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

In this paper, we attempt to classify the pre- and post-operation 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.