Detection of human reflex response in EMG signals: a time-frequency approach

Author(s):  
H. Laurent ◽  
C. Doncarli ◽  
M. Guglielmi
Author(s):  
L. Vacca-Galloway ◽  
Y.Q. Zhang ◽  
P. Bose ◽  
S.H. Zhang

The Wobbler mouse (wr) has been studied as a model for inherited human motoneuron diseases (MNDs). Using behavioral tests for forelimb power, walking, climbing, and the “clasp-like reflex” response, the progress of the MND can be categorized into early (Stage 1, age 21 days) and late (Stage 4, age 3 months) stages. Age-and sex-matched normal phenotype littermates (NFR/wr) were used as controls (Stage 0), as well as mice from two related wild-type mouse strains: NFR/N and a C57BI/6N. Using behavioral tests, we also detected pre-symptomatic Wobblers at postnatal ages 7 and 14 days. The mice were anesthetized and perfusion-fixed for immunocytochemical (ICC) of CGRP and ChAT in the spinal cord (C3 to C5).Using computerized morphomety (Vidas, Zeiss), the numbers of IR-CGRP labelled motoneurons were significantly lower in 14 day old Wobbler specimens compared with the controls (Fig. 1). The same trend was observed at 21 days (Stage 1) and 3 months (Stage 4). The IR-CGRP-containing motoneurons in the Wobbler specimens declined progressively with age.


2001 ◽  
Vol 120 (5) ◽  
pp. A718-A718
Author(s):  
C DIENEFELD ◽  
L WANG ◽  
K NEUFELD ◽  
Y MAO ◽  
S HOLLERBACH ◽  
...  

Author(s):  
Weihai Sun ◽  
Lemei Han

Machine fault detection has great practical significance. Compared with the detection method that requires external sensors, the detection of machine fault by sound signal does not need to destroy its structure. The current popular audio-based fault detection often needs a lot of learning data and complex learning process, and needs the support of known fault database. The fault detection method based on audio proposed in this paper only needs to ensure that the machine works normally in the first second. Through the correlation coefficient calculation, energy analysis, EMD and other methods to carry out time-frequency analysis of the subsequent collected sound signals, we can detect whether the machine has fault.


1997 ◽  
Vol 117 (3) ◽  
pp. 338-345 ◽  
Author(s):  
Masatake Kawada ◽  
Masakazu Wada ◽  
Zen-Ichiro Kawasaki ◽  
Kenji Matsu-ura ◽  
Makoto Kawasaki

2009 ◽  
Vol E92-B (12) ◽  
pp. 3717-3725
Author(s):  
Thomas HUNZIKER ◽  
Ziyang JU ◽  
Dirk DAHLHAUS

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