Vertaisarvioitu artikkeli konferenssijulkaisussa (A4)

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

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

Konferenssin vakiintunut nimi: Computing in Cardiology

Julkaisuvuosi: 2018

Journal: Computing in Cardiology

Kirjan nimi *: CinC 2018: Proceedings

Sarjan nimi: Computing in Cardiology

Volyymi: 45

Sivujen määrä: 4

ISSN: 2325-8861


Rinnakkaistallenteen osoite:


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.

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Last updated on 2022-07-04 at 17:07