恶性心律不整的检测使用合成分析模拟方法的动脉血压信号
Yongxin Chou1, Miao Yang2, Yiyun Sun1
1School of Electrical and Automatic Engineering, Changshu Institute of Technology, Suzhou 215500, China.
Medical engineering & physics
|February 16, 2024
概括
这项研究引入了一种新的合成分析 (SA) 建模方法,使用动脉血压 (ABP) 信号来检测四种恶性心律不整 (MA). 随机森林 (RF) 分类器表现出高准确度,显示了m-health应用的潜力.
科学领域:
- 生物医学工程 生物医学工程
- 心血管健康 心血管健康
- 信号处理 信号处理
背景情况:
- 恶性心律不整 (MAs),如胸肌梗塞,心力衰竭和心室动/心力衰竭,是突然心脏死亡的关键原因.
- 早期检测MAs对于及时干预和改善患者结果至关重要.
- 动脉血压 (ABP) 信号为MA检测提供了一个易于获取的生理数据来源.
研究的目的:
- 开发和验证一种新的合成分析 (SA) 建模方法,用于使用动脉血压 (ABP) 信号检测四种特定恶性心律不整 (MA).
- 量化描述与MA相关的ABP波的病理和生理变化.
- 为了比较概率神经网络 (PNN) 和随机森林 (RF) 分类器的诊断性能,用于MA检测.
主要方法:
- 合成分析 (SA) 建模方法应用于ABP信号,以生成MAs和健康受试者的平均模型.
- 用12个参数来定量描述ABP波浪模型的变化.
- 概率神经网络 (PNN) 和随机森林 (RF) 分类器被训练并使用来自Fantasia和2015 PhysioNet/CinC挑战数据库的数据进行评估.
主要成果:
- SA建模成功地从ABP平均模型中提取了病理和生理变化,在组之间观察到显著差异 (p < 0.05).
- 随机森林 (RF) 分类器在检测MA时显著超过PNN分类器.
- 射频分类器实现了高的平均卡帕系数 (KC),最高记录在99.895±0.545%和97.083±0.67%的整体平均KC.
结论:
- 拟议的SA建模方法有效地识别了暗示恶性心律失常的ABP信号中的关键变化.
- 与PNN相比,随机森林分类器在MA检测方面表现优越,与PNN相比.
- 这种方法在m-health应用中显示出可靠的MA诊断的巨大潜力,比现有研究提供了更好的性能.
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