A ballistocardiogram dataset with reference sensor signals in long-term natural sleep environments

Area of Science:

• Biomedical Engineering
• Physiological Monitoring
• Sleep Science

Background:

• Unobtrusive and continuous sleep monitoring is crucial for assessing sleep quality.
• Ballistocardiogram (BCG) signals offer potential for non-invasive physiological monitoring during sleep.
• Existing datasets may lack comprehensive, continuous BCG data in natural sleep environments.

Purpose of the Study:

• To present a novel dataset for sleep monitoring using ballistocardiogram (BCG) signals.
• To facilitate research on intelligent sleep quality assessment and unobtrusive vital sign monitoring.
• To provide data for evaluating and optimizing sleep quality using BCG.

Main Methods:

• Collected continuous ballistocardiogram (BCG) data from 32 subjects over multiple nights.
• Utilized piezoelectric film sensors for unobtrusive BCG data acquisition in regular sleep environments.
• Recorded reference heart rate and respiratory data for validation purposes.

Main Results:

• The dataset comprises multi-night continuous BCG recordings from 32 individuals.
• Reference physiological data (heart rate, respiration) are included for signal validation.
• The data enables extraction and validation of cardiac and respiratory components from BCG.

Conclusions:

• The presented BCG dataset is a valuable resource for sleep monitoring research.
• It supports the development of intelligent systems for sleep quality assessment.
• The dataset aids in optimizing sleep quality evaluation through unobtrusive vital sign monitoring.