基于变压器的心电图分类,用于早期检测心律不整
Sunnia Ikram1, Amna Ikram2, Harvinder Singh3
1Department of Software Engineering, The Islamia University of Bahawalpur, Bahawalpur, Pakistan.
本研究引入了用于自动心电图 (ECG) 分类的变压器深度学习模型,改进了心血管疾病检测. 该模型有效地使用先进的信号处理技术对各种心律异常进行分类.
科学领域:
- 生物医学工程 生物医学工程
- 医疗保健中的人工智能
- 心脏病学 心脏病学
背景情况:
- 电心电图 (ECG) 的分类对于诊断心血管疾病至关重要.
- 目前的方法可能缺乏用于早期和准确检测所需的精度.
- 自动化分析有望增强诊断能力.
研究的目的:
- 开发和评估基于变压器的深度学习框架,用于自动化的心电图分类.
- 集成先进的信号预处理,特征选择和维度减小,以提高性能.
- 评估模型在分类各种心律不整时的有效性.
主要方法:
- 信号预处理:对原始心电图数据进行无声化,规范化和重新标记.
- 特性选择和维度减少:主要组件分析 (PCA) 和相关性分析.
- 基于变压器的深度学习模型,经过优化损失函数和规范化训练.
主要成果:
- 变压器模型在MIT-BIH基准数据集上取得了强的表现.
- 证明了正常,心房过早收缩 (APC),心室过早收缩 (VPC) 和融合节拍的有效分类.
- 使用t分布式随机邻居嵌入 (t-SNE) 的可视化显示了明确的类可分离性.
结论:
- 基于变压器的模型显示了生物医学信号处理中自动化ECG诊断的巨大潜力.
- 拟议的框架为改善心血管疾病监测提供了一个可扩展的方法.
- 对于实时和资源受限的应用程序,需要进一步优化.
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