Recent advances in technology have facilitated the prospect of automatic cardiac auscultation by using digital stethoscopes. This in turn creates the need for development of algorithms capable of automatic segmentation of the heart sound. Pediatric heart sound segmentation is a challenging task due to various factors including the significant influence of respiration on the heart sound. This project studies the application of homomorphic filtering and Hidden Markov Model for the purpose of pediatric heart sound segmentation. The efficacy of the proposed method is evaluated on a publicly available dataset and its performance is compared with those of three other existing methods. The results show that our proposed method achieves accuracy of 92.4% ±1.1% and 93.5% ±1.1% in identification of first and second heart sound components, and is superior to four other existing methods in term of accuracy or time complexity.
|Commitee:||Ebert, Todd, Englert, Burkhard|
|School:||California State University, Long Beach|
|Department:||Computer Engineering and Computer Science|
|School Location:||United States -- California|
|Source:||MAI 53/06M(E), Masters Abstracts International|
|Subjects:||Computer Engineering, Artificial intelligence, Computer science|
|Keywords:||Heart sound segmentation, Machine learning, Pattern recognition|
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