Objective: We consider challenges in accurate segmentation of heart sound signals recorded under noisy clinical environments for subsequent classification of pathological events. Existing state-of-the-art solutions to heart sound segmentation use probabilistic models such as hidden Markov models (HMMs), which, however, are limited by its observation independence assumption and rely on pre-extraction of noise-robust features. Methods: We propose a Markov-switching autoregressive (MSAR) process to model the raw heart sound signals directly, which allows efficient segmentation of the cyclical heart sound states according to the distinct dependence structure in each state. To enhance robustness, we extend the MSAR model to a switching linear dynamic system (SLDS) that jointly model both the switching AR dynamics of underlying heart sound signals and the noise effects. We introduce a novel algorithm via fusion of switching Kalman filter and the duration-dependent Viterbi algorithm, which incorporates the duration of heart sound states to improve state decoding. Results: Evaluated on Physionet/CinC Challenge 2016 dataset, the proposed MSAR-SLDS approach significantly outperforms the hidden semi-Markov model (HSMM) in heart sound segmentation based on raw signals and comparable to a feature-based HSMM. The segmented labels were then used to train Gaussian-mixture HMM classifier for identification of abnormal beats, achieving high average precision of 86.1% on the same dataset including very noisy recordings. Conclusion: The proposed approach shows noticeable performance in heart sound segmentation and classification on a large noisy dataset. Significance: It is potentially useful in developing automated heart monitoring systems for pre-screening of heart pathologies.

中文翻译：

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马尔可夫切换模型在心音分割和分类中的应用

目的：我们考虑在嘈杂的临床环境下记录的心音信号进行准确分割以进行后续病理事件分类时所面临的挑战。现有的最新心音分割解决方案使用概率模型，例如隐马尔可夫模型（HMM），但是，其受观测独立性假设的限制，并且依赖于对噪声鲁棒特征的预提取。方法：我们提出了一种马尔可夫切换自回归（MSAR）过程来直接对原始心音信号进行建模，从而可以根据每种状态下明显的依赖性结构对循环心音状态进行有效的分割。为了增强鲁棒性，我们将MSAR模型扩展到一个切换线性动态系统（SLDS），该系统共同对基础心音信号的切换AR动态和噪声效果进行建模。我们通过结合切换卡尔曼滤波器和依赖于持续时间的维特比算法引入了一种新颖的算法，该算法结合了心音状态的持续时间以改善状态解码。结果：对Physionet / CinC Challenge 2016数据集进行了评估，提出的MSAR-SLDS方法在基于原始信号的心音分割中明显优于隐藏的半马尔可夫模型（HSMM），与基于特征的HSMM相当。然后将分割后的标签用于训练高斯混合HMM分类器，以识别异常心跳，在包括非常嘈杂的录音的同一数据集上实现了86.1％的高平均精度。结论：所提出的方法在大型嘈杂数据集的心音分割和分类中显示出显着的性能。启示：在开发用于心脏疾病预筛查的自动心脏监测系统中可能很有用。