Sengur Abdulkadir
Department of Electronics and Computer Science, Technical Education Faculty, Firat University, Elazig, Turkey.
Comput Biol Med. 2008 Mar;38(3):329-38. doi: 10.1016/j.compbiomed.2007.11.004. Epub 2008 Jan 4.
In the last two decades, the use of artificial intelligence methods in medical analysis is increasing. This is mainly because the effectiveness of classification and detection systems have improved a great deal to help the medical experts in diagnosing. In this work, we investigate the use of principal component analysis (PCA), artificial immune system (AIS) and fuzzy k-NN to determine the normal and abnormal heart valves from the Doppler heart sounds. The proposed heart valve disorder detection system is composed of three stages. The first stage is the pre-processing stage. Filtering, normalization and white de-noising are the processes that were used in this stage. The feature extraction is the second stage. During feature extraction stage, wavelet packet decomposition was used. As a next step, wavelet entropy was considered as features. For reducing the complexity of the system, PCA was used for feature reduction. In the classification stage, AIS and fuzzy k-NN were used. To evaluate the performance of the proposed methodology, a comparative study is realized by using a data set containing 215 samples. The validation of the proposed method is measured by using the sensitivity and specificity parameters; 95.9% sensitivity and 96% specificity rate was obtained.
在过去二十年中，人工智能方法在医学分析中的应用不断增加。这主要是因为分类和检测系统的有效性有了很大提高，有助于医学专家进行诊断。在这项工作中，我们研究了主成分分析（PCA）、人工免疫系统（AIS）和模糊k近邻算法，以从多普勒心音中确定正常和异常的心脏瓣膜。所提出的心脏瓣膜疾病检测系统由三个阶段组成。第一阶段是预处理阶段。滤波、归一化和去白噪声是该阶段使用的处理过程。特征提取是第二阶段。在特征提取阶段，使用了小波包分解。接下来，将小波熵视为特征。为了降低系统的复杂性，使用PCA进行特征约简。在分类阶段，使用了AIS和模糊k近邻算法。为了评估所提出方法的性能，通过使用包含215个样本的数据集进行了比较研究。所提出方法的有效性通过敏感性和特异性参数来衡量；获得了95.9%的敏感性和96%的特异性率。
