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

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

An extended Kalman filter for fetal heart location estimation during Doppler-based heart rate monitoring

Fetal heart rate (fHR) monitoring using the Doppler ultrasound (US) is a standard clinical practice for assessing fetal well-being before and during labor. For continuous fHR measurements, the US transducer is positioned on the maternal abdomen using a flexible belt. Due to fetal movement, the relative fetal heart location (fHL) with respect to the US transducer can change, leading to frequent periods of signal loss hampering the clinical assessment of fetal health. Consequently, the clinical staff has to repeatedly reposition the US transducer--a cumbersome task affecting clinical workflow. We propose a method to estimate the fHL during fHR monitoring to support clinicians in efficiently repositioning the US transducer. Unlike typical US transducers, which do not provide any information on the spatial fHL, we exploit the fact that multiple transducer elements are present in the array aperture of the US transducer. We developed a novel model that relates the measured Doppler power in the individual transducer elements to the fHL and use it within the probabilistic framework of an extended Kalman filter (EKF). The performance of the EKF algorithm was evaluated in simulations and in in vitro experiments using a dedicated setup of a beating fetal heart. Both simulations and in vitro experiments showed that the fHL can be determined with an accuracy of 4 mm. Furthermore, we demonstrate that when the fetal heart is drifting out of the US beam, the EKF algorithm accurately estimates the fHL up to a radial distance of $34$ mm

An extended Kalman filter for fetal heart location estimation during Doppler-based heart rate monitoring

\u3cp\u3eFetal heart rate (fHR) monitoring using the Doppler ultrasound (US) is a standard clinical practice for assessing fetal well-being before and during labor. For continuous fHR measurements, the US transducer is positioned on the maternal abdomen using a flexible belt. Due to fetal movement, the relative fetal heart location (fHL) with respect to the US transducer can change, leading to frequent periods of signal loss hampering the clinical assessment of fetal health. Consequently, the clinical staff has to repeatedly reposition the US transducer--a cumbersome task affecting clinical workflow. We propose a method to estimate the fHL during fHR monitoring to support clinicians in efficiently repositioning the US transducer. Unlike typical US transducers, which do not provide any information on the spatial fHL, we exploit the fact that multiple transducer elements are present in the array aperture of the US transducer. We developed a novel model that relates the measured Doppler power in the individual transducer elements to the fHL and use it within the probabilistic framework of an extended Kalman filter (EKF). The performance of the EKF algorithm was evaluated in simulations and in in vitro experiments using a dedicated setup of a beating fetal heart. Both simulations and in vitro experiments showed that the fHL can be determined with an accuracy of 4 mm. Furthermore, we demonstrate that when the fetal heart is drifting out of the US beam, the EKF algorithm accurately estimates the fHL up to a radial distance of $34$ mm.\u3c/p\u3