Abstract
A bed sensor with multiple pressure sensitive
non-contacting electrodes has been applied for
unobtrusive monitoring during sleep. The novelty is in
using multichannel algorithms to improve extraction of
the heart rate and respiration signal from the recorded
ballistocardiographic (BCG) data. Heart rate is extracted
by using a sliding Fourier Transform, and after averaging
the sensor channels in the frequency domain, the attained
resolution enables to detect individual heart beat
intervals (HBI) and estimate the heart rate variability
(HRV). The respiration signal is calculated from the low
pass filtered BCG signals by updating the linear
coefficients with an adaptive principal component
analysis (PCA) model. In comparison to the reference ECG
R-R interval, the relative error of the HBI has been 0.40
% with 88 % measurement coverage for the healthy subjects
during normal sleep. The error of the respiratory rate
estimated from the bed sensor has been 1.5 % in
comparison with the respiratory inductive plethysmogram
(RIP). For the group of patients having different kinds
of suspected sleep disorders, the measurement coverage
varied a lot between subjects due to increased movement
artifacts. In this case, some examples of detecting
respiratory disorders with bed sensor signals are shown.
Original language | English |
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Title of host publication | Proceedings of the 39th Computing in Cardiology, 2012 |
Pages | 313-316 |
Publication status | Published - 2012 |
MoE publication type | A4 Article in a conference publication |
Event | 39th Computing in Cardiology Conference - Krakow, Poland Duration: 9 Sept 2012 → 12 Sept 2012 |
Conference
Conference | 39th Computing in Cardiology Conference |
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Country/Territory | Poland |
City | Krakow |
Period | 9/09/12 → 12/09/12 |
Keywords
- bed sensor
- heart rate
- sleep
- apnoea
- ballistocardiographic