Identification of Schizophrenic Patients and Healthy Controls Based on Musical Perception Using AEP Analysis

Schizophrenia (SZ) is one of the least understood and most costly mental disorders in terms of human suffering and societal costs. The diagnosis of SZ involves individual evaluations of the severity of the characteristic symptoms and coupled with social or occupational dysfunction, for at least six months in the absence of another diagnosis that would better account for the presentation. However the pathophysiology of schizophrenia remains unclear and the patients with schizophrenia are believed to be heterogeneous, there are no laboratory tests or biomarkers used as directly diagnostic tools at present. The aim of this study is to propose an objectively distinguishing method for identification SZ by analyzing physiological information. An auditory event-related potential (AEP)-based schizophrenia-classification system in which passive and response-free listening to musical intervals and chord stimuli were used to reduce the workload involved in identifying SZ is developed. A feature selection strategy combines discrimination and correlation analysis is also proposed to select key features and remove redundancy. Two AEP components, amplitude of N1 evoked by chord stimuli and amplitude of P2 evoked by interval stimuli from the frontal lobe, were screened and fed to the linear discriminate analysis (LDA) for classification. The accuracy reaches 83.33% through leave-one-out cross-validation from 12 SZ and 12 healthy subjects. Due to the advantages associated with the recording process and feature analysis, it is expected to be a useful tool to help us understand abnormalities of brain function and a potential biomarker to subgroup endophenotypes in schizophrenia.