单线,远程医疗心电图信号中的QRS检测:对比测试开源算法
Florian Kristof1, Maximilian Kapsecker1,2, Leon Nissen2
1TUM School of Computation, Information, and Technology, Technical University of Munich, Garching bei München, Germany.
PLOS digital health
|August 13, 2024
概括
在分析远程医疗心电图 (ECG) 方面,Neurokit和新南威尔士大学的QRS探测器表现最好. 然而,低质量的ECG的性能下降,这凸显了临床决策中需要仔细选择数据的必要性.
科学领域:
- 生物医学工程 生物医学工程
- 心脏病学 心脏病学
- 数字健康数字健康
背景情况:
- 自动心电图 (ECG) 分析对于检测心律失常至关重要.
- 与临床心电图相比,远程医疗心电图带来了独特的挑战,因为与临床心电图相比,噪音增加了.
- 识别强大的QRS复杂检测算法对于可靠的远程医疗ECG解释至关重要.
研究的目的:
- 评估和确定用于远程医疗心电图分析的最佳开源QRS探测器.
- 为了比较各种QRS探测器在监督和无监督ECG数据集上的性能.
- 评估心电图质量和心房对QRS检测准确性的影响.
主要方法:
- 在六个数据集中评估了18个开源QRS检测器,包括监督和远程医疗ECG.
- 利用SAFER研究数据集 (479个单线电图) 进行远程医疗电图分析.
- 性能与手册注释进行了比较,F1分数作为主要指标.
主要成果:
- 十二个QRS探测器在监督的ECG上实现了高性能 (F1 ≥0.96).
- 较少的检测器在远程医疗ECG上表现出色:在TELE ECG数据库上有五个,在高质量的SAFER数据上有六个.
- 低质量的SAFER远程医疗心电图的表现明显较差 (F1 0.78-0.84).
结论:
- 纽罗基特和新南威尔士大学的QRS探测器在远程医疗ECG上表现出卓越的性能.
- 这些高性能探测器在高质量的远程医疗数据上是有效的,但在低质量的记录方面却存在困难.
- 确保对低质量的心电图进行适当处理对于远程医疗环境中准确的临床决策至关重要.
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