Continuous Cardiorespiratory Monitoring Using Ballistocardiography From Load Cells Embedded in a Hospital Bed

The objective of this research is to explore signal processing and machine learning techniques to allow continuous monitoring of cardiorespiratory parameters using the ballistocardiogram (BCG) signals recorded with sensors embedded in a hospital bed. First, the heart rate (HR) estimation algorithms were presented. The first is signal processing-based HR estimation with array processing for multi-channel combination. The second uses a deep learning (DL) model that transforms BCG signals into an interpretable triangular waveform, from which heartbeat locations can be estimated. Second part of the work focuses on estimating respiratory rate (RR) and respiratory volume (RV) using the respiration waveforms derived from the low-frequency components of the load cell signals. Lastly, this work presents two models for blood pressure (BP) estimation -- 1) Conventional pulse transit time (PTT)-based model and 2) DL-based model, both using multi-channel BCG and the photoplethysmogram (PPG) signals to extract features. Overall, this work established methods to enable non-invasive and continuous monitoring of standard vital signs utilizing the sensors already embedded in commonly-deployed commercially available hospital beds. Such technologies could potentially improve the continuous assessment of the patients' physiologic state without adding an extra burden on the caregivers.

Level thesis:degree_level

Doctoral

Department dc:contributor.department

Electrical and Computer Engineering

Grantor dc:publisher

Georgia Institute of Technology

Year dc:date.issued

2022

Author dc:creator

• Jung, Hewon

Advisor dc:contributor.advisor

• Inan, Omer T.

Committee members dc:contributor.committeemember

• Zhang, Ying
• Yeo, Woon-Hong
• Ghalichechian, Nima
• Kamaleswaran, Rishikesan

• Continuous Cardiorespiratory Monitoring
• Ballistocardiography
• Machine Learning
• Signal Processing

Language dc:language.iso

en_US

Handle dc:identifier.uri

http://hdl.handle.net/1853/67266

OAI identifier oai:identifier

oai:repository.gatech.edu:1853/67266