Arjoune Youness, Nguyen Trong N, Doroshow Robin W, Shekhar Raj
Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Hospital, Washington, DC 20010, USA.
AusculTech Dx, Silver Spring, MD 20902, USA.
IEEE Access. 2024;12:7747-7761. doi: 10.1109/access.2024.3351570. Epub 2024 Jan 8.
Heart sound segmentation has been shown to improve the performance of artificial intelligence (AI)-based auscultation decision support systems increasingly viewed as a solution to compensate for eroding auscultatory skills and the associated subjectivity. Various segmentation approaches with demonstrated performance can be utilized for this task, but their robustness can suffer in the presence of noise. A noise-robust heart sound segmentation algorithm was developed and its accuracy was tested using two datasets: the CirCor DigiScope Phonocardiogram dataset and an in-house dataset - a heart murmur library collected at the Children's National Hospital (CNH). On the CirCor dataset, our segmentation algorithm marked the boundaries of the primary heart sounds S1 and S2 with an accuracy of 0.28 ms and 0.29 ms, respectively, and correctly identified the actual positive segments with a sensitivity of 97.44%. The algorithm also executed four times faster than a logistic regression hidden semi-Markov model. On the CNH dataset, the algorithm succeeded in 87.4% cases, achieving a 6% increase in segmentation success rate demonstrated by our original Shannon energy-based algorithm. Accurate heart sound segmentation is critical to supporting and accelerating AI research in cardiovascular diseases. The proposed algorithm increases the robustness of heart sound segmentation to noise and viability for clinical use.
心音分割已被证明可提高基于人工智能（AI）的听诊决策支持系统的性能，该系统日益被视为一种解决方案，以弥补不断下降的听诊技能及相关的主观性。各种具有已证明性能的分割方法可用于此任务，但在存在噪声的情况下，它们的鲁棒性可能会受到影响。开发了一种抗噪声的心音分割算法，并使用两个数据集测试了其准确性：CirCor DigiScope心音图数据集和一个内部数据集——在儿童国家医院（CNH）收集的心脏杂音库。在CirCor数据集上，我们的分割算法标记主要心音S1和S2的边界时，准确率分别为0.28毫秒和0.29毫秒，并以97.44%的灵敏度正确识别实际的阳性段。该算法的执行速度也比逻辑回归隐藏半马尔可夫模型快四倍。在CNH数据集上，该算法在87.4%的病例中取得成功，比我们原来基于香农能量的算法所展示的分割成功率提高了6%。准确的心音分割对于支持和加速心血管疾病的AI研究至关重要。所提出的算法提高了心音分割对噪声的鲁棒性以及临床应用的可行性。
