Respiratory rate estimation from multi-channel signals using auto-regulated adaptive extended Kalman filter

Gupta, Nishant; Simmen, Patrizia; Trachsel, Daniel; Haeberlin, Andreas; Jost, Kerstin; Niederhauser, Thomas (2023). Respiratory rate estimation from multi-channel signals using auto-regulated adaptive extended Kalman filter Biomedical Signal Processing and Control, 84, p. 104977. Elsevier 10.1016/j.bspc.2023.104977

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Background: Respiration rate (RR) is a major cause for false alarms in intensive care units (ICU) and is primarily impaired by the artifact prone signals from skin-attached electrodes. Catheter-integrated esophageal electrodes are an alternative source for multi-channel physiological signals from multiple organs such as the heart and the diaphragm. Nonlinear estimation and sensor fusion are promising techniques for extracting the respiratory activity from such multi-component signals, however, pathologic breathing patterns with rapid RR changes typically observed in patient populations such as premature infants, pose significant challenges. Methods: We developed an auto-regulated adaptive extended Kalman filter (AA-EKF), which iteratively adapts the system model and the noise parameters based on the respiratory pattern. AA-EKF was tested on neonatal esophageal observations (NEO), and also on simulated multi-components signals created using waveforms in CapnoBase and ETNA databases. Results: AA-EKF derived RR (RRAA-EKF) from NEO had lower median (inter-quartile range) error of 0.1 (10.6) breaths per minute (bpm) compared to contemporary neonatal ICU monitors (RRNICU): −3.8 (15.7) bpm (p <0.001). RRAA-EKF error of −0.2 (3.2) bpm was achieved for ETNA wave forms and a bias (95% LOA) of 0.1 (−5.6, 5.9) in breath count. Mean absolute error (MAE) of RRAA-EKF with Capnobase waveforms, as median (inter-quartile range), at 0.3 (0.2) bpm was comparable to the literature reported values. Discussion: The auto-regulated approach allows RR estimation on a broad set of clinical data without requiring extensive patient specific adjustments. Causality and fast response times of EKF based algorithms makes the AA-EKF suitable for bedside monitoring in the ICU setting.

Item Type:

Journal Article (Original Article)

Division/Institute:

School of Engineering and Computer Science > Institute for Human Centered Engineering (HUCE)
School of Engineering and Computer Science > Institute for Human Centered Engineering (HUCE) > HUCE / Laboratory for Microelectronics and Medical Devices
School of Engineering and Computer Science
BFH Centres and strategic thematic fields > BFH centre for Health technologies

Name:

Gupta, Nishant;
Simmen, Patrizia;
Trachsel, Daniel;
Haeberlin, Andreas;
Jost, Kerstin and
Niederhauser, Thomas0000-0003-2633-0844

Subjects:

R Medicine > RJ Pediatrics
T Technology > TA Engineering (General). Civil engineering (General)

ISSN:

1746-8094

Publisher:

Elsevier

Language:

English

Submitter:

Thomas Niederhauser

Date Deposited:

07 Jun 2023 08:53

Last Modified:

11 Jun 2023 01:37

Publisher DOI:

10.1016/j.bspc.2023.104977

Related URLs:

Uncontrolled Keywords:

Respiration rate Sensor fusion Kalman filters Neonates NICU Esophagus

ARBOR DOI:

10.24451/arbor.19294

URI:

https://arbor.bfh.ch/id/eprint/19294

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