A new seismocardiography segmentation algorithm for diastolic timed vibrations

Area of Science:

• Biomedical Engineering
• Cardiovascular Physiology
• Signal Processing

Background:

• Diastolic timed vibrations (DTV) analysis is crucial for assessing cardiac function.
• Previous methods for DTV analysis often rely solely on electrocardiography (ECG), limiting accuracy.
• Integrating seismocardiography (SCG) offers a novel approach to enhance DTV detection.

Purpose of the Study:

• To develop and validate a novel algorithm for detecting diastolic timed vibrations (DTV).
• To improve the efficiency and accuracy of identifying key diastolic events using combined ECG and SCG signals.
• To establish a new reference point for DTV analysis using the ECG-R wave.

Main Methods:

• An algorithm was developed combining electrocardiography (ECG) and seismocardiography (SCG) signals.
• The algorithm utilizes the ECG-R wave as a primary reference point.
• It detects aortic valve closure (AC) and mitral valve closure (MC) points within the SCG signal.

Main Results:

• The proposed algorithm demonstrated high efficiency compared to previous ECG-only methods.
• Achieved a 95% prediction rate for detecting seismocardiography-mitral valve closure (SCG-MC).
• Achieved an 88% prediction rate for detecting seismocardiography-aortic valve closure (SCG-AC).

Conclusions:

• The combined ECG-SCG algorithm provides an efficient and accurate method for DTV analysis.
• This approach enhances the detection of critical diastolic events, particularly SCG-MC and SCG-AC.
• The algorithm holds promise for improved non-invasive cardiac function assessment.