Schizophrenia Recognition Based on Gramian Angular Field Combining Activation Features: A Functional Near-Infrared Spectroscopy Study

In recent years, the incidence of schizophrenia has been increasing globally. Combination of functional near-infrared spectroscopy with verbal fluency task (fNIRS-VFT) provides an objective neurofunctional assessment tool for psychiatrists in auxiliary diagnosis. However, current methods face challenges such as insufficient time series feature extracting, inadequate feature utilization, and unstable robustness of single or few features. In this study we designed a fNIRS classification strategy by gramian angular field (GAF) coding integrating activation information. This strategy utilizes GAF normalized by the global maximum and minimum to via fNIRS signals into 2D representations virtual images for SCZ recognition. Specifically, fNIRS data of 200 participants were constructed and processed into virtual images. Then, the classification performance of five different processing methods, including directly using activation sequence, recurrence plot, Markov transition field, GAF image without activation, and traditional fNIRS features is compared. Finally, this study additionally collected 40 cases of fNIRS-VFT data as an external test set. Compared with EfftivenetV2 with GAF images without activate information (73.4% accuracy) and CNN HbO signals classification (75.5% accuracy), the SCZ recognition accuracy based on GAF images combining activation information improved by 7.6% and 4.9%. The ShuffleNetV2 achieved the best performance with an accuracy of 81.0% on the cross-validation dataset, and obtained an accuracy of 72.0% on the external test set. Our findings indicate that GAF virtual image coding approach integrating activation information forms a new strategy for supporting SCZ screening and diagnosis. It further promotes the application of fNIRS technology in the field of clinical psychiatric disorders.