使用卷积神经网络进行胎儿心脏声音细分
概括
人工神经网络提高了胎儿心声细分的准确性,特别是在第二次心声. 这一进步有助于开发更精确的心脏病诊断工具,使用心声学.
科学领域:
- 生物医学工程 生物医学工程
- 信号处理 信号处理
- 人工智能在医学中的应用
背景情况:
- 声心录像 (心脏声音记录) 处理对于诊断心脏疾病至关重要.
- 对大多数诊断方法来说,精确的心电图 (PCG) 信号细分是必不可少的.
- 传统的方法,如隐藏的半马尔科夫模型 (HSMM) 与后勤回归是常见的,但可以改进.
研究的目的:
- 开发和评估基于U-net的卷积神经网络 (CNN),用于从腹部记录中对胎儿心脏声音进行细分.
- 为了比较CNN模型的性能与已建立的基于HSMM的心声细分的方法.
- 评估深度学习在改善胎儿心电图分析方面的潜力.
主要方法:
- 一个U-netCNN架构被用于心声细分.
- 该模型最初在一个开放的儿科数据集上进行了训练,随后使用胎儿数据进行了微调.
- 通过将CNN与两个HSMM方法 (先前开发的方法和具有后勤回归的HSMM) 进行比较来评估性能.
主要成果:
- 最好的U-net模型实现了第一个心声检测的92.2%PPV和84.9%F1,平均绝对误差为17.2±26.0ms.
- 对于第二次心声检测,U-net模型实现了88.1%的PPV和88.1%的F1,平均绝对误差为17.9±12.7 ms.
- 虽然第一个心声检测并没有始终超过HSMM方法,但第二个心声检测的准确性显著改善.
结论:
- 深度学习,特别是U-net CNNs,显示了从腹部记录中增强胎儿心脏声音细分的希望.
- 第二次心声检测的提高准确性表明,潜在的更精细的诊断能力.
- 未来的研究可以将专门训练的神经网络与HSMM时间建模相结合,从而在声心图分析中获得更高的准确性.
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