Analysis of energy and major components in chromatographic signals for the diagnosis of prostate cancer-Reference-Cited by-同舟云学术

Analysis of energy and major components in chromatographic signals for the diagnosis of prostate cancer

Published:2019-01-01 Issue:1 Volume:24 Page:76-85
ISSN:2422-5053
Container-title:Respuestas
language:
Short-container-title:Respuestas

Author:

Soto Vergel Ángelo Joseph^ORCID,Mendoza Luis Enrique^ORCID,Medina Delgado Byron^ORCID

Abstract

The prostate exam is an early detection tool to prevent prostate cancer and the main diagnostic tools for obtaining signs are generally invasive. This article tries chromatographic signals from the urine of prostate cancer patients and control patients as a non-invasive examination proposal. For this purpose, methodologically, urine samples are taken, digitized in chromatograms, treated with mathematical techniques and classified. The mathematical techniques are time normalization, dead time elimination, baseline correction, noise elimination, and peak alignment. Classification techniques analyze energy, in the domain of time and frequency, and the main components in sedimentation graphs and scores. As a result, the chromatographic signal is characterized and identifies the characteristic curve that represents the signal of prostate cancer patients and control patients. The data structure shows a cluster distribution of 88.88% of the vectors for the control patients. In the case of prostate cancer patients, the distribution of data is in clusters around the area defined by control patients. This characterization demarcates signal classification regions to diagnose possible prostate cancer patients, validating the relationship between the chromatographic signal and cancer.

Publisher

Universidad Francisco de Paula Santander

Reference26 articles.

1. Á. Fajardo-Zapata and G. Jaimes-Monroy,“Conocimiento, percepción y disposición sobre el examen de próstata en hombres mayores de 40 años,” Investig. Orig., vol. 64, no. 2, pp. 223–228,2016.

2. D. Glujovsky, A. Bardach, S. García-Martí, D.Comandé, and A. Ciapponi, “PRM2 EROS: A New Software For Early Stage Of Systematic REVIEWS,”Value Heal., vol. 14, no. 7, p. A564, Nov. 2011.

3. I. Rivera et al., “Diseño de dispositivos para el diagnóstico de sarcopenia en miembro superior,”Memorias del Congr. Nac. Ing. Biomédica, vol. 2,no. 1, pp. 174–177, 2017.

4. L. Garrido-Martínez and R. I. González-Fernández, “Revista cubana de informática médica,”Rev. Cuba. Informática Médica, vol. 15, no. 2, pp.153–164, 2015.

5. E. Dugarte-Dugarte et al., “Algoritmo de bajo costo de procesamiento para la detección de potenciales tardíos ventriculares (PTV),” CLIC Conoc. Libr. y Licenciamiento, vol. 8, no. 15, pp.73–93, 2017.

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Autoregressive modelling of chromatographic signals from urine samples for prostate cancer diagnosis;Journal of Physics: Conference Series;2021-05-01