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Identification of Myocardial Infarction by High Frequency Serial ECG Measurement




TekijätSandelin Jonas, Sirkiä Jukka-Pekka, Anzanpour Arman, Koivisto Tero

ToimittajaN/A

Konferenssin vakiintunut nimiComputing in Cardiology

Julkaisuvuosi2022

JournalComputing in Cardiology

Kokoomateoksen nimiComputing in Cardiology 2022

Sarjan nimiComputing in Cardiology

Vuosikerta49

ISSN2325-8861

eISSN2325-887X

DOIhttps://doi.org/10.22489/CinC.2022.185

Verkko-osoitehttps://cinc.org/archives/2022/pdf/CinC2022-185.pdf

Rinnakkaistallenteen osoitehttps://research.utu.fi/converis/portal/detail/Publication/178084055


Tiivistelmä

The purpose of this study is to attempt to identify acute myocardial infarction with high frequency serial electrocardiogram which both are ECG analyzing techniques. The idea is to combine these two techniques and see if changes between different ECGs from the same person can provide us with some information, whether it being in the high frequency or normal frequency range of ECG. A heart attack can occur at any time and therefore the possibility of using a wearable device was also researched.

To answer the questions, an existing database which contained multiple ECGs for each person with high sampling frequency was used. On top of this, a new serial ECG database was gathered using a wearable device designed by the University of Turku. Using multiple ECGs, features were extracted from the signals and then used in different machine learning methods in order to classify the subjects.

All of the methods seem to be relevant. High frequency ECG was found to be useful, while serial ECG provided us good results with both databases. The device was also found to be able to produce good quality ECG.


Ladattava julkaisu

This is an electronic reprint of the original article.
This reprint may differ from the original in pagination and typographic detail. Please cite the original version.





Last updated on 2024-26-11 at 22:45