Sudarshan Vidya K, Acharya U Rajendra, Ng E Y K, Tan Ru San, Chou Siaw Meng, Ghista Dhanjoo N
School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore; Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore.
Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore; Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Malaysia.
Comput Biol Med. 2016 Apr 1;71:241-51. doi: 10.1016/j.compbiomed.2016.01.029. Epub 2016 Feb 10.
Early expansion of infarcted zone after Acute Myocardial Infarction (AMI) has serious short and long-term consequences and contributes to increased mortality. Thus, identification of moderate and severe phases of AMI before leading to other catastrophic post-MI medical condition is most important for aggressive treatment and management. Advanced image processing techniques together with robust classifier using two-dimensional (2D) echocardiograms may aid for automated classification of the extent of infarcted myocardium. Therefore, this paper proposes novel algorithms namely Curvelet Transform (CT) and Local Configuration Pattern (LCP) for an automated detection of normal, moderately infarcted and severely infarcted myocardium using 2D echocardiograms. The methodology extracts the LCP features from CT coefficients of echocardiograms. The obtained features are subjected to Marginal Fisher Analysis (MFA) dimensionality reduction technique followed by fuzzy entropy based ranking method. Different classifiers are used to differentiate ranked features into three classes normal, moderate and severely infarcted based on the extent of damage to myocardium. The developed algorithm has achieved an accuracy of 98.99%, sensitivity of 98.48% and specificity of 100% for Support Vector Machine (SVM) classifier using only six features. Furthermore, we have developed an integrated index called Myocardial Infarction Risk Index (MIRI) to detect the normal, moderately and severely infarcted myocardium using a single number. The proposed system may aid the clinicians in faster identification and quantification of the extent of infarcted myocardium using 2D echocardiogram. This system may also aid in identifying the person at risk of developing heart failure based on the extent of infarcted myocardium.
急性心肌梗死（AMI）后梗死区域的早期扩展具有严重的短期和长期后果，并会导致死亡率增加。因此，在导致其他心肌梗死后灾难性医疗状况之前识别AMI的中度和重度阶段对于积极治疗和管理至关重要。先进的图像处理技术与使用二维（2D）超声心动图的强大分类器相结合，可能有助于自动分类梗死心肌的范围。因此，本文提出了一种新的算法，即曲波变换（CT）和局部配置模式（LCP），用于使用二维超声心动图自动检测正常、中度梗死和重度梗死心肌。该方法从超声心动图的CT系数中提取LCP特征。将获得的特征进行边际Fisher分析（MFA）降维技术，然后采用基于模糊熵的排序方法。使用不同的分类器根据心肌损伤程度将排序后的特征分为正常、中度和重度梗死三类。所开发的算法仅使用六个特征，对于支持向量机（SVM）分类器，准确率达到了98.99%，灵敏度达到了98.48%，特异性达到了100%。此外，我们还开发了一个名为心肌梗死风险指数（MIRI）的综合指数，用于使用单个数字检测正常、中度和重度梗死心肌。所提出的系统可能有助于临床医生使用二维超声心动图更快地识别和量化梗死心肌的范围。该系统还可能有助于根据梗死心肌的范围识别有发生心力衰竭风险的人。
