IEEE Trans Biomed Eng. 2018 Sep;65(9):1964-1974. doi: 10.1109/TBME.2018.2843258. Epub 2018 Jun 1.
In this paper, we accurately detect the state-sequence first heart sound (S1)-systole-second heart sound (S2)-diastole, i.e., the positions of S1 and S2, in heart sound recordings. We propose an event detection approach without explicitly incorporating a priori information of the state duration. This renders it also applicable to recordings with cardiac arrhythmia and extendable to the detection of extra heart sounds (third and fourth heart sound), heart murmurs, as well as other acoustic events.
We use data from the 2016 PhysioNet/CinC Challenge, containing heart sound recordings and annotations of the heart sound states. From the recordings, we extract spectral and envelope features and investigate the performance of different deep recurrent neural network (DRNN) architectures to detect the state sequence. We use virtual adversarial training, dropout, and data augmentation for regularization.
We compare our results with the state-of-the-art method and achieve an average score for the four events of the state sequence of ${\bf F}_{1}\approx 96$% on an independent test set.
Our approach shows state-of-the-art performance carefully evaluated on the 2016 PhysioNet/CinC Challenge dataset.
In this work, we introduce a new methodology for the segmentation of heart sounds, suggesting an event detection approach with DRNNs using spectral or envelope features.
在本文中，我们准确地检测心音记录中的心音状态序列（S1）-收缩期-第二心音（S2）-舒张期，即 S1 和 S2 的位置。我们提出了一种无需显式纳入状态持续时间先验信息的事件检测方法。这使其也适用于心律失常的记录，并可扩展到检测额外的心音（第三和第四心音）、心杂音以及其他声学事件。
我们使用来自 2016 年 PhysioNet/CinC 挑战赛的数据，其中包含心音记录和心音状态的注释。我们从记录中提取光谱和包络特征，并研究不同深度递归神经网络（DRNN）架构检测状态序列的性能。我们使用虚拟对抗训练、随机失活和数据增强进行正则化。
我们将我们的结果与最先进的方法进行了比较，并在独立测试集上对状态序列的四个事件的平均得分达到了${\bf F}_{1}\approx96%$。
我们的方法在经过精心评估的 2016 年 PhysioNet/CinC 挑战赛数据集上表现出了最先进的性能。
在这项工作中，我们引入了一种新的心音分段方法，提出了一种使用 DRNN 进行基于光谱或包络特征的事件检测方法。
