Abstract
Background Schizophrenia is a highly heterogeneous disease with different clinical subtypes.Artificial intelligence technology represented by deep learning models has provided considerable benefits for the electroencephalogram(EEG)-based schizophrenia diagnosis,treatment and research,however,to date little research has been conducted regarding any of these benefits among Chinese schizophrenic patients.Objective To investigate the application of deep learning techniques utilizing EEG parameters for the diagnosis of first-episode schizophrenia and grading of EEG abnormalities in patients,with the aim of contributing to improved clinical diagnosis and treatment strategies for the disorder.Methods From January 2020 to January 2023,a total of 130 patients with first-episode schizophrenia who met the diagnostic criteria of International Classification of Diseases,tenth edition(ICD-10),and attended at the Third People's Hospital of Fuyang,along with 150 health checkup examinees,were enrolled.All of them underwent EEG examination.An optimized long short-term memory(LSTM)deep learning model was developed utilizing EEG signals.Ten-fold cross-validation method was employed to evaluate the model's performance.The dataset was then split into two components:a training set(90%)for LSTM model development and a test set(10%)for validation.The accuracy,recall rate,precision,F1-score,schizophrenia diagnosis and EEG abnormality grading were used as evaluation indicators,and the results of the proposed model were compared to the assessments made by experienced psychiatrists.Results For schizophrenia diagnosis,the modeling group achieved the following performance metrics:precision(94.40±3.03)%,recall rate(94.30±3.23)%,accuracy(94.60±2.22)%,and F1-score(94.20±2.20)%.In the validation group,the corresponding metrics were precision(90.90±2.85)%,recall rate(92.20±1.14)%,accuracy(92.20±1.69)%,and F1-score(91.50±1.78)%.Statistical analysis revealed no significant differences between the LSTM diagnostic model and the experienced psychiatrists in terms of precision,recall rate,accuracy,and F1-score for schizophrenia diagnosis(χ2=1.500,0.750,2.722,1.056,P>0.05).The modeling group demonstrated an accuracy rate of(91.71±1.73)%in grading EEG abnormalities.For Grade 1 abnormalities,the modeling group reported a precision of(96.40±2.39)%,a recall rate of(94.77±1.40)%,and an F1-score of(95.55±1.14)%.In the case of Grade 2 abnormalities,the precision was(85.89±2.04)%,the recall rate was(88.10±6.18)%,and the F1-score was(87.06±3.12)%.For the more severe Grade 3 abnormalities,the modeling group's precision was(79.61±7.33)%,the recall rate was(81.79±9.87)%,and the F1-score was(80.41±6.79)%.Additionally,the validation group exhibited an accuracy rate of(85.61±6.16)%.The precision,recall rate,and F1-score for Grade 1 abnormalities were(91.43±6.25)%,(92.64±9.65)%and(91.56±4.83)%,respectively.For Grade 2 abnormalities,these metrics were(71.17±19.02)%,(77.64±17.24)%and(71.88±11.33)%.In the case of Grade 3 abnormalities,the precision was(90.00±21.08)%,the recall rate was(80.00±25.82)%,and the F1-score was(81.67±19.95)%.There was no significant difference in the accuracy,recall,accuracy and F1 value between LSTM model and senior doctors in evaluating the abnormal degree of EEG in schizophrenia(χ2=0.098,0.036,0.020,0.336,P>0.05).The LSTM model takes less time to diagnose schizophrenia and EEG abnormalities than senior doctors,and the differences were statistically significant(t=57.147,43.104,P<0.01).Conclusion The study utilizes an EEG-based LSTM deep learning model for diagnosing first-episode schizophrenia and grading EEG abnormalities,and the model not only matches the performance of experienced psychiatrists but also significantly reduces the time required for diagnosis.关键词
深度学习/脑电图/精神分裂症/诊断/异常程度分级/长短期记忆模型Key words
Deep learning model/EEG/Schizophrenia/Diagnosis/Abnormality grading/Long short-term memory model分类
医药卫生
