使用心电图信号的基于机器学习的心血管疾病检测:性能与复杂性
Huy Pham1, Konstantin Egorov2, Alexey Kazakov3
1Department of Computer Science, HSE University, Moscow, Russia.
Frontiers in cardiovascular medicine
|August 16, 2023
概括
1D ResNet模型有效地检测ECG中的心律失常,与其他方法相比,提供更高的准确性和能源效率. 这一进步有望实现更快,更可靠的心脏病诊断.
科学领域:
- 心脏病学和医疗信息学
- 医疗保健中的人工智能
背景情况:
- 心血管疾病是一个主要的健康问题,需要有效的诊断工具.
- 电心电图 (ECG) 的解释至关重要,但需要专业知识和时间.
- 开发用于早期心脏异常检测的自动化方法对于改善患者护理至关重要.
研究的目的:
- 评估使用心电图记录对心脏病分类的现代方法.
- 为了比较不同机器学习模型的性能和效率.
- 调查用于ECG分析的能源消耗和模型解释性.
主要方法:
- 使用深度学习图像分类器的Poincaré表示.
- 将一维卷积神经网络 (1D CNN) 应用于原始的心电图信号.
- 使用XGBoost模型进行时间序列特征预测.
主要成果:
- 1D ResNet 模型获得了最高的 F1 评分 (85% 在 CinC 2017,71% 在 CinC 2020),超过了挑战中获胜的解决方案.
- 一维卷积模型显示出高特异性和更高的能效 (更低的功耗和二氧化碳排放).
- 卡雷方法在心房动 (AF) 中表现有前途,但其他心律失常并非如此;XGBoost的推断时间很长.
结论:
- 一维卷积模型,特别是一维ResNet,对于ECG的心脏病分类非常有效和高效.
- 1D CNN中的剩余连接在简化模型的同时保持性能.
- 对电力消耗和模型解释的分析为计算机制和效率提供了洞察力.
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