Abstract
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.
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Murillo Rendón, S., Hoyos, C.C., Travieso-Gonzales, C.M., Castellanos-Domínguez, G. (2013). Phonocardiography Signal Segmentation for Telemedicine Environments. In: Rojas, I., Joya, G., Cabestany, J. (eds) Advances in Computational Intelligence. IWANN 2013. Lecture Notes in Computer Science, vol 7903. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38682-4_15
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DOI: https://doi.org/10.1007/978-3-642-38682-4_15
Publisher Name: Springer, Berlin, Heidelberg
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