a. Department of Biomedical Engineering; b. Department of Medical Big Data; c. Department of Rehabilitation, Chinese PLA
General Hospital, Beijing 100853, China
Abstract:Objective To establish an automatic classification model for schizophrenia using deep learning method, and to provide
a reference for the diagnosis of schizophrenia in clinical practice. Methods We extracted the energy, phase, signal-to-noise ratio
and differential entropy of auditory steady state responses as input features of models and compared the performance of deep belief
networks (DBNs) and support vector machines (SVM) using accuracy, sensitivity, specificity and receiver operating characteristic
curve. Results The accuracy, sensitivity, specificity and area under the curve of DBNs model were 85.6%, 88.33%, 75.50% and 0.88,
respectively. The diagnostic capacity of DBNs model was significantly higher than that of three kinds of SVM models based on linear
kernel, radial basis function kernel and sigmoid kernel. Conclusion The diagnostic model based on DBNs can effectively assist
clinicians in the diagnosis of patients with schizophrenia and achieve the early detection of disease.
许飞飞a,应俊b,张立宁c,宋亚男b,谢惠敏c,陈广飞a. 基于深度学习方法的精神分裂症听觉稳态诱发电位分析[J]. 中国医疗设备, 2020, 35(1): 18-22.
XU Feifeia, YING Junb, ZHANG Liningc, SONG Yananb, XIE Huiminc, CHEN Guangfeia. Auditory Steady State Responses of Schizophrenia Based On Deep Learning Algorithm. China Medical Devices, 2020, 35(1): 18-22.