Automated magnetocardiogram classifications with Self-Organizing Maps(SOMs)

Thanakotn Naenna; Mark J. Embrechts; Bolek Szymanski; Karsten Sternickel

Automated magnetocardiogram classifications with Self-Organizing Maps(SOMs)

Thanakotn Naenna
, Mark J. Embrechts
, Bolek Szymanski
, Karsten Sternickel

Department of Industrial Engineering

Rensselaer Polytechnic Institute
Cardiomag Imaging Inc.

Research output: Contribution to conference › Paper › peer-review

Abstract

The main goal of this paper is to apply the Self-Organizing Maps (SOM), a novel learning and visualization technique, for abnormal and normal magnetocardiography (MCG) classification. MCG is the measurement of magnetic fields emitted by the electrophysiological activity of the human heart The interpretation of MCG recordings remains a challenge since there are no databases available from which precise rules could be educed. Hence, there is a need to automate interpretation of MCG measurements to minimize human input for the analysis. In this particular case SOMs are applied in detecting ischemia, which is a loss of conductivity because of damaged cell tissue in the heart and the main cause of heart attacks.

Original language	English
Pages	B458-B460
Publication status	Published - 2004
Event	IEEE TENCON 2004 - 2004 IEEE Region 10 Conference: Analog and Digital Techniques in Electrical Engineering - Chiang Mai, Thailand Duration: 21 Nov 2004 → 24 Nov 2004

Conference

Conference	IEEE TENCON 2004 - 2004 IEEE Region 10 Conference: Analog and Digital Techniques in Electrical Engineering
Country/Territory	Thailand
City	Chiang Mai
Period	21/11/04 → 24/11/04

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Cite this

@conference{50dbb84b1d8a4b94b0c3ba08ed1414cc,

title = "Automated magnetocardiogram classifications with Self-Organizing Maps(SOMs)",

abstract = "The main goal of this paper is to apply the Self-Organizing Maps (SOM), a novel learning and visualization technique, for abnormal and normal magnetocardiography (MCG) classification. MCG is the measurement of magnetic fields emitted by the electrophysiological activity of the human heart The interpretation of MCG recordings remains a challenge since there are no databases available from which precise rules could be educed. Hence, there is a need to automate interpretation of MCG measurements to minimize human input for the analysis. In this particular case SOMs are applied in detecting ischemia, which is a loss of conductivity because of damaged cell tissue in the heart and the main cause of heart attacks.",

author = "Thanakotn Naenna and Embrechts, \{Mark J.\} and Bolek Szymanski and Karsten Sternickel",

year = "2004",

language = "English",

pages = "B458--B460",

note = "IEEE TENCON 2004 - 2004 IEEE Region 10 Conference: Analog and Digital Techniques in Electrical Engineering ; Conference date: 21-11-2004 Through 24-11-2004",

}

Automated magnetocardiogram classifications with Self-Organizing Maps(SOMs). / Naenna, Thanakotn; Embrechts, Mark J.; Szymanski, Bolek et al.
2004. B458-B460 Paper presented at IEEE TENCON 2004 - 2004 IEEE Region 10 Conference: Analog and Digital Techniques in Electrical Engineering, Chiang Mai, Thailand.

Research output: Contribution to conference › Paper › peer-review

TY - CONF

T1 - Automated magnetocardiogram classifications with Self-Organizing Maps(SOMs)

AU - Naenna, Thanakotn

AU - Embrechts, Mark J.

AU - Szymanski, Bolek

AU - Sternickel, Karsten

PY - 2004

Y1 - 2004

N2 - The main goal of this paper is to apply the Self-Organizing Maps (SOM), a novel learning and visualization technique, for abnormal and normal magnetocardiography (MCG) classification. MCG is the measurement of magnetic fields emitted by the electrophysiological activity of the human heart The interpretation of MCG recordings remains a challenge since there are no databases available from which precise rules could be educed. Hence, there is a need to automate interpretation of MCG measurements to minimize human input for the analysis. In this particular case SOMs are applied in detecting ischemia, which is a loss of conductivity because of damaged cell tissue in the heart and the main cause of heart attacks.

AB - The main goal of this paper is to apply the Self-Organizing Maps (SOM), a novel learning and visualization technique, for abnormal and normal magnetocardiography (MCG) classification. MCG is the measurement of magnetic fields emitted by the electrophysiological activity of the human heart The interpretation of MCG recordings remains a challenge since there are no databases available from which precise rules could be educed. Hence, there is a need to automate interpretation of MCG measurements to minimize human input for the analysis. In this particular case SOMs are applied in detecting ischemia, which is a loss of conductivity because of damaged cell tissue in the heart and the main cause of heart attacks.

UR - https://www.scopus.com/pages/publications/27944508328

M3 - Paper

AN - SCOPUS:27944508328

SP - B458-B460

T2 - IEEE TENCON 2004 - 2004 IEEE Region 10 Conference: Analog and Digital Techniques in Electrical Engineering

Y2 - 21 November 2004 through 24 November 2004

ER -

Automated magnetocardiogram classifications with Self-Organizing Maps(SOMs)

Abstract

Conference

UN SDGs

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