一个新的混合CNN-变压器模型用于检测心律失常,而不需要R峰识别,使用斯托克威尔变换
Donghyeon Kim1, Kyoung Ryul Lee2, Dong Seok Lim2
1Department of Defense Acquisition Program, Kwangwoon University, Seoul, 01897, Republic of Korea.
Scientific reports
|March 6, 2025
概括
本研究引入了一种新的混合深度学习模型,用于使用心电图 (ECG) 信号进行心律失常的分类. 该模型在没有R峰检测的情况下实现了高精度,改进了基于心电图的诊断和实时监测.
科学领域:
- 生物医学工程 生物医学工程
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 电心电图 (ECG) 信号对于诊断心律失常至关重要.
- 准确和有效的心律失常分类对于患者的监测和治疗至关重要.
- 传统方法通常依赖于R峰检测,这可能是一个局限性.
研究的目的:
- 开发一种新的混合深度学习模型,用于自动地从心电图信号中对心律失常进行分类.
- 提高基于心电图的心律失常诊断的准确性和效率.
- 创建一个不需要R峰检测的模型.
主要方法:
- 利用斯托克威尔变换从时间序列ECG数据中提取特征.
- 采用混合深度学习架构,将卷积神经网络 (CNN) 和变压器网络结合起来.
- CNN捕获了局部模式,而变压器学习了频率转换的心电图信号的长期依赖性.
主要成果:
- 在Icentia11k数据集 (4种心律失常类) 上获得了97.8%的准确性.
- 在MIT-BIH数据集 (5种心律失常类) 上获得了99.58%的准确性.
- 与传统的基于CNN的模型相比,证明了更高的准确性和效率.
结论:
- 拟议的混合深度学习模型为心律失常的分类提供了强大而准确的解决方案.
- 该模型在没有R峰检测的情况下运行的能力提高了其在实时监控中的适用性.
- 这些发现支持自动化心电图分析的进步,以改善心脏护理.
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