DR.BEAT: Rule-Based Algorithm for SCG Analysis Without ECG Reference.

Area of Science:

• Biomedical Engineering
• Cardiovascular Technology
• Wearable Sensors

Background:

• Cardiac health monitoring is crucial for early disease detection.
• Wearable sensors offer a non-invasive approach to continuous physiological data collection.
• Ballistocardiography (BCG) and seismocardiography (SCG) provide insights into cardiac function.

Purpose of the Study:

• To develop an accelerometer-based wearable sensor system for measuring BCG signals.
• To create a rule-based algorithm for heartbeat detection and health parameter derivation.
• To evaluate the performance of the developed system and algorithm for cardiac health monitoring.

Main Methods:

• Development of an accelerometer-based wearable sensor system.
• Implementation of a rule-based algorithm for heartbeat detection from BCG/SCG signals.
• Initial performance evaluation using data from twelve healthy adults during rest and physical stress.

Main Results:

• The system achieved an average heartbeat detection rate of 87.6% across all measurements.
• High detection rates were observed at rest (97.6%), with lower rates during physical stress (71.9%).
• The developed algorithm enables health parameter derivation independent of external reference systems.

Conclusions:

• The DR.BEAT project demonstrates a promising wearable sensor system for cardiac health monitoring.
• The rule-based heartbeat detection algorithm shows potential for reliable cardiac assessment.
• Further validation is needed, especially under various physical stress conditions.