通过基于心电图像矢量化的分类来改善心脏病诊断
AbdulAdhim Ashtaiwi1, Tarek Khalifa1, Omar Alirr1
1College of Engineering and Technology, American University of the Middle East, Kuwait.
Heliyon
|September 27, 2024
概括
本研究介绍了一种图像向量化技术,用于更高效的心电图 (ECG) 分析. 这种方法通过提高机器学习模型性能来增强早期心脏病检测和预防.
科学领域:
- 生物医学工程 生物医学工程
- 心脏病学 心脏病学
- 医疗保健中的人工智能
背景情况:
- 心脏病对健康构成重大挑战,需要早期发现和预防策略.
- 电心电图 (ECG) 对于诊断心脏疾病至关重要.
- 自动化心电图分析和特征提取可以提高诊断准确性和效率.
研究的目的:
- 开发一个高效的图像向量化技术用于ECG分析.
- 提高基于心电图的心脏病分类模型的准确性.
- 为了减少ECG分析所需的计算资源.
主要方法:
- 开发了一种图像矢量化技术,包括图像剪切,网格线移除和像素赋值.
- 使用这种技术提取特征向量,并与VGG16.0的特征向量进行比较.
- 人工神经网络 (ANN) 使用图像矢量化衍生特征数据集进行训练.
主要成果:
- 图像向量化技术产生了比VGG16短589倍的特征向量.
- 这种减少显著降低了内存需求和计算功率需求.
- 经过图像向量化训练的机器学习算法比CNN和VGG16.6等传统方法表现更好.
结论:
- 拟议的图像向量化技术为ECG特征提取提供了一种高效的方法.
- 这种方法提高了用于心脏病检测的机器学习模型的性能.
- 该技术有望为更有效和更容易在线检测和预防心脏病提供希望.
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