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Automatic cardiac phase detection of mitral and aortic valves stenosis and regurgitation via localization of active valves

机译:通过活动瓣膜的定位自动检测二尖瓣和主动脉瓣狭窄和反流的心脏相位

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摘要

Heart auscultation is a primary way of heart diagnosis and monitoring. A critical step of heart sound analysis is dividing the sound into its basic phases, known as S1, systole, S2 and diastole. Heart sound phase detection becomes a challenging issue when more than one heart valve problems are simultaneously existed. In this paper, a powerful approach is proposed that properly determine phases of heart sounds with Mitral and Aortic stenosis and Regurgitation murmurs. In this approach, heart valves spatial temporal information investigated by a multiple channel data recording system is employed to determine different heart phases. Since heart valves are very near to each other and are located in the reverberant environment of chest, according to its anatomy, the group-delay information of Recursively Applied and Projected-Multiple Signal Classification spectra (RAP-MUSIC) is utilized to localize active heart valves. The proposed segmentation algorithm is applied to some normal and abnormal (mitral and aortic valve malfunctions) heart sounds recorded by a rectangular microphone array consisted of six sensors. To evaluate the benefits of the proposed method, it is compared with the basic and Tunable Q wavelet and S-transform based segmentation methods, by conducting some proper experiments. The obtained results show that the proposed algorithm is considerably superior to the mentioned methods The average positive predictive value, sensitivity and accuracy results from the proposed segmentation algorithm are 97.4%, 95.5%, and 93%, respectively. (C) 2017 Elsevier Ltd. All rights reserved.
机译:心脏听诊是心脏诊断和监测的主要方法。心音分析的关键步骤是将声音分为基本阶段,分别称为S1,收缩期,S2和舒张期。当同时存在多个心脏瓣膜问题时,心音相位检测成为一个具有挑战性的问题。在本文中,提出了一种有效的方法,可以正确确定具有二尖瓣和主动脉瓣狭窄以及反流性杂音的心音相位。在这种方法中,采用由多通道数据记录系统调查的心脏瓣膜时空信息来确定不同的心脏相位。由于心脏瓣膜彼此非常靠近并且位于胸部的混响环境中,因此根据其解剖结构,递归应用和投影多信号分类谱(RAP-MUSIC)的群延迟信息可用于定位活动心脏阀门。提出的分割算法应用于由六个传感器组成的矩形麦克风阵列记录的某些正常和异常(双瓣膜和主动脉瓣功能异常)心音。为了评估该方法的优点,通过进行一些适当的实验,将其与基于基本和可调谐Q小波和S变换的分割方法进行了比较。所得结果表明,该算法明显优于上述方法。分割算法的平均正预测值,灵敏度和准确性结果分别为97.4%,95.5%和93%。 (C)2017 Elsevier Ltd.保留所有权利。

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