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使用多参数极性表示和深度学习进行心力衰竭评估

Mohanad Alkhodari, Leontios J Hadjileontiadis, Herbert F Jelinek

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|December 12, 2023

概括

这项研究引入了一种使用心率变化和患者数据的新型心力衰竭查方法. 这种方法实现了高精度,为心血管疾病评估提供了个性化,补充心声谱的工具.

科学领域:

心脏病学心脏病学
人工智能的人工智能
生物医学工程生物医学工程

背景情况:

心力衰竭每年影响数百万人,目前的查缺乏昼夜和患者特定的数据.
心声图是标准的,但不包括心率变化或患者个人资料.
需要先进的诊断工具来改善心血管疾病的评估.

研究的目的:

开发一种新的方法,整合24小时心率变化 (HRV) 和患者个人资料信息.
为了创建一个多参数,颜色编码的极性表示,用于心血管疾病评估.
为了验证一个深度学习模型来预测心力衰竭群体,使用这个新的表征.

主要方法:

整合24小时HRV特征和患者个人资料数据.
开发一个多参数,彩色编码的极性表示.
在7575个生成图像上训练一个深度学习模型,以预测心力衰竭类别.

主要成果:

深度学习模型实现了93%的整体准确度,88%的灵敏度和95%的特异性.
记录了高性能指标,包括AUROC为0.88和AUPR为0.79.
这种新方法在分类心力衰竭群体方面表现出有效性.

结论:

拟议的极性表示和深度学习模型为心血管疾病评估提供了一个新的协议.
这种方法补充了心声回声学,通过结合昼夜心律和个性化的患者数据.
临床实施可以增强个性化的心血管医学,减少医疗保健提供者的负担.

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