Mechanocardiography in the Detection of Acute ST Elevation Myocardial Infarction: The MECHANO-STEMI Study
Tero Koivisto1, Olli Lahdenoja1, Tero Hurnanen1
1Department of Computing, University of Turku, Vesilinnantie 5, 20500 Turku, Finland.
Insights
Mechanocardiography (MCG) signals from chest sensors can detect ST elevation myocardial infarction (STEMI). This novel approach using accelerometers and gyroscopes shows significant differences between STEMI patients and controls, aiding early detection.
Area of Science:
- Biomedical Engineering
- Cardiology
- Signal Processing
Background:
- Minimizing treatment delays for ST elevation myocardial infarction (STEMI) is critical.
- Mechanocardiography (MCG), derived from chest-worn accelerometers and gyroscopes, offers a potential non-invasive method for cardiac assessment.
- Current diagnostic methods for STEMI may involve delays, necessitating innovative screening tools.
Purpose of the Study:
- To investigate if STEMI alters MCG signals detectable by wearable sensors.
- To evaluate the feasibility of using MCG data for early STEMI detection.
- To differentiate STEMI patients from control subjects using machine learning analysis of MCG signals.
Main Methods:
- A wearable sensor (3-axis accelerometer, 3-axis gyroscope, 1-lead ECG) recorded MCG signals from the upper sternum.
- Study included 41 STEMI patients and 49 controls with normal cardiac function.
- A supervised machine learning approach analyzed signal stability, strength, amplitude, and timing across six axes and various frequency bands (2-90 Hz).
Main Results:
- Machine learning analysis identified significant differences in MCG signals between STEMI patients and controls.
- A logistic regression model using 150 features achieved a sensitivity of 73.9%, specificity of 85.7%, and an AUC of 0.857.
- The findings indicate distinct mechanical signal patterns associated with STEMI.
Conclusions:
- Chest wall mechanical signals recorded via accelerometers and gyroscopes differ significantly in STEMI patients compared to controls.
- MCG shows promise as a potential tool for the early screening and detection of STEMI.
- Further research is warranted to validate MCG for widespread clinical application in acute coronary syndrome management.
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.
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