highlights: We developed a new CAD system using EEG signals based on phase to amplitude coupling. The aim was to diagnose multiple sclerosis (MS) disease during the covert visual attention tasks. We used machine learning algorithms to identify whether the signals are indication of disease or not. The electrodes and frequency band combinations which made the most contributions in each tasks were illustrated. The system achieved 91.2% accuracy based on phase to amplitude features. Abstract: Background and objective: Computer Aided Diagnosis (CAD) techniques have widely been used in research to detect the neurological abnormalities and improve the consistency of diagnosis and treatment in medicine. In this study, a new CAD system based on EEG signals was developed. The motivation for the development of the CAD system was to diagnose multiple sclerosis (MS) disease during covert visual attention tasks. It is worth noting that research of this kind on the efficacy of attention tasks is limited in scope for MS patients; therefore, it is vital to develop a feature of EEG to characterize the patient's state with high sensitivity and specificity. Methods: We evaluated the use of phase–amplitude coupling (PAC) of EEG signals to diagnose MS. It is assumed that the role of PAC for information encoding during visual attention in MS is greatly unknown; therefore, we made an attempt to investigate it via CAD systems. The EEG signals were recorded from healthy and MS patients while performing new visual attention tasks. Machine learning algorithms were also used to identify the EEG signals as to whether the disease existed or not. The challenge regarding the dimensionality of the extracted features was addressed through selecting the relevant and efficient features using T-test and Bhattacharyya distance criteria, and the validity of the system was assessed through leave-one-subject-out cross-validation method. Results: Our findings indicated that online sequential extreme learning machine (OS-ELM) classifier with T-test feature selection method yielded peak accuracy, sensitivity and specificity in both color and direction tasks. These values were 91%, 83% and 96% for color task, and 90%, 82% and 96% for the direction task. Conclusions: Based on the results, it can be concluded that this procedure can be used for the automatic diagnosis of early MS, and can also facilitate the treatment assessment in patients.