Multiresolution segmentation of respiratory electromyographic signals.
Analysis of respiratory electromyographic (EMG) signals in the study of respiratory control requires the detection of burst activity from background (signal segmentation), and focuses upon the determination of onset and cessation points of the burst activity (boundary estimation). This paper describes a new automated multiresolution technique for signal segmentation and boundary estimation. During signal segmentation, a new transitional segment is defined which contains the boundary between background and burst activity. Boundary estimation is then performed within this transitional segment. Boundary candidates are selected and a probability is attributed to each candidate, using an artificial neural network. The final boundary for a given transitional segment is the boundary estimate with the maximum a posteriori probability. This new method has proved accurate when compared to boundaries chosen by two investigators.
Choi
H G
HG
Department of Electrical Engineering, University of Florida, Gainesville 32611.
Principe
J C
JC
Hutchison
A A
AA
Wozniak
J A
JA
D000200:Action Potentials / Q000502:physiology; D000465:Algorithms; D000818:Animals; D004576:Electromyography; D006801:Humans; D016571:Neural Networks, Computer; D012132:Respiratory Muscles / Q000502:physiology*; D012756:Sheep; D012815:Signal Processing, Computer-Assisted