A classification method using deep belief network for phonocardiogram signal classification

Moh Faturrahman, Ito Wasito, Fakhirah Dianah Ghaisani, Ratna Mufidah

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Phonocardiogram (PCG) signal is a graphical representation of the heart sounds that can be used to diagnose a heart disease. Diagnosing heart disease based on PCG signal is more effective. Because of its ability to capture all heart sound components including S1 and S2. Nevertheless, the interpretation of PCG signal is depend on the cardiologist's expertise. Therefore automated PCG signal classification is required in order to help the cardiologist diagnosing and monitoring heart disease. The classification of PCG signal is influenced by the segmentation and the feature extraction process. The segmentation process aims to detect the location of heart sound components including S1 and S2 in PCG signal. However it is difficult to find those component in a noisy PCG signal. The feature extraction process aims to extract relevant features that lie in segmented PCG signal. This process is required because the segmented PCG signal has high dimensionality and redundant information. This study proposes Shannon Energy Envelope for segmenting PCG signal and Deep Belief Network (DBN) for feature extraction method. The results show that the proposed method outperforms shallow models in existing datasets.

Original languageEnglish
Title of host publication2017 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages283-289
Number of pages7
ISBN (Electronic)9781538631720
DOIs
Publication statusPublished - 4 May 2018
Event9th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2017 - Jakarta, Indonesia
Duration: 28 Oct 201729 Oct 2017

Publication series

Name2017 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2017
Volume2018-January

Conference

Conference9th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2017
CountryIndonesia
CityJakarta
Period28/10/1729/10/17

Keywords

  • Deep Belief Network
  • Deep Learning
  • Feature Extraction
  • Heart Sound
  • Phonocardiogram Signal
  • Segmentation

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  • Cite this

    Faturrahman, M., Wasito, I., Ghaisani, F. D., & Mufidah, R. (2018). A classification method using deep belief network for phonocardiogram signal classification. In 2017 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2017 (pp. 283-289). [8355047] (2017 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2017; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICACSIS.2017.8355047