Qingshu Liu1, Xiaomei Wu2,3, Xiaojing Ma4. 1. Department of Electronic Engineering, Fudan University, Room 522 B, Science Building, 220 Handan Rd., Shanghai, China. 2. Department of Electronic Engineering, Fudan University, Room 522 B, Science Building, 220 Handan Rd., Shanghai, China. xiaomeiwu@fudan.edu.cn. 3. Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention (MICCAI) of Shanghai, 138 Medical College Rd., Shanghai, China. xiaomeiwu@fudan.edu.cn. 4. Children's Hospital of Fudan University, 399 Wanyuan Rd., Shanghai, China.
Abstract
BACKGROUND: There are two major challenges in automated heart sound analysis: segmentation and classification. An efficient segmentation is capable of providing valuable diagnostic information of patients. In addition, it is crucial for some feature-extraction based classification methods. Therefore, the segmentation of heart sound is of significant value. METHODS: This paper presents an automatic heart sound segmentation method that combines the time-domain analysis, frequency-domain analysis and time-frequency-domain analysis. Employing this method, the boundaries of heart sound components are first located, and the components are then recognized. Finally, the heart sounds are divided into several segments on the basis of the results of boundary localization and component identification. RESULTS: In order to evaluate the performance of the proposed method, quantitative experiments are performed on an authoritative heart sound database. The experimental results show that the boundary localization has a sensitivity (Se) of 100%, a positive predictive value (PPV) of 99.3% and an accuracy (Acc) of 99.93%. Moreover, the Se, PPV and Acc of component identification reach 98.63, 99.86 and 98.49%, respectively. CONCLUSION: The proposed method shows reliable performance on the segmentation of heart sounds. Compared with previous works, this method can be applied to not only normal heart sounds, but also the sounds with S3, S4 and murmurs, thus greatly increasing the applied range.
BACKGROUND: There are two major challenges in automated heart sound analysis: segmentation and classification. An efficient segmentation is capable of providing valuable diagnostic information of patients. In addition, it is crucial for some feature-extraction based classification methods. Therefore, the segmentation of heart sound is of significant value. METHODS: This paper presents an automatic heart sound segmentation method that combines the time-domain analysis, frequency-domain analysis and time-frequency-domain analysis. Employing this method, the boundaries of heart sound components are first located, and the components are then recognized. Finally, the heart sounds are divided into several segments on the basis of the results of boundary localization and component identification. RESULTS: In order to evaluate the performance of the proposed method, quantitative experiments are performed on an authoritative heart sound database. The experimental results show that the boundary localization has a sensitivity (Se) of 100%, a positive predictive value (PPV) of 99.3% and an accuracy (Acc) of 99.93%. Moreover, the Se, PPV and Acc of component identification reach 98.63, 99.86 and 98.49%, respectively. CONCLUSION: The proposed method shows reliable performance on the segmentation of heart sounds. Compared with previous works, this method can be applied to not only normal heart sounds, but also the sounds with S3, S4 and murmurs, thus greatly increasing the applied range.
Authors: Ping Wang; Chu Sing Lim; Sunita Chauhan; Jong Yong A Foo; Venkataraman Anantharaman Journal: Ann Biomed Eng Date: 2006-12-14 Impact factor: 3.934