Ceylan Murat, Ceylan Rahime, Dirgenali Fatma, Kara Sadik, Ozbay Yüksel
Selçuk University, Department of Electronics Engineering, 42075 Konya, Turkey.
Comput Biol Med. 2007 Jan;37(1):28-36. doi: 10.1016/j.compbiomed.2005.08.005. Epub 2005 Dec 15.
In this study, carotid arterial Doppler ultrasound signals were acquired from left carotid arteries of 38 patients and 40 healthy volunteers. The patient group had an established diagnosis of the early phase of atherosclerosis through coronary or aortofemoropopliteal angiographies. Results were classified using complex-valued artificial neural network (CVANN). Principal component analysis (PCA) and fuzzy c-means clustering (FCM) algorithm were used to make a CVANN system more effective. For this aim, before classifying with CVANN, PCA method was used for feature extraction in PCA-CVANN architecture and FCM algorithm was used for data set reduction in FCM-CVANN architecture. Training and test data were selected randomly using 10-fold cross validation. PCA-CVANN and FCM-CVANN architectures classified healthy and unhealthy subjects for training and test data with about 100% correct classification rate. These results shown that PCA-CVANN and FCM-CVANN classified Doppler signals successfully.
在本研究中，从38例患者和40名健康志愿者的左颈动脉采集了颈动脉多普勒超声信号。患者组已通过冠状动脉造影或主动脉股腘动脉造影确诊为动脉粥样硬化早期。使用复值人工神经网络（CVANN）对结果进行分类。主成分分析（PCA）和模糊c均值聚类（FCM）算法用于提高CVANN系统的有效性。为此，在使用CVANN进行分类之前，PCA方法用于PCA-CVANN架构中的特征提取，FCM算法用于FCM-CVANN架构中的数据集约简。使用10折交叉验证随机选择训练和测试数据。PCA-CVANN和FCM-CVANN架构对训练和测试数据中的健康和不健康受试者进行分类，分类正确率约为100%。这些结果表明，PCA-CVANN和FCM-CVANN成功地对多普勒信号进行了分类。
