Objectives

This study focuses on integration of anatomical left ventricle myocardium features and optimized extreme learning machine (ELM) for discrimination of subjects with normal, mild, moderate and severe abnormal ejection fraction (EF). The physiological alterations in myocardium have diagnostic relevance to the etiology of cardiovascular diseases (CVD) with reduced EF.

Materials and Methods

This assessment is carried out on cardiovascular magnetic resonance (CMR) images of 104 subjects available in Kaggle Second Annual Data Science Bowl. The Segment CMR framework is used to segment myocardium from cardiac MR images, and it is subdivided into 16 sectors. 86 clinically significant anatomical features are extracted and subjected to ELM framework. Regularization coefficient and hidden neurons influence the prediction accuracy of ELM. The optimal value for these parameters is achieved with the butterfly optimizer (BO). A comparative study of BOELM framework with different activation functions and feature set has been conducted.

Results

Among the individual feature set, myocardial volume at ED gives a better classification accuracy of 83.3% compared to others. Further, the given BOELM framework is able to provide higher multi-class accuracy of 95.2% with the entire feature set than ELM. Better discrimination of healthy and moderate abnormal subjects is achieved than other sub groups.

Conclusion

The combined anatomical sector wise myocardial features assisted BOELM is able to predict the severity levels of CVDs. Thus, this study supports the radiologists in the mass diagnosis of cardiac disorder.

中文翻译：

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使用基于心肌解剖特征的优化极限学习机方法进行心血管疾病严重程度检测

目标

本研究侧重于整合解剖左心室心肌特征和优化的极限学习机 (ELM)，以区分正常、轻度、中度和重度异常射血分数 (EF) 的受试者。心肌的生理变化与 EF 降低的心血管疾病 (CVD) 的病因学具有诊断相关性。

材料和方法

这项评估是在 Kaggle 第二届年度数据科学碗中提供的 104 名受试者的心血管磁共振 (CMR) 图像上进行的。Segment CMR框架用于从心脏MR图像中分割心肌，它被细分为16个扇区。提取了 86 个具有临床意义的解剖特征并将其置于 ELM 框架中。正则化系数和隐藏神经元影响 ELM 的预测精度。这些参数的最佳值是通过蝶形优化器 (BO) 实现的。已经对具有不同激活函数和特征集的BOELM框架进行了比较研究。

结果

在单个特征集中，与其他特征相比，ED 的心肌体积具有更好的分类准确度，达到 83.3%。此外，与 ELM 相比，给定的 BOELM 框架能够在整个特征集上提供 95.2% 的更高多类准确率。与其他亚组相比，可以更好地区分健康和中度异常受试者。

结论

结合解剖扇区智能心肌特征辅助 BOELM 能够预测 CVD 的严重程度。因此，这项研究支持放射科医师对心脏疾病的大规模诊断。