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. 2022 Jun 9;22(12):4384.
doi: 10.3390/s22124384.

Mechanocardiography in the Detection of Acute ST Elevation Myocardial Infarction: The MECHANO-STEMI Study

Tero Koivisto  1 , Olli Lahdenoja  1 , Tero Hurnanen  1 , Tuija Vasankari  2 , Samuli Jaakkola  2 , Tuomas Kiviniemi  2 , K E Juhani Airaksinen  2
Affiliations

Mechanocardiography in the Detection of Acute ST Elevation Myocardial Infarction: The MECHANO-STEMI Study

Tero Koivisto et al. Sensors (Basel). .

Abstract

Novel means to minimize treatment delays in patients with ST elevation myocardial infarction (STEMI) are needed. Using an accelerometer and gyroscope on the chest yield mechanocardiographic (MCG) data. We investigated whether STEMI causes changes in MCG signals which could help to detect STEMI. The study group consisted of 41 STEMI patients and 49 control patients referred for elective coronary angiography and having normal left ventricular function and no valvular heart disease or arrhythmia. MCG signals were recorded on the upper sternum in supine position upon arrival to the catheterization laboratory. In this study, we used a dedicated wearable sensor equipped with 3-axis accelerometer, 3-axis gyroscope and 1-lead ECG in order to facilitate the detection of STEMI in a clinically meaningful way. A supervised machine learning approach was used. Stability of beat morphology, signal strength, maximum amplitude and its timing were calculated in six axes from each window with varying band-pass filters in 2-90 Hz range. In total, 613 features were investigated. Using logistic regression classifier and leave-one-person-out cross validation we obtained a sensitivity of 73.9%, specificity of 85.7% and AUC of 0.857 (SD = 0.005) using 150 best features. As a result, mechanical signals recorded on the upper chest wall with the accelerometers and gyroscopes differ significantly between STEMI patients and stable patients with normal left ventricular function. Future research will show whether MCG can be used for the early screening of STEMI.

Keywords: ECG; STEMI; accelerometer; acute myocardial infarction; electrocardiography; gyroscope; seismocardiography; telemonitoring.

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Conflict of interest statement

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Figures

Figure 2
Figure 2
Device used in data acquisition including 3-axis accelerometer, 3-axis gyroscope and single lead ECG for R-peak extraction. The optical sensor is not used in this study.
Figure 1
Figure 1
Total pre-hospital delay.
Figure 3
Figure 3
The overall measurement and analysis pipeline. First motion artefact removal selects only the longest connected artefact-free signal portion (in all axes). Then resulting signal was band-pass filtered. ECG R peaks were used to locate heartbeats for feature extraction and three main feature types were analyzed: stability of heartbeat morphology, signal strength and amplitude and time intervals (calculated within specific windows inside each heartbeat and axis). Finally, feature selection and binary classification with logistic regression classifier utilizing cross validation (LOOCV) were performed.
Figure 4
Figure 4
Box plot and receiver operating characteristic (ROC) curve with 150 best features using logistic regression classifier to separate STEMI and control patients.
Figure 5
Figure 5
Plots representing median of beat stability (band-pass filter 20–90 Hz) in AccX axis in a window around second heart sound (Upper panel), systole/diastole ratio in signal strength in GyroX axis (Middle panel), and median value of signal amplitude (band-pass filter 10–80 Hz) around third heart sound in AccZ axis (Lower panel) in STEMI and control patients.
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