用深度神经网络算法与12导电心电图进行心力衰竭严重程度分类的模型,用于患有多变性心肌病的患者
Sanshiro Togo1, Yuki Sugiura1, Sayumi Suzuki2
1Hamamatsu University School of Medicine, Hamamatsu, Japan.
Open heart
|December 6, 2023
概括
深度学习模型现在可以使用心电图数据对过度缩性心肌病患者的心力衰竭严重程度进行分类. 这种方法显示出对非侵入性评估疾病进展和指导治疗策略的希望.
科学领域:
- 心脏病学 心脏病学
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 电心电图 (ECG) 异常是高性心肌病 (HCM) 的特征.
- 虽然心电图有助于对心律失常的风险分层,但它在使用深度学习 (DL) 确定心力衰竭 (HF) 严重程度方面的实用性仍未得到充分探索.
- 识别HF严重程度对于管理HCM患者至关重要.
研究的目的:
- 开发和评估一个DL模型,使用12个导电心电图数据对HCM患者的HF严重程度进行分类.
- 评估模型在区分不同级别的HF严重程度方面的表现.
- 探索基于ECG的DL在HCM的非侵入性HF评估中的潜力.
主要方法:
- 使用来自HCM和非HCM患者的12导电图数据开发了一个残余神经网络.
- 使用纽约心脏协会的功能类别,脑内尿素水平的N终端前激素和堪萨斯城心肌病问卷 (KCCQ) -12分数来对HF严重程度进行分类.
- 采用转移学习,在PTB-XL心电图数据集上预训练模型,以应对有限的样本大小.
主要成果:
- 对于轻度至中度HF分类,DL模型实现了0.745的F1加权平均得分和0.750的精度.
- 当根据KCCQ-12分数对患者进行分组时,观察到类似的表现.
- 分析显示,QRS波是轻度至中度高频率的关键指标,而严重高频率的模式更具变化.
结论:
- 一个深度神经网络模型有效地使用12导电图数据对HCM患者的HF严重程度进行分类.
- 开发的DL算法显示,它有可能成为评估HCM中HF状态的有价值工具.
- 这种方法可以促进非侵入性监测和心力衰竭在HCM的管理.
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