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Diagnosing Cardiovascular Diseases with Machine Learning on Body Surface Potential Mapping Data

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Piotr BednarczukAuthors Info & Claims

SenSys '21: Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems

Pages 379 - 381

https://doi.org/10.1145/3485730.3492883

Published: 15 November 2021 Publication History

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Abstract

This research aimed to develop a high accuracy machine learning algorithm that can diagnose cardiovascular diseases from the stream of data from multiple body surface potential mapping devices equipped with 102 textile electrodes. The algorithm is based on the 1D convolutional neural network, trained on the comparable real-life data gathered from the FLUKE ECG simulator connected to the resistance-based human phantom. The developed neural network achieved an accuracy of 99.91% on the test data. Additionally, an additional algorithm was developed that can use the neural network to analyse the data streamed from the medical device and notice the medical staff about dangerous heart rhythms detected by the system.

References

[1]

Simon D. Carley, Michelle Jenkins, and Kevin Mackway Jones. 2005. Body surface mapping versus the standard 12 lead ECG in the detection of myocardial infarction amongst Emergency Department patients: a Bayesian approach. 64, 3 (March 2005), 309--314. https://doi.org/10.1016/j.resuscitation.2004.10.002

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Hubert Cochet, Rémi Dubois, Frédéric Sacher, Nicolas Derval, Maxime Sermesant, Mélèze Hocini, Michel Montaudon, Michel Haïssaguerre, François Laurent, and Pierre Jaïs. 2014. Cardiac Arrythmias: Multimodal Assessment Integrating Body Surface ECG Mapping into Cardiac Imaging. 271, 1 (April 2014), 239--247. https://doi.org/10.1148/radiol.13131331

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Coeytaux RR, Williams JW Jr, Chung E, and Gharacholou SM. 2010. ECG-Based Signal Analysis Technologies. (2010). https:// .ncbi.nlm.nih.gov/25473705/

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Iskra KDziadosz MOleszek MPiwko A(2024)Monitoring the health of patients using a nodal potential mapJournal of Modern Science10.13166/jms/19125657:3(701-712)Online publication date: 20-Aug-2024
https://doi.org/10.13166/jms/191256
Yang LAmin OShihada B(2024)Intelligent Wearable Systems: Opportunities and Challenges in Health and SportsACM Computing Surveys10.1145/364846956:7(1-42)Online publication date: 9-Apr-2024
https://dl.acm.org/doi/10.1145/3648469
Jia HKwon YMat DPham NQendro LVu TMascolo C(2024)UR2M: Uncertainty and Resource-Aware Event Detection on Microcontrollers2024 IEEE International Conference on Pervasive Computing and Communications (PerCom)10.1109/PerCom59722.2024.10494467(1-10)Online publication date: 11-Mar-2024
https://doi.org/10.1109/PerCom59722.2024.10494467

Index Terms

Diagnosing Cardiovascular Diseases with Machine Learning on Body Surface Potential Mapping Data
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Hardware
  1. Communication hardware, interfaces and storage
    1. Signal processing systems
      1. Digital signal processing

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Information

Published In

SenSys '21: Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems

November 2021

686 pages

ISBN:9781450390972

DOI:10.1145/3485730

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 November 2021

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Poster
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Refereed limited

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SenSys '21

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SenSys '21: The 19th ACM Conference on Embedded Networked Sensor Systems

November 15 - 17, 2021

Coimbra, Portugal

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SenSys '21 Paper Acceptance Rate 25 of 139 submissions, 18%;

Overall Acceptance Rate 174 of 867 submissions, 20%

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SenSys '24

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The 22nd ACM Conference on Embedded Networked Sensor Systems

November 4 - 7, 2024

Hangzhou , China

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Cited By

View all

Iskra KDziadosz MOleszek MPiwko A(2024)Monitoring the health of patients using a nodal potential mapJournal of Modern Science10.13166/jms/19125657:3(701-712)Online publication date: 20-Aug-2024
https://doi.org/10.13166/jms/191256
Yang LAmin OShihada B(2024)Intelligent Wearable Systems: Opportunities and Challenges in Health and SportsACM Computing Surveys10.1145/364846956:7(1-42)Online publication date: 9-Apr-2024
https://dl.acm.org/doi/10.1145/3648469
Jia HKwon YMat DPham NQendro LVu TMascolo C(2024)UR2M: Uncertainty and Resource-Aware Event Detection on Microcontrollers2024 IEEE International Conference on Pervasive Computing and Communications (PerCom)10.1109/PerCom59722.2024.10494467(1-10)Online publication date: 11-Mar-2024
https://doi.org/10.1109/PerCom59722.2024.10494467
Stracina TRonzhina MRedina RNovakova M(2022)Golden Standard or Obsolete Method? Review of ECG Applications in Clinical and Experimental ContextFrontiers in Physiology10.3389/fphys.2022.86703313Online publication date: 25-Apr-2022
https://doi.org/10.3389/fphys.2022.867033

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