时频分析在精神分裂症认知障碍筛查中的应用综述

A Comprehensive Review of the Application of Time-Frequency Analysis in Screening for Cognitive Impairment in Schizophrenia

摘要: 精神分裂症是一种主要以认知功能受损为主要表现的严重疾病。近年来，学者们越来越关注精神分裂症患者脑结构与功能变化的相关性及其机制。因此，如何从神经心理层面有效筛查和识别潜在患者成为研究焦点。为更加全面捕捉大脑电活动模式，研究基于深度学习的脑电信号分析技术至关重要。时频分析是一种可以对脑电图(EEG)信号进行分解，揭示出不同时间窗口和频率范围内的电活动模式的技术，它对于理解大脑的功能状态、识别异常活动以及研究大脑疾病的机制具有重要意义。在精神分裂症的研究中，时频分析已被应用于EEG信号的处理和分析。本研究旨在通过时频分析方法，深入了解精神分裂症早期认知障碍的临床表现，期望揭示大脑在不同状态下的电活动模式，为早期检测和治疗提供新视角。

Abstract: Schizophrenia is a serious disease primarily manifested by impaired cognitive function. In recent years, scholars have increasingly focused on the correlation between brain structure and functional changes in patients with schizophrenia and its underlying mechanisms. Therefore, effectively screening and identifying potential patients from the neuropsychological perspective has become a research focus. To capture the brain’s electrical activity patterns more comprehensively, it is crucial to study EEG signal analysis techniques based on deep learning. Time-frequency analysis is a technique that can decompose electroencephalogram (EEG) signals and reveal electrical activity patterns across different time windows and frequency ranges. It is significant for understanding the functional state of the brain, identifying abnormal activities, and studying the mechanisms of brain diseases. In the study of schizophrenia, time-frequency analysis has been applied to the processing and analysis of EEG signals. This study aims to gain a deeper understanding of the clinical manifestations of early cognitive impairment in schizophrenia through time-frequency analysis methods, expecting to reveal the electrical activity patterns of the brain under different states and provide a new perspective for early detection and treatment.