DocumentCode :
2084351
Title :
Heart sound recognition algorithm based on Probabilistic neural network for evaluating cardiac contractility change trend
Author :
Xingming, Guo ; Shouzhong, Xiao ; Jing, Pan ; Yan, Yan ; Xin, Tan
Author_Institution :
Chongqing Univ., Chongqing
fYear :
2007
fDate :
23-27 May 2007
Firstpage :
260
Lastpage :
264
Abstract :
The paper discusses the recognition of heart sound for evaluating the cardiac contractility change trend, which includes heart sound samples recorded at different exercise condition. Especially, the recognition of heart sound recorded after great exercise workload is also discussed. The algorithm proposed consisted of two correlative methods. The first was used to recognize heart sound recorded at rest and after light exercise workloads by probabilistic neural network and the second was used to recognize heart sound recorded after great exercise workloads based on the knowledge of heart sound. Finally, the performance of the algorithm was evaluated using 45 digital heart sound recordings including normal and abnormal heart sound, which were recorded at rest and after light exercise workloads, and 28 digital heart sound recordings recorded after great exercise workloads. The result showed that over 94% of heart sound samples were classified and recognized correctly. This provides a basis for further heart sound analysis.
Keywords :
cardiology; echocardiography; medical signal processing; neural nets; cardiac contractility change trend; digital heart sound recordings; exercise workloads; heart sound analysis; heart sound recognition; probabilistic neural network; sound classification; Biomedical engineering; Cardiovascular diseases; Digital recording; Educational institutions; Heart valves; Hemodynamics; Medical diagnostic imaging; Neural networks; Signal processing; Signal processing algorithms;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Complex Medical Engineering, 2007. CME 2007. IEEE/ICME International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4244-1077-4
Electronic_ISBN :
978-1-4244-1078-1
Type :
conf
DOI :
10.1109/ICCME.2007.4381734
Filename :
4381734
Link To Document :
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