Motion Artifact Suppression in Impedance Pneumography Signal for Portable Monitoring of Respiration: An Adaptive Approach

Abstract

The focus of this paper is motion artifact (MA) reduction from the impedance pneumography (IP) signal, which is widely used to monitor respiration. The amplitude of the MA that contaminates the IP signal is often much larger than the amplitude of the respiratory component of the signal. Moreover, the morphology and frequency composition of the artifacts may be very similar to that of the respiration, making it difficult to remove these artifacts. The proposed filter uses a regularization term to ensure that the pattern of the filtered signal is similar to that of respiration. It also ensures that the amplitude of the filter output is within the expected range of the IP signal by imposing an ε-tube on the filtered signal. The adaptive ε-tube filter is 100 times faster than the previously proposed nonadaptive version and achieves higher accuracies. Moreover, the experimental results, using several different performance measures, suggest that the proposed method outperforms popular MA reduction methods such as normalized least mean squares (NLMS) and recursive least squares (RLS) as well as independent component analysis (ICA). When used to extract the respiratory rate, the adaptive ε-tube achieves a mean error of 1.27 breaths per minute (BPM) compared to 4.72 and 4.63 BPM for the NLMS and RLS filters, respectively. When compared to the ICA algorithm, the proposed filter has an error of 1.06 BPM compared to 3.47 BPM for ICA. The statistical analyses indicate that all of the reported performance improvements are significant.

Chest Movement and Respiratory Volume both Contribute to Thoracic Bioimpedance during Loaded Breathing

“…Therefore, the objective is to have more knowledge about thoracic bioimpedance changes and its relation with breathing movement and respiratory volume. The combination of accelerometer and bioimpedance measurements has been used before, however, these studies used the combination for motion aritifact removal 22,23 . These studies did not examine the relationship between these signals.…”

Utility of a smartphone based system (cvrphone) to accurately determine apneic events from electrocardiographic signals

“…In general, these studies showed acceptable validity and reliability for RR monitoring, but lower validity and reliability for TV and minute ventilation. Recently, researchers have constructed ambulatory impedance pneumography devices for quantitative monitoring of respiratory characteristics [13], and developed algorithms to remove motion artifact in impedance pneumography signals [23]. Further efforts have been made to develop new methods to estimate the RR from ECG and photoplethysmogram signals [24, 25].…”

Design and Implementation of Wearable Dynamic Electrocardiograph Real-Time Monitoring Terminal

“…Ansai et al found that the adaptive filter based on N.L.M.S. algorithm was applied to remove motion artifacts in respiratory signals with an average error of only 4.72 [10]. Satyanarayana et al compared the noise interference of the adaptive filter based on LMS and N.L.M.S.…”