DocumentCode
1702512
Title
A non-parametric statistical approach to EMG signal analysis
Author
Erlandson, Robert F. ; Joynt, Robert L. ; Wu, Shi Jian ; Wang, Chuan-Ming
Author_Institution
Metropolitan Center for High Technol., Detroit, MI, USA
fYear
1989
Firstpage
727
Abstract
An event identification and classification technique in which electromyographic (EMG) signals are transformed from the time domain into a probability space using nonparametric statistics is reported. Data points with a high probability of being an event are collected into similarity groups using rank-order statistics. EMG interpulse-interval data are used to establish the motor unit components of the detected superimposition events
Keywords
bioelectric potentials; muscle; statistical analysis; waveform analysis; EMG interpulse-interval data; EMG signal analysis; classification technique; data points; event identification; motor unit components; probability space; similarity groups; superimposition events; Computer simulation; Electroencephalography; Electromyography; Engineering in medicine and biology; Medical signal detection; Muscles; Signal analysis; Sleep;
fLanguage
English
Publisher
ieee
Conference_Titel
Engineering in Medicine and Biology Society, 1989. Images of the Twenty-First Century., Proceedings of the Annual International Conference of the IEEE Engineering in
Conference_Location
Seattle, WA
Type
conf
DOI
10.1109/IEMBS.1989.95953
Filename
95953
Link To Document