research-article

Identification of Normal and Abnormal Heart Sounds by Prominent Peak Analysis

Authors:

M.G.M. Milani,

Pg Emeroylariffion Abas,

Liyanage C. De SilvaAuthors Info & Claims

SSPS '19: Proceedings of the 2019 International Symposium on Signal Processing Systems

Pages 31 - 35

https://doi.org/10.1145/3364908.3364924

Published: 20 September 2019 Publication History

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Abstract

This paper presents an automatic method of segmenting normal and abnormal Phonocardiography (PCG) signals by analyzing prominent peak distances, amplitudes, area, and cardiac cycle durations. Principal Component Analysis (PCA) is used to reduce the dimensions of the calculated prominent peak ratios. Subsequently, Artificial Neural Networks (ANNs) are used to identify the boundary between normal and abnormal heart sounds. PhysioNet/CinC normal and abnormal data recordings are used in this proposed method to segment S1 and S2. The segmentation results are used to differentiate normal and abnormal heart sound signals. The identification of normal and abnormal heart sounds achieved 90% accuracy. The results and observations of this paper are verified with some clinical examination observations.

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Netto AAbraham LPhilip S(2024)HBNETTechnology and Health Care10.3233/THC-23129032:3(1925-1945)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.3233/THC-231290
Chen JGuo ZXu XJeon GCamacho D(2024)Artificial intelligence for heart sound classification: A reviewExpert Systems10.1111/exsy.1353541:4Online publication date: 8-Jan-2024
https://doi.org/10.1111/exsy.13535
Han DBashar SLázaro JMohagheghian FPeitzsch ANishita NDing EDickson EDiMezza DScott JWhitcomb CFitzgibbons TMcManus DChon K(2022)A Real-Time PPG Peak Detection Method for Accurate Determination of Heart Rate during Sinus Rhythm and Cardiac ArrhythmiaBiosensors10.3390/bios1202008212:2(82)Online publication date: 29-Jan-2022
https://doi.org/10.3390/bios12020082
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Index Terms

Identification of Normal and Abnormal Heart Sounds by Prominent Peak Analysis
1. Computer systems organization
  1. Architectures
    1. Other architectures
      1. Neural networks

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SSPS '19: Proceedings of the 2019 International Symposium on Signal Processing Systems

September 2019

188 pages

ISBN:9781450362412

DOI:10.1145/3364908

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Beijing University of Posts and Telecommunications

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Association for Computing Machinery

New York, NY, United States

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Published: 20 September 2019

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SSPS 2019: 2019 International Symposium on Signal Processing Systems

September 20 - 22, 2019

Beijing, China

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Netto AAbraham LPhilip S(2024)HBNETTechnology and Health Care10.3233/THC-23129032:3(1925-1945)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.3233/THC-231290
Chen JGuo ZXu XJeon GCamacho D(2024)Artificial intelligence for heart sound classification: A reviewExpert Systems10.1111/exsy.1353541:4Online publication date: 8-Jan-2024
https://doi.org/10.1111/exsy.13535
Han DBashar SLázaro JMohagheghian FPeitzsch ANishita NDing EDickson EDiMezza DScott JWhitcomb CFitzgibbons TMcManus DChon K(2022)A Real-Time PPG Peak Detection Method for Accurate Determination of Heart Rate during Sinus Rhythm and Cardiac ArrhythmiaBiosensors10.3390/bios1202008212:2(82)Online publication date: 29-Jan-2022
https://doi.org/10.3390/bios12020082
Ramashini MAbas PDe Silva L(2021)A Novel Approach of Audio Based Feature Optimisation for Bird ClassificationPertanika Journal of Science and Technology10.47836/pjst.29.4.0829:4Online publication date: 29-Oct-2021
https://doi.org/10.47836/pjst.29.4.08
Milani MRamashini MMurugiah KChamal L(2021)Exploiting optimum acoustic features in COVID-19 individual's breathing sounds2021 International Research Conference on Smart Computing and Systems Engineering (SCSE)10.1109/SCSE53661.2021.9568369(71-76)Online publication date: 16-Sep-2021
https://doi.org/10.1109/SCSE53661.2021.9568369
Manisha Milani MRamashini MKrishani M(2021)Speech signal analysis of COVID-19 patients via machine learning approach2021 International Conference on Decision Aid Sciences and Application (DASA)10.1109/DASA53625.2021.9682294(314-319)Online publication date: 7-Dec-2021
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Milani MMurugaiya RMurugiah KSenaratne G(2021)Sleep Pattern Analysis from PolySomnoGraphic Signals using a Supervised Machine Learning ApproachSN Computer Science10.1007/s42979-021-00606-82:3Online publication date: 22-Apr-2021
https://dl.acm.org/doi/10.1007/s42979-021-00606-8

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A Biomedical System Based on Artificial Neural Network and Principal Component Analysis for Diagnosis of the Heart Valve Diseases

Support Vectors Machine-based identification of heart valve diseases using heart sounds

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