Postural and longitudinal variability in seismocardiographic signals

Area of Science:

• Cardiovascular monitoring
• Biomedical signal processing
• Non-invasive diagnostics

Background:

• Seismocardiography (SCG) measures low-frequency chest vibrations for cardiovascular assessment.
• Intra-subject variability in SCG can limit its use for long-term patient monitoring.
• Quantifying SCG variability sources is crucial for improving its clinical utility.

Purpose of the Study:

• To quantify postural and longitudinal SCG variability in healthy resting subjects.
• To assess the impact of different body postures on SCG features.
• To evaluate SCG signal consistency over extended periods.

Main Methods:

• Longitudinal acquisition of SCG and ECG signals from 19 healthy subjects over five months.
• Segmentation of SCG cycles synchronized with ECG R-peaks.
• Application of unsupervised machine learning to cluster SCG signals and reduce respiratory artifacts.
• Analysis of SCG waveform morphology, spectral energy ratios, and cardiac timing intervals across postures.

Main Results:

• Significant morphological SCG variability was observed between supine and sitting postures, but not between supine and 45° head-up.
• Intra-cluster SCG variability was similar for 45° and sitting postures, while inter-cluster variability differed significantly.
• Cardiac timing intervals and heart rate showed significant variations with postural changes.
• SCG features remained comparable across longitudinal recording sessions, indicating minimal long-term variability.

Conclusions:

• SCG features exhibit significant postural dependency, necessitating posture specification for longitudinal comparisons.
• Consistent SCG feature values over time suggest that observed longitudinal changes likely reflect true cardiac alterations.
• SCG holds potential for non-invasive cardiovascular monitoring, provided postural effects are accounted for.