基于马尔科夫的神经网络用于心声细分:以原则的方式使用域名知识
IEEE journal of biomedical and health informatics
|September 6, 2023
概括
基于马尔科夫的神经网络 (MNN) 有效地细分心脏声音. 这种混合方法显著优于现有方法,并适应新数据,提高诊断准确度.
科学领域:
- 生物医学工程 生物医学工程
- 人工智能在医学中的应用
- 信号处理 信号处理
背景情况:
- 准确细分心脏声音对于诊断心脏疾病至关重要.
- 当前的方法通常依赖于纯粹的数据驱动方法,这可能会与一般化作斗争.
- 将统计模型与深度学习集成为提高绩效提供了一个有希望的途径.
研究的目的:
- 引入一种新的混合框架,即基于马尔科夫的神经网络 (MNN),用于心声细分.
- 对公开数据集的现有数据驱动方法来评估MNN的性能.
- 开发一个无监督学习算法,以适应MNN与未见的数据分布.
主要方法:
- 开发了一个混合端到端框架,将马尔科夫模型与人工神经网络 (ANN) 结合起来.
- 在MNN架构中使用一维卷积ANN.
- 提出了一个基于梯度的无监督学习算法,用于适应性学习.
主要成果:
- 在PhysioNet 2016和CirCor DigiScope 2022数据集上,MNN显著超过了最近的两个纯数据驱动的解决方案.
- 在PhysioNet 2016上实现了高性能指标:灵敏度 (0.947 ± 0.02) 和正预测值 (0.937 ± 0.025)
- 在未经监督的算法使用CirCor DigiScope 2022的预训练后,在未见的PhysioNet 2016数据上的正预测值中平均有3.90%的改善.
结论:
- MNNs代表了心声细分的强大混合方法,统一统计和数据驱动的技术.
- 拟议的无监督学习算法提高了MNN适应性和稳定性,以适应不同的数据分布.
- 这一框架具有显著的潜力,可以提高自动心脏听觉的准确性和可靠性.
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