Refereed article in conference proceedings (A4)

Machine Learning Based Classification of Myocardial Infarction Conditions Using Smartphone-derived Seismo- and Gyrocardiography

List of Authors: Saeed Mehrang, Mojtaba Jafari Tadi, Matti Kaisti, Olli Lahdenoja, Tuija Vasankari, Tuomas Kiviniemi, Juhani Airaksinen, Tero Koivisto, Mikko Pänkäälä

Conference name: Computing in Cardiology

Publication year: 2018

Journal: Computing in Cardiology

Book title *: CinC 2018: Proceedings

Title of series: Computing in Cardiology

Volume number: 45

Number of pages: 4

ISSN: 2325-8861


Self-archived copy’s web address:


In this paper, we attempt to classify the pre- and postoperation cardiac conditions of ST-elevation myocardial
infarction (STEMI) utilizing seismocardiography (SCG)
and gyrocardiography (GCG) signals recorded solely by a
smartphone. SCG and GCG signals were recorded from 20
MI patients who were admitted to Emergency Department
of Turku Hospital. Two measurements were recorded from
each subject, one before they proceeded to percutaneous
coronary intervention (pre-operation) and one afterwards
(post-operation) with an average time interval of 2 days.
Noise and artefact removal were applied to the signals and
subsequently 25 features were extracted. Two classification algorithms, random forest (RF) and support vector
machines (SVM), were deployed to discriminate the two
cardiac conditions. Accuracy rates of 74% and 78% were
obtained for RF and SVM, respectively. The results indicate that smartphone SCG-GCG based ischaemia analysis
has clinical implications that warrants further investigations.

Downloadable publication

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 2022-07-04 at 17:07