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ECG sonification: a new approach for diagnosis of cardiac pathologies

Authors:

Shaid Hasan,

Iqbal Kabir,

Pratic A MuntakimAuthors Info & Claims

NSysS '19: Proceedings of the 6th International Conference on Networking, Systems and Security

Pages 99 - 103

https://doi.org/10.1145/3362966.3362968

Published: 17 December 2019 Publication History

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Abstract

Easy detection of electrocardiogram (ECG) data is highly required in modern clinical system in case of different diseases. The present existing technique to represent and analysis data is visualization. Another alternative way of data representation known as sonification can make a revolutionary development in many clinical applications. In this work, we have applied sonification technique on ECG dataset and demonstrated a user study on 20 undergraduate students for diagnosis of cardiac pathologies. We have also made a user study comparison between sonification and visualization technique. Our study can be the foundation in further sonification and medical researches.

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Cited By

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Krasteva VIliev ITabakov S(2024)Application of Convolutional Neural Network for Decoding of 12-Lead Electrocardiogram from a Frequency-Modulated Audio Stream (Sonified ECG)Sensors10.3390/s2406188324:6(1883)Online publication date: 15-Mar-2024
https://doi.org/10.3390/s24061883

Index Terms

ECG sonification: a new approach for diagnosis of cardiac pathologies
1. Applied computing
  1. Life and medical sciences
    1. Health care information systems

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Information

Published In

NSysS '19: Proceedings of the 6th International Conference on Networking, Systems and Security

December 2019

146 pages

ISBN:9781450376990

DOI:10.1145/3362966

Program Chairs:
A. B. M. Alim Al Islam
Bangladesh University of Engineering and Technology, Bangladesh
,
Manzur Murshed
Federation University, Australia
,
Mahmuda Naznin
Bangladesh University of Engineering and Technology, Bangladesh

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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 December 2019

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6th NSysS 2019

6th NSysS 2019: 6th International Conference on Networking, Systems and Security

December 17 - 19, 2019

Dhaka, Bangladesh

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NSysS '19 Paper Acceptance Rate 12 of 44 submissions, 27%;

Overall Acceptance Rate 12 of 44 submissions, 27%

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Cited By

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Krasteva VIliev ITabakov S(2024)Application of Convolutional Neural Network for Decoding of 12-Lead Electrocardiogram from a Frequency-Modulated Audio Stream (Sonified ECG)Sensors10.3390/s2406188324:6(1883)Online publication date: 15-Mar-2024
https://doi.org/10.3390/s24061883

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Recommendations

Development of Lead System for ECG-Derived Respiration Aimed at Detection of Obstructive Sleep Apnea Syndrome

Detection of cardiac abnormalities in ECG signal using time-based signal processing algorithm

An efficient method for ECG beat classification and correction of ectopic beats