基于尺度不变变换的深度学习框架,用于使用心电图信号检测心力衰竭
Manas Ranjan Prusty1, Trilok Nath Pandey2, Pujala Shree Lekha3
1Centre for Cyber Physical Systems, Vellore Institute of Technology, Chennai, 600127, Tamil Nadu, India.
Scientific reports
|February 1, 2024
概括
这项研究引入了一种新的卷积神经网络 (CNN) 系统,使用规模不变特征转换 (SIFT) 进行早期心脏病检测. SIFT-CNN模型准确地分类心电图 (ECG) 信号,在心律失常等疾病中实现高精度.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 心脏病是全球主要的死亡原因.
- 早期发现和治疗对于改善患者的治疗结果至关重要.
- 电心电图 (ECG) 分析通过监测心跳波动,提供了对心脏健康的洞察.
研究的目的:
- 开发和评估一种新型自动化卷积神经网络 (CNN) 系统,用于准确检测心脏病.
- 利用规模不变特征转换 (SIFT) 来从心电图信号中增强特征提取.
- 将心电图信号分为心律失常 (ARR),充血性心力衰竭 (CHF) 和正常鼻腔节律 (NSR).
主要方法:
- 使用自定义的卷积神经网络 (CNN) 架构.
- 使用规模不变特征转换 (SIFT) 来从心电图信号图像中提取独特特征.
- 将SIFT与其他特征提取技术 (如HOG和SURF) 相比较.
主要成果:
- 在162张心电图像的数据集上,SIFT-CNN模型实现了99.78%的准确性和99.78%的F1得分.
- 与使用HOG (99.45%准确率) 和SURF (78%准确率) 的模型相比,实现了更高的性能.
- 对于检测心律失常,充血性心力衰竭和正常的鼻腔节律,证明了高分类准确性.
结论:
- 拟议的SIFT-CNN模型代表了自动心脏病检测的重大进步.
- 将SIFT特征提取与定制CNN模型相结合,提供了一种新且高效的方法.
- 这种方法表现出卓越的性能,优于现有的基于心电图数据对心脏病的分类模型.
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