在心电图中通过人工智能驱动的分析识别心室功能障碍指标
Hisaki Makimoto1,2,3,4, Takayuki Okatani2, Masanori Suganuma2
1Cardiovascular Centre, Jichi Medical University, Shimotsuke 329-0498, Japan.
Bioengineering (Basel, Switzerland)
|November 27, 2024
概括
人工智能可以使用心电图 (ECG) 检测心室功能障碍. 双节拍心电图和特定波形段,特别是从P波到T波,提供了最精确的分类.
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:
- 人工智能 (AI) 在通过心电图 (ECG) 来识别心室功能障碍方面表现有前途.
- 具体的心电图波形表明心脏功能障碍仍然在很大程度上未被定义.
- 在临床实践中,评估心室功能的准确,非侵入性方法至关重要.
研究的目的:
- 使用心电图数据开发和验证人工智能模型,用于检测使用心电图数据的左心室喷射率 (LVEF) 降低.
- 为了确定特定的心电图配置和波形段,最有效的诊断心室功能障碍.
- 确定不同心电图导线和段的诊断效用,以评估心脏功能.
主要方法:
- 分析了来自日本和德国的17422名患者的心电图和心声回声数据.
- 开发10层卷积神经网络 (CNN) 用于LVEF分类 (<50%).
- 使用四倍交叉验证对各种心电图配置 (3s条,单拍,两拍叠加) 和段 (PQRST,QRST,P,QRS,PQRS) 的模型性能进行评估.
主要成果:
- 与单拍和3s条形模型相比,两拍心电图配置在检测心室功能障碍方面表现优异.
- 单拍模型确定了肢体领头部I和aVR作为功能障碍的特别迹象.
- 从QRS复合体到T波的心电图段是最有信息的,而P波段进一步提高了诊断准确性.
结论:
- 通过人工智能处理的双节拍心电图分析,可以非常精确地对心室功能进行分类.
- 特定的心电图段,特别是从P波到T波的特定心电图段,更有效地评估心室功能障碍.
- 电脑心电图导线I和aVR在评估心室功能障碍方面具有显著的诊断效用.
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