基于深度学习的高效节律失常检测使用智能手表ECG心电图
Herwin Alayn Huillcen Baca1, Flor de Luz Palomino Valdivia1
1Faculty of Engineering, Academic Department of Engineering and Information Technology, Jose Maria Arguedas National University, Andahuaylas 03701, Peru.
Sensors (Basel, Switzerland)
|September 13, 2025
概括
这项研究介绍了一种高效的1D CNN模型,用于从智能手表心电图中检测心律不整. 该模型在多类检测中表现出高精度,支持早期诊断和临床应用.
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:
- 心血管疾病,包括心律失常,是全球主要的死亡原因.
- 早期和准确的诊断心律失常是关键的,但受到电图解释主观性和噪音的挑战.
- 目前用于心律失常检测的深度学习模型往往忽视了效率和临床适用性,仅专注于开放数据集.
研究的目的:
- 提出一种高效的1D卷积神经网络 (CNN) 模型,用于使用智能手表的心电图 (ECG) 检测心律不整.
- 开发一个适合实际临床部署的模型,用于持续监测和早期检测心律失常.
- 评估模型在二元和多类心律失常检测任务中的效率和有效性.
主要方法:
- 开发了一种高效的1D CNN架构,用于基于智能手表心电图的心律失常检测.
- 使用UMass医学院Simband数据集训练和评估一个二进制心律失常检测模型.
- 使用MIT-BIH心律失常数据库验证了一种多类心律失常检测模型,并将其与最先进的方法进行比较.
主要成果:
- 二进制模型实现了64.81%的准确度,89.47%的灵敏度和6.25%的特异性,突出了它的可靠性,特别是在特异性方面.
- 该模型以120万个参数和68.48MFlops的计算效率进行了证明.
- 多类模型以99.57%的精度,99.57%的灵敏度和99.47%的特异性实现了高性能,使其成为最先进的建议之一.
结论:
- 提出的1D CNN模型是有效和可靠的,用于检测智能手表心电图的心律失常.
- 该模型的性能,特别是在多类检测中,支持其在早期心律失常诊断和监测中实际临床应用的潜力.
- 这项工作解决了有效的深度学习模型中的差距,用于使用可穿戴技术检测现实世界的心律失常.
关键词:
在美国,CNN是CNN.这是一个ECGECGECGECGECG.这是一个PPGPPG.检测心律失常的检测方式心血管疾病心血管疾病深度学习是一种深度学习.电心电图 (ECG) 是一种心电图.心率是指心率是如何发生的.智能手表 智能手表更多相关视频
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