School of Technology and Health, Royal Institute of Technology, Huddinge, Sweden.
Sensors (Basel). 2013 Aug 7;13(8):10074-86. doi: 10.3390/s130810074.
After cancer and cardio-vascular disease, stroke is the third greatest cause of death worldwide. Given the limitations of the current imaging technologies used for stroke diagnosis, the need for portable non-invasive and less expensive diagnostic tools is crucial. Previous studies have suggested that electrical bioimpedance (EBI) measurements from the head might contain useful clinical information related to changes produced in the cerebral tissue after the onset of stroke. In this study, we recorded 720 EBI Spectroscopy (EBIS) measurements from two different head regions of 18 hemispheres of nine subjects. Three of these subjects had suffered a unilateral haemorrhagic stroke. A number of features based on structural and intrinsic frequency-dependent properties of the cerebral tissue were extracted. These features were then fed into a classification tree. The results show that a full classification of damaged and undamaged cerebral tissue was achieved after three hierarchical classification steps. Lastly, the performance of the classification tree was assessed using Leave-One-Out Cross Validation (LOO-CV). Despite the fact that the results of this study are limited to a small database, and the observations obtained must be verified further with a larger cohort of patients, these findings confirm that EBI measurements contain useful information for assessing on the health of brain tissue after stroke and supports the hypothesis that classification features based on Cole parameters, spectral information and the geometry of EBIS measurements are useful to differentiate between healthy and stroke damaged brain tissue.
中风是全球第三大致死疾病，仅次于癌症和心血管疾病。鉴于目前用于中风诊断的成像技术存在局限性，因此急需便携式、非侵入性且价格更低廉的诊断工具。先前的研究表明，头部的生物阻抗（EBI）测量可能包含与中风发作后大脑组织变化相关的有用临床信息。在这项研究中，我们从九名患者的 18 个半脑的两个不同头部区域记录了 720 次 EBI 光谱（EBIS）测量。其中三人患有单侧脑出血性中风。从大脑组织的结构和固有频率相关特性中提取了许多基于特征的参数。然后，这些特征被输入到分类树中。结果表明，在经过三个层次的分类步骤后，能够实现对受损和未受损脑组织的完全分类。最后，使用留一法交叉验证（LOO-CV）评估了分类树的性能。尽管这项研究的结果仅限于一个小型数据库，并且必须用更大的患者队列进一步验证观察结果，但这些发现证实了 EBI 测量包含有关中风后评估脑组织健康的有用信息，并支持了这样的假设，即基于 Cole 参数、光谱信息和 EBIS 测量的几何形状的分类特征可用于区分健康和中风受损的脑组织。
