Erol Fatih S, Uysal Hadi, Ergün Uçman, Barişçi Necaattin, Serhathoğlu Selami, Hardalaç Firat
Department of Neurosurgery, Faculty of Medicine, Firat University, Elazig, Turkey.
J Med Syst. 2005 Jun;29(3):205-15. doi: 10.1007/s10916-005-5181-x.
In this study it is aimed to assess the posttraumatic cerebral hemodynamia in minor head injured patients. Eighty patients with minor head injury (Group 1) evaluated in the early 8 h of posttraumatic period between July 2003 and February 2004. The control group (Group 2) has composed of 32 healthy people. Bilateral blood flow velocities of middle cerebral arteries (MCA) had measured using transtemporal technique while internal carotid arteries were evaluated by submandibular examination. Two different mathematical models such as the traditional statistical method on the basis of logistic regression and a multi-layer perceptron (MLP) neural network are used to classify the age, sex, velocitiy parameters of MCA, mean velocity of extracranial ICAs and V(MCA)/ V(ICA) ratios. The neural network was trained, cross-validated and tested with subject's transcranial Doppler signals. As a result of these classifications, we found the success rate of logistic regression, the success rate of MLP neural network is 88.2 and 89.1%, respectively. The classification results show that MLP neural network is offering the best results in the case of diagnosis.
本研究旨在评估轻度颅脑损伤患者的创伤后脑血流动力学。2003年7月至2004年2月期间，80例轻度颅脑损伤患者（第1组）在创伤后早期8小时内接受评估。对照组（第2组）由32名健康人组成。使用颞部技术测量双侧大脑中动脉（MCA）的血流速度，同时通过下颌下检查评估颈内动脉。使用两种不同的数学模型，如基于逻辑回归的传统统计方法和多层感知器（MLP）神经网络，对年龄、性别、MCA速度参数、颅外颈内动脉平均速度和V（MCA）/V（ICA）比值进行分类。利用受试者的经颅多普勒信号对神经网络进行训练、交叉验证和测试。这些分类的结果显示，逻辑回归的成功率、MLP神经网络的成功率分别为88.2%和89.1%。分类结果表明，在诊断方面MLP神经网络提供了最佳结果。
