Software System Development to Diagnose Heart Diseases

  • Renan Yuji Koga Ferreira UEL
  • Guilherme Camargo Fabricio de Melloy UEL
  • Fabio Sakurayz UEL
  • Wesley Attrot UEL

Resumo


Many deaths are caused from heart diseases and several of them could be prevented with early detection. Many people do not have conditions to seek for a doctor or sometimes there are not enough physicians to attend them. In order to detect heart diseases we are developing an electrocardiogram feature extraction algorithm using wavelet transforms prioritizing a low computational cost. This algorithm will be integrated in an embedded system that is under development. This system is going to be accessible, portable and have low cost, because we intend to assist people, mostly those who live in precarious regions, that do not have a physician to attend them. To execute tests on our algorithm we will use the ECG records from MITBIH database and after that we will classify the heartbeats in order to detect anomalies on them.

Palavras-chave: ECG, heart diseases, feature extraction, wavelet transform.

Referências

World Health Organization, “Cardiovascular diseases (cvds),” https://www.who.int/en/news-room/fact-sheets/ detail/cardiovascular-diseases-(cvds), May 2017.

A. Alberdi, A. Aztiria, A. Basarab, “Towards an automatic early recognition system for office environments based on multimodas measurements: A review,” Journal of Biomedical Informatics, 2016.

Selcan Kaplan Berkaya, Alper Kursat Uysal, Efnan Sora Gunal, Semih Ergin, Serkan Gunal, Mehmet Bilginer Gulmezoglu, “A survey on ecg analysis,” Biomedical Signal Processing and Control, 2018.

C. C. F. de Medicina, “Demografia Médica: Publicação já está disponível na internet,” https://portal.cfm.org.br/index.php?option=com_content&view=article&id=27509:2018-03-21-19-29-36&catid=3

P. S. Addison, The Illustrated Wavelet Transform Handbook: Introductory Theory and Applications in Science, Engineering, Medicine and Finance. CRC Press, 2016.

Physionet, “Mit-bih arrhythmia database directory,” https://physionet.org/physiobank/database/mitdb/, May 1997.

Physionet, “Mit-bih arrhythmia database directory,” https://physionet.org/physiobank/database/html/mitdbdir/intro.htm, May 1997.

George B. Moody, Roger G. Mark, “The impact of the mitbih arrhythmia database,” IEEE Engineering in Medicine and Biology Magazine, 2001.

Kuo-Kuang Jen, Yean-Ren Hwang, “Ecg feature extraction and classification using cepstrum and neural networks,” Journal of Medical and Biological Engineering, 2006.

Qibin Zhao, Liqing Zhan, “Ecg feature extraction and classification using wavelet transform and support vector machines,” International Conference on Neural Networks and Brain, 2005.

Yun-Chi Yeh, Wen-June Wang, Che Wun Chiou, “Cardiac arrhythmia diagnosis method using linear discriminant analysis on ecg signals,” Measurement, 2009.

Yun-Chi Yeh, Wen-June Wang, Che Wun Chiou, “Feature selection algorithm for ecg signals using range-overlaps method,” Expert Systems with Applications, 2010.

Jiapu Pan, Willis J. Tompkins, “A real-time qrs detection algorithm,” IEEE Transactions on Biomedical Engineering, 1985.

D. L. Fugal, Conceptual Wavelets in Digital Signal Processing. Space & Signals Technologies LLC, 2009.

S. Mallat, A Wavelet Tour of Signal Processing (3. ed.). Academic Press, 1999.

T. Q. Company, “Qt - cross-platform software development for embedded & desktop,” https://www.qt.io/.

Gari D. Clifford, Francisco Azuaje, Patrick E. McSharry, Advanced Methods and Tools for ECG Data Analysis. Artech House, 2006.

Arduino, “Arduino - home,” https://www.arduino.cc/.

R. P. Foundation, “Raspberry pi,” https://www.raspberrypi.org/.
Publicado
27/11/2019
Como Citar

Selecione um Formato
FERREIRA, Renan Yuji Koga; DE MELLOY, Guilherme Camargo Fabricio; SAKURAYZ, Fabio; ATTROT, Wesley. Software System Development to Diagnose Heart Diseases. In: CONGRESSO LATINO-AMERICANO DE SOFTWARE LIVRE E TECNOLOGIAS ABERTAS (LATINOWARE), 16. , 2019, Foz do Iguaçu. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2019 . p. 156-159. DOI: https://doi.org/10.5753/latinoware.2019.10353.