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Interpretable XGBoost based classification of 12-lead ECGs applying information theory measures from neuroscience.

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Title: Interpretable XGBoost based classification of 12-lead ECGs applying information theory measures from neuroscience.
Authors: Rajpal, H
Sas, M
Lockwood, C
Joakim, R
Peters, NS
Falkenberg, M
Item Type: Conference Paper
Abstract: Automated ECG classification is a standard feature in many commercial 12-Lead ECG machines. As part of the Physionet/CinC Challenge 2020, our team, "Mad-hardmax", developed an XGBoost based classification method for the analysis of 12-Lead ECGs acquired from four different countries. Our aim is to develop an interpretable classifier that outputs diagnoses which can be traced to specific ECG features, while also testing the potential of information theoretic features for ECG diagnosis. These measures capture high-level interdependencies across ECG leads which are effective for discriminating conditions with multiple complex morphologies. On unseen test data, our algorithm achieved a challenge score of 0.155 relative to a winning score of 0.533, putting our submission in 24th position from 41 successful entries.
Issue Date: 10-Feb-2021
Date of Acceptance: 1-Feb-2021
URI: http://hdl.handle.net/10044/1/88924
DOI: 10.22489/CinC.2020.185
ISSN: 2325-8861
Publisher: IEEE
Start Page: 1
End Page: 4
Journal / Book Title: Comput Cardiol (2010)
Volume: 47
Copyright Statement: © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Sponsor/Funder: British Heart Foundation
Funder's Grant Number: RG/16/3/32175
Conference Name: 2020 Computing in Cardiology
Publication Status: Published
Start Date: 2020-09-13
Finish Date: 2020-09-16
Conference Place: Rimini, Italy
Online Publication Date: 2021-02-10
Appears in Collections:Physics
National Heart and Lung Institute
Faculty of Natural Sciences