Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/48815
Título: Phonocardiography signal segmentation for telemedicine environments
Autores/as: Murillo Rendón, Santiago
Hoyos, Cristian Castro
Travieso-Gonzales, Carlos M. 
Castellanos-Domínguez, Germán
Clasificación UNESCO: 3314 Tecnología médica
Palabras clave: Heart Sound Segmentation
Phonocardiogram
Telemedicine
Autocorrelation
Fecha de publicación: 2013
Publicación seriada: Lecture Notes in Computer Science 
Conferencia: 12th International Work-Conference on Artificial Neural Networks (IWANN) 
Resumen: In this paper, phonocardiography (PCG) segmentation methodology based on envelope detection is developed by using a time-scale representation and a synthetic electrocardiogram signal (EKG). The heart cycle duration is calculated by autocorrelation of S1-S2 sounds that are synchronized with the synthetic EKG. Two algorithms for noisy signal removal are implemented to ensure the detection of signals with low signal to noise ratio. Approach is tested in a PCG database holding 232 recordings. Results show an achieved accuracy up of 90%, thus, overperforming three state-of-the-art PCG segmentation techniques used to compare the proposed approach. Additionally, the synthetic EKG is built by estimation of heart rate length, thus it does not use a patient recording EKG, reducing the computational cost and the amount of required devices.
URI: http://hdl.handle.net/10553/48815
ISBN: 9783642386817
ISSN: 0302-9743
DOI: 10.1007/978-3-642-38682-4_15
Fuente: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)[ISSN 0302-9743],v. 7903 LNCS, p. 124-134
Colección:Actas de congresos
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