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Heartbeat detection from a hydraulic bed sensor using a clustering approach.

Licet Rosales¹, Marjorie Skubic, David Heise

¹Department of Electrical and Computer Engineering, University of Missouri, Columbia, MO 65211, USA. lr5zf@missouri.edu

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Summary

This summary is machine-generated.

A new hydraulic transducer and k-means clustering method accurately detect heartbeats from ballistocardiogram (BCG) signals. This system shows high detection rates across diverse participants, improving non-invasive cardiac monitoring.

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Area of Science:

Biomedical Engineering
Cardiovascular Technology
Signal Processing

Background:

Previous hydraulic bed sensors showed promise for non-invasive physiological monitoring.
Accurate heartbeat detection from ballistocardiogram (BCG) signals is crucial for remote health assessment.

Purpose of the Study:

To develop an improved hydraulic transducer for capturing heartbeat signals.
To implement a novel k-means clustering algorithm for robust heartbeat detection from BCG data.

Main Methods:

A new hydraulic transducer configuration was designed and tested.
The k-means clustering algorithm was applied to BCG signals, focusing on J-peak identification.
The system was evaluated on four participants with varying physical characteristics and cardiac histories.

Main Results:

The enhanced transducer successfully captured heartbeat signals from all participants.
The k-means clustering approach achieved 98.6-100% correct heartbeat detection for three participants.
One participant showed detection rates between 71.0% and 92.5%, indicating areas for system refinement.

Conclusions:

The proposed hydraulic transducer and k-means clustering method offer a promising approach for accurate, non-invasive heartbeat detection.
Further adjustments to the system may enhance detection accuracy for all individuals.
This technology has potential applications in remote and continuous cardiac monitoring.