title: Tissue artifact removal from respiratory signals based on empirical mode decomposition.
abstract: On-line measurement of respiration plays an important role in monitoring human physical activities. Such measurement commonly employs sensing belts secured around the rib cage and abdomen of the test object. Affected by the movement of body tissues, respiratory signals typically have a low signal-to-noise ratio. Removing tissue artifacts therefore is critical to ensuring effective respiration analysis. This paper presents a signal decomposition technique for tissue artifact removal from respiratory signals, based on the empirical mode decomposition (EMD). An algorithm based on the mutual information and power criteria was devised to automatically select appropriate intrinsic mode functions for tissue artifact removal and respiratory signal reconstruction. Performance of the EMD-algorithm was evaluated through simulations and real-life experiments (N = 105). Comparison with low-pass filtering that has been conventionally applied confirmed the effectiveness of the technique in tissue artifacts removal.
authors: Department of Mechanical Engineering, University of Connecticut, 191 Auditorium Rd., Unit-3139, Storrs, CT 06269, USA.
authors: Shaopeng
authors: S
authors: Liu
authors: Robert X
authors: RX
authors: Gao
authors: Dinesh
authors: D
authors: John
authors: John
authors: J
authors: Staudenmayer
authors: Patty
authors: P
authors: Freedson
mesh_terms: D000465:Algorithms; D016477:Artifacts; D001330:Electronic Data Processing / Q000379:methods*; D006801:Humans; D012123:Pulmonary Ventilation; D015656:Respiratory Mechanics; D012135:Respiratory Sounds