Title :
Multiresolution segmentation of respiratory electromyographic signals
Author :
Choi, Haan-Go ; Principe, Jose C. ; Hutchison, Alastair A. ; Wozniak, John A.
Author_Institution :
Dept. of Electr. Eng., Florida Univ., Gainesville, FL, USA
fDate :
3/1/1994 12:00:00 AM
Abstract :
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). The authors describe 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 a 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.
Keywords :
bioelectric potentials; medical signal processing; muscle; artificial neural network; automated multiresolution technique; boundary candidates; boundary estimation; burst activity detection; medical signal analysis; multiresolution segmentation; respiratory electromyographic signals; signal segmentation; transitional segment; Central nervous system; Centralized control; Control systems; Electrodes; Electromyography; Humans; Muscles; Signal resolution; Spatial resolution; Timing; Action Potentials; Algorithms; Animals; Electromyography; Humans; Neural Networks (Computer); Respiratory Muscles; Sheep; Signal Processing, Computer-Assisted;
Journal_Title :
Biomedical Engineering, IEEE Transactions on