用自适应式窗口和深度神经网络对心电图进行节拍细分
S M Isuru Niroshana1, Satoshi Kuroda2, Kazuyuki Tanaka2
1Biomedical Information Engineering Lab, The University of Aizu, Fukushima, 965-8580, Japan.
Scientific reports
|July 7, 2023
概括
这项研究引入了一种新的心电图 (ECG) 节拍细分技术,使用CNN和自适应窗口. 该方法准确识别正规和不规则的心跳,这对于可靠的心电图分析和患者监测至关重要.
科学领域:
- 生物医学工程 生物医学工程
- 信号处理 信号处理
- 医疗保健中的人工智能
背景情况:
- 准确的心电图 (ECG) 分析对于患者监测和治疗后护理至关重要.
- 节拍细分是可靠的自动ECG分类的关键先决条件.
- 现有的方法可能无法达到临床应用所需的忠实性和信心.
研究的目的:
- 开发和验证一个强大的心电图节拍细分技术.
- 为了提高识别心脏周期事件和细分心跳的准确性和可靠性.
- 为了使自动化心电图分析更有信心的临床应用.
主要方法:
- 开发了一个与自适应窗口算法集成的卷积神经网络 (CNN) 模型.
- 适应性窗口算法识别心脏循环事件,以便精确细分.
- 该技术从心电图信号中对正规和不规则的心跳进行细分.
主要成果:
- 在MIT-BIH心跳检测数据集上获得了99.08%的准确性和F1分数.
- 在MIT-BIH数据集上,在边界确定方面证明了99.25%的准确性.
- 在欧洲S-T (98.3%准确率,97.4%精确率) 和Fantasia (99.4%准确率,99.4%精确率) 数据库中展示了高性能.
结论:
- 拟议的ECG节拍细分技术在多个数据集中显示出高精度和可靠性.
- 该方法能够准确细分正规和不规则的节拍,这支持其临床实用性.
- 这种算法为增强对各种ECG分析应用程序的信心提供了一个有希望的解决方案.
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