Automatic Classification Normal ECGs Based on Normal PathECG and WaveECG Features

Elzbieta Pociask; Krzysztof P. Malinowski; Mhd Jafar Mortada; Klaudia K. Proniewska; Peter M. Van Dam

doi:10.22489/CinC.2023.216

Automatic Classification Normal ECGs Based on Normal PathECG and WaveECG Features

Elzbieta Pociask^*, Krzysztof P. Malinowski, Mhd Jafar Mortada, Klaudia K. Proniewska, Peter M. Van Dam

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

Abstract

Classification of the ECG waveform to normal or abnormal is important to the non-experienced ECG-reader. We propose an algorithm to use solely the waveform of a single ECG beat to classify the ECG as normal or abnormal. In this study we used a subset of the normal classified ECGs from the PTB-XL database to create a normal distribution of the ECG waveform (WaveECG) and its PathECG positions. The aim of this study was to use these distributions to classify all human validated ECGs from the PTB-XL database as either normal or abnormal. Our initial results show an accuracy of 87% to determine whether an ECG is normal or abnormal, irrespective of the gender group used. Using solely the ECG waveform can detect the vast majority of abnormal ECGs, including conduction disorders, ischemia, and arrhythmias.

Original language	English
Title of host publication	Computing in Cardiology, CinC 2023
Publisher	IEEE Computer Society Press
Pages	1-4
Number of pages	4
ISBN (Electronic)	9798350382525
DOIs	https://doi.org/10.22489/CinC.2023.216
Publication status	Published - 2023
Externally published	Yes
Event	50th Computing in Cardiology, CinC 2023 - Atlanta, United States Duration: 1 Oct 2023 → 4 Oct 2023

Publication series

Name	Computing in Cardiology
Volume	50
ISSN (Print)	2325-8861
ISSN (Electronic)	2325-887X

Conference

Conference	50th Computing in Cardiology, CinC 2023
Country/Territory	United States
City	Atlanta
Period	1/10/23 → 4/10/23

Access to Document

10.22489/CinC.2023.216

Cite this

@inproceedings{1ea70b32096149c78859a0b9beb55a10,

title = "Automatic Classification Normal ECGs Based on Normal PathECG and WaveECG Features",

abstract = "Classification of the ECG waveform to normal or abnormal is important to the non-experienced ECG-reader. We propose an algorithm to use solely the waveform of a single ECG beat to classify the ECG as normal or abnormal. In this study we used a subset of the normal classified ECGs from the PTB-XL database to create a normal distribution of the ECG waveform (WaveECG) and its PathECG positions. The aim of this study was to use these distributions to classify all human validated ECGs from the PTB-XL database as either normal or abnormal. Our initial results show an accuracy of 87% to determine whether an ECG is normal or abnormal, irrespective of the gender group used. Using solely the ECG waveform can detect the vast majority of abnormal ECGs, including conduction disorders, ischemia, and arrhythmias.",

author = "Elzbieta Pociask and Malinowski, {Krzysztof P.} and Mortada, {Mhd Jafar} and Proniewska, {Klaudia K.} and {Van Dam}, {Peter M.}",

note = "Publisher Copyright: {\textcopyright} 2023 CinC.; 50th Computing in Cardiology, CinC 2023 ; Conference date: 01-10-2023 Through 04-10-2023",

year = "2023",

doi = "10.22489/CinC.2023.216",

language = "English",

series = "Computing in Cardiology",

publisher = "IEEE Computer Society Press",

pages = "1--4",

booktitle = "Computing in Cardiology, CinC 2023",

}

Pociask, E, Malinowski, KP, Mortada, MJ, Proniewska, KK & Van Dam, PM 2023, Automatic Classification Normal ECGs Based on Normal PathECG and WaveECG Features. in Computing in Cardiology, CinC 2023. Computing in Cardiology, vol. 50, IEEE Computer Society Press, pp. 1-4, 50th Computing in Cardiology, CinC 2023, Atlanta, United States, 1/10/23. https://doi.org/10.22489/CinC.2023.216

Automatic Classification Normal ECGs Based on Normal PathECG and WaveECG Features. / Pociask, Elzbieta; Malinowski, Krzysztof P.; Mortada, Mhd Jafar et al.
Computing in Cardiology, CinC 2023. IEEE Computer Society Press, 2023. p. 1-4 (Computing in Cardiology; Vol. 50).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Automatic Classification Normal ECGs Based on Normal PathECG and WaveECG Features

AU - Pociask, Elzbieta

AU - Malinowski, Krzysztof P.

AU - Mortada, Mhd Jafar

AU - Proniewska, Klaudia K.

AU - Van Dam, Peter M.

PY - 2023

Y1 - 2023

N2 - Classification of the ECG waveform to normal or abnormal is important to the non-experienced ECG-reader. We propose an algorithm to use solely the waveform of a single ECG beat to classify the ECG as normal or abnormal. In this study we used a subset of the normal classified ECGs from the PTB-XL database to create a normal distribution of the ECG waveform (WaveECG) and its PathECG positions. The aim of this study was to use these distributions to classify all human validated ECGs from the PTB-XL database as either normal or abnormal. Our initial results show an accuracy of 87% to determine whether an ECG is normal or abnormal, irrespective of the gender group used. Using solely the ECG waveform can detect the vast majority of abnormal ECGs, including conduction disorders, ischemia, and arrhythmias.

AB - Classification of the ECG waveform to normal or abnormal is important to the non-experienced ECG-reader. We propose an algorithm to use solely the waveform of a single ECG beat to classify the ECG as normal or abnormal. In this study we used a subset of the normal classified ECGs from the PTB-XL database to create a normal distribution of the ECG waveform (WaveECG) and its PathECG positions. The aim of this study was to use these distributions to classify all human validated ECGs from the PTB-XL database as either normal or abnormal. Our initial results show an accuracy of 87% to determine whether an ECG is normal or abnormal, irrespective of the gender group used. Using solely the ECG waveform can detect the vast majority of abnormal ECGs, including conduction disorders, ischemia, and arrhythmias.

UR - http://www.scopus.com/inward/record.url?scp=85182324403&partnerID=8YFLogxK

U2 - 10.22489/CinC.2023.216

DO - 10.22489/CinC.2023.216

M3 - Conference contribution

AN - SCOPUS:85182324403

T3 - Computing in Cardiology

SP - 1

EP - 4

BT - Computing in Cardiology, CinC 2023

PB - IEEE Computer Society Press

T2 - 50th Computing in Cardiology, CinC 2023

Y2 - 1 October 2023 through 4 October 2023

ER -

Automatic Classification Normal ECGs Based on Normal PathECG and WaveECG Features

Abstract

Publication series

Conference

Access to Document

Other files and links

Fingerprint

Cite this