Abstract
BackgroundSchizophrenia is a kind of serious mental illness. Due to the lack of an objective physiological data supporting and a unified data analysis method, doctors can only rely on the subjective experience of the data to distinguish normal people and patients, which easily lead to misdiagnosis. In recent years, functional Near-Infrared Spectroscopy (fNIRS) has been widely used in clinical diagnosis, it can get the hemoglobin concentration through the variation of optical intensity.MethodsFirstly, the prefrontal brain networks were constructed based on oxy-Hb signals from 52-channel fNIRS data of schizophrenia and healthy controls. Then, Complex Brain Network Analysis (CBNA) was used to extract features from the prefrontal brain networks. Finally, a classier based on Support Vector Machine (SVM) is designed and trained to discriminate schizophrenia from healthy controls. We recruited a sample which contains 34 healthy controls and 42 schizophrenia patients to do the one-back memory task. The hemoglobin response was measured in the prefrontal cortex during the task using a 52-channel fNIRS system.ResultsThe experimental results indicate that the proposed method can achieve a satisfactory classification with the accuracy of 85.5%, 92.8% for schizophrenia samples and 76.5% for healthy controls. Also, our results suggested that fNIRS has the potential capacity to be an effective objective biomarker for the diagnosis of schizophrenia.ConclusionsOur results suggested that, using the appropriate classification method, fNIRS has the potential capacity to be an effective objective biomarker for the diagnosis of schizophrenia.
Highlights
Schizophrenia is a kind of serious mental illness
In [7], authors measured the changes of the oxy-Hb signal during multiple cognitive tasks from two functional Near-Infrared Spectroscopy (fNIRS) channels located in the bilateral prefrontal areas and applied stepwise linear discriminant analysis to distinguish patients with schizophrenia from healthy subjects
The testing result of schizophrenics and healthy controls is shown in Table 1, where 39 of the 42 schizophrenia and 26 of 34 health controls were discriminated successfully on Oxygenated hemoglobin (Oxy-Hb)/Deoxy-Hb signal
Summary
DataSet description The dataset was provided by Peking University Sixth Hospital. The fNIRS measurements were conducted with. Schizophrenic discrimination method The process of schizophrenia discrimination includes preprocessing the fNIRS data, constructing brain network, feature extraction, training the classifier, cross validation and testing. The Pearson correlation coefficient of fNIRS time series between different nodes is usually calculated to quantify the relationship between them. After quantizing the relationship between 52 channels,we got the Pearson correlation coefficient matrix, it is necessary to choose an appropriate threshold T in order to construct the edge of the nodes. After reduce the dimension of eigenvector of each attribute, we find there is a significant difference of the node degree when the threshold is set to 0.21. In the leave-one-out cross validation, the dataset is separated into 76 samples, every sample will be as a test sample and the rest samples will be a training set, continues rounds
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