论文标题

使用深神经网络和组合优化的生理信号的细分和最佳区域选择

Segmentation and Optimal Region Selection of Physiological Signals using Deep Neural Networks and Combinatorial Optimization

论文作者

Oliveira, Jorge, Carvalho, Margarida, Nogueira, Diogo Marcelo, Coimbra, Miguel

论文摘要

生理信号(例如心电图和声音图)通常被嘈杂的来源损坏。通常,人工智能算法分析信号无论其质量如何。另一方面,医生采用完全正交的策略。他们没有评估整个记录,而是搜索一个很容易检测到基本和异常波的细分市场,只有那时才尝试预后。受到这一事实的启发,根据用户定义的标准,一种新算法会自动为后处理阶段选择最佳段。在此过程中,使用神经网络来计算每个样本的输出概率分布。使用上述数量,设计了图,而状态过渡约束则物理施加到图表中,并使用一组约束来检索记录的子集,以最大程度地提高用户提出的可能性函数。在两个应用程序中对开发的框架进行了测试和验证。在这两种情况下,系统性能都显着提高,例如,与文献中的标准方法相比,心脏声音分割,灵敏度增加了2.4%。

Physiological signals, such as the electrocardiogram and the phonocardiogram are very often corrupted by noisy sources. Usually, artificial intelligent algorithms analyze the signal regardless of its quality. On the other hand, physicians use a completely orthogonal strategy. They do not assess the entire recording, instead they search for a segment where the fundamental and abnormal waves are easily detected, and only then a prognostic is attempted. Inspired by this fact, a new algorithm that automatically selects an optimal segment for a post-processing stage, according to a criteria defined by the user is proposed. In the process, a Neural Network is used to compute the output state probability distribution for each sample. Using the aforementioned quantities, a graph is designed, whereas state transition constraints are physically imposed into the graph and a set of constraints are used to retrieve a subset of the recording that maximizes the likelihood function, proposed by the user. The developed framework is tested and validated in two applications. In both cases, the system performance is boosted significantly, e.g in heart sound segmentation, sensitivity increases 2.4% when compared to the standard approaches in the literature.

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