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

• Published in: IEEE journal of biomedical and health informatics 2017-03, Vol.21 (2), p.387-398
• Main Authors: Ansari, Sardar, Ward, Kevin R., Najarian, Kayvan
• Format: Article
• Language: English
• Subjects: Adaptive filters; Amplitudes; Biomedical monitoring; Electric Impedance - therapeutic use; Electrodiagnosis - methods; Electron tubes; Epsilon-tube; Humans; Impedance; impedance pneumography (IP); Independent component analysis; IP networks; Least mean squares; Least mean squares algorithm; Lung - physiology; Monitoring; Monitoring, Physiologic - methods; Morphology; Motion; motion artifact (MA); Pneumography; portable and in-home monitoring; Prototypes; Recursive methods; Reduction; Regularization; Respiration; respiration monitoring; Respiratory rate; Respiratory Rate - physiology; Signal monitoring; Signal Processing, Computer-Assisted; Statistical analysis; Time-frequency analysis
• ISSN: 2168-2194; 2168-2208
• DOI: 10.1109/JBHI.2016.2524646

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.