电心电图分析,以评估心力衰竭的风险与保存的喷射分数:一个深度学习模型
Zheng Gao1, Yuqing Yang1, Zhiqiang Yang2,3
1Department of Cardiology, The First Hospital of Hebei Medical University, Shijiazhuang, China.
ESC heart failure
|October 28, 2024
概括
一个深度学习模型 (DLM) 有效地选使用心电图 (ECG) 的心力衰竭与保存喷射率 (HFpEF) 风险. 这种具有成本效益的工具有助于早期检测和改善HFpEF的管理.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 医学诊断 医学诊断 医学诊断
背景情况:
- 由于缺乏有效的查方法,心力衰竭与保存的喷射分数 (HFpEF) 构成了重大的临床挑战.
- 早期识别HFpEF对于及时干预和改善患者结果至关重要.
研究的目的:
- 开发和验证一个深度学习模型 (DLM) 以准确和成本效益的选HFpEF风险.
- 通过使用心电图 (ECG) 评估DLM的性能,以识别高风险HFpEF的个体.
主要方法:
- 一个卷积神经网络长期短期记忆 (CNN-LSTM) DLM被开发和训练在从队列A (n=238) 的心电图数据.
- HFpEF风险是由左心室末端透气压 (LVEDP) 和临床症状来定义的,LVEDP通过侵入性导管测量.
- 在一个独立的数据集,B队列 (n=117) 上,对DLM进行了前性验证.
主要成果:
- 在培训 (A队列) 和验证 (B队列) 队列中,DLM在HFpEF风险评估中实现了78%的准确性.
- 在B组中,DLM表现为71.9%的特异性和71.7%的敏感性.
- 与低风险组相比,DLM识别的高风险HFpEF组显示出糖尿病和肥胖的更高患病率,以及较低的CCB使用率,与BNP和E/E"等传统标志物不同.
结论:
- 开发的DLM提供了一种准确且具有成本效益的方法,用于使用ECG选高风险.
- 这种人工智能驱动的方法有可能促进早期检测,并增强HFpEF患者的临床管理策略.
相关概念视频
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Introduction
An electrocardiogram (ECG) is a diagnostic tool for identifying cardiac conditions such as arrhythmias, conduction abnormalities, and myocardial ischemia.
Definition
An electrocardiogram (ECG) visualizes the heart's electrical activity by tracing the electrical movement associated with each heartbeat on a graph or monitor. As the heart beats, an electrical wave passes through it, correlating with the cardiac cycle events.
Parts of an ECG
An ECG utilizes electrodes on the skin...
An electrocardiogram (ECG) is a diagnostic tool for identifying cardiac conditions such as arrhythmias, conduction abnormalities, and myocardial ischemia.
Definition
An electrocardiogram (ECG) visualizes the heart's electrical activity by tracing the electrical movement associated with each heartbeat on a graph or monitor. As the heart beats, an electrical wave passes through it, correlating with the cardiac cycle events.
Parts of an ECG
An ECG utilizes electrodes on the skin...
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