Improved surface EMG electrode for measuring genioglossus muscle activity.

Activation of the genioglossus (GG) muscles is necessary to maintain the patency of the upper airway. In the condition of obstructive sleep apnea (OSA) this mechanism fails and the possible role of fatigue in its pathogenesis is still not fully understood. In this paper, a new electrode design for recording the genioglossus surface electromyogram (sEMG) is presented. The new design differs from a widely used GG surface electrode in both electrode configuration (unilateral rather than bilateral) and electrode material (Ag-AgCl rather than stainless steel (SS)). The separate effects of these factors were evaluated during force-varying and fatiguing contractions on normal human subjects and using GG sEMG model simulations. Unilateral sEMG was found to have lower amplitude, lower frequency content and a different rate of change of median frequency during fatiguing contractions. It was shown to overcome several disadvantages posed by the bilateral configuration and be more selective. Ag-AgCl has more favorable impedance characteristics and resulted in greater signal amplitudes. It was concluded that the new design is more suitable for detecting GG sEMG and allows more reliable interpretation of changes in sEMG due to physiological mechanisms, thus providing a new methodology for studying GG function and the role of fatigue in OSA.

Design of surface electrode array for measuring conduction velocity in the human genioglossus muscle.

A new appliance, incorporating linear arrays of pin electrodes for genioglossus (GG) surface electromyography measurement, is presented. This design enables the estimation of GG muscle fiber conduction velocity, which decreases with fatigue. The performance of the device was evaluated for ten healthy human subjects during fatiguing and force varying contractions.

Automatic detection of gait events using kinematic data.

The timing of heel strike (HS) and toe off (TO), the events that mark the transitions between stance and swing phase of gait, is essential when analysing gait. Force plate recordings are routinely used to identify these events. Additional instrumentation, such as force sensitive resistors, can also been used. These approaches, however, include restrictions on the number of steps that can be analyzed and further encumbrance of the subject. We developed an algorithm which automatically determines these times from kinematic data recorded by a motion capture system, which is routinely used in gait analysis laboratories. The foot velocity algorithm (FVA) uses data from the heel and toe markers and identifies features in the vertical velocity of the foot which correspond to the gait events. We verified the performance of the FVA using a large data set of 54 normal children that contained both force plate recordings and kinematic data and found errors of (mean+/-standard deviation) 16+/-15 ms for HS and 9+/-15 ms for TO. The algorithm also worked well when tested on a small number of children with spastic diplegia. We compared the performance of the FVA with another kinematic method previously described. Our foot velocity algorithm offered more accurate results and was easier to implement than the previously described one, and should be applicable in a variety of gait analysis settings.