脑缺血是一种常见的突发性脑外科疾病, 具有较高的致死致残率, 快速、 准确对脑缺血程度进行检测对脑缺血的诊断与治疗工作具有重要意义。 基于衰减全反射式太赫兹时域光谱技术, 分别对脑缺血时间为0, 0.5, 1, 2, 4, 6和24 h的大鼠脑脊液和血清进行光谱检测, 并对不同缺血时间的大鼠脑脊液和血清的吸收系数和折射率随缺血时间的变化规律进行分析。 结果表明, 与对照组相比, 不同缺血时间的大鼠脑脊液和血清的吸收系数和折射率均存在一定的差异。 在此基础上, 根据不同缺血时间的大鼠脑脊液和血清的吸收系数, 采用主成分分析法和机器学习算法对大鼠的脑缺血程度进行自动分类识别。 其中, 基于脑脊液吸收系数的支持向量机分类模型的识别准确率相对较高, 达到了89.3％。 该方法为脑缺血的早期诊断提供一种新的检测方法。

Cerebral ischemia is a common sudden cerebral surgical disease with a high lethality and disability rate. The rapid and accurate detection of cerebral ischemia is of great significance to the diagnosis and treatment of cerebral ischemia. Inthispaper, we performed spectroscopy on cerebrospinal fluid (CSF) and serum of rats with ischemia time of 0, 0.5, 1, 2, 4, 6 and 24 h respectively, based on attenuated total reflection terahertz time-domain spectroscopy (THz-TDS). The changes in absorption coefficient and refractive index of CSF and serum with different ischemic times were analyzed. The results showed that the absorption coefficient and refractive index of CSF and serum of rats with different ischemic times were somewhat different compared with the control group. Furthermore, according to the absorption coefficient of CSF and serum with different ischemic times, principal component analysis and machine learning algorithms were used to automatically classify and recognize the degree of cerebral ischemia in rats. Especially, the recognition accuracy of the support vector machine classification model based on the absorption coefficient of CSF is relatively high, reaching 89.3%. Combining terahertz spectroscopic detection of CSF and serum ofrats with machine learning algorithms provides a new and effective detection method for the early diagnosis of cerebral ischemia.