Publications

The electrical activity of the diaphragm measured by surface electromyography (sEMGdi) provides indirect information on neural respiratory drive. Moreover, it allows evaluating the ventilatory pattern from the onset and offset (ntoff) estimation of the neural inspiratory time. sEMGdi amplitude variation was quantified using the fixed sample entropy (fSampEn), a less sensitive method to the interference from cardiac activity. The detection of the ntoff is controversial, since it is located in an intermediate point between the maximum value and the cessation of sEMGdi inspiratory activity, evaluated by the fSampEn. In this work ntoff detection has been analyzed using thresholds between 40% and 100 % of the fSampEn peak. Furthermore, fSampEn was evaluated analyzing the r parameter from 0.05 to 0.6, using a m equal to 1 and a sliding window size equal to 250 ms. The ntoff has been compared to the offset time (toff) obtained from the airflow during a controlled respiratory protocol varying the fractional inspiratory time from 0.54 to 0.18 whilst the respiratory rate was constant at 16 bpm. Results show that the optimal threshold values were between 66.0 % to 77.0 % of the fSampEn peak value. r values between 0.25 to 0.50 were found suitable to be used with the fSampEn.

JTD Keywords: Protocols, Low pass filters, Electrodes, Standards, Band-pass filters, Muscles, Cutoff frequency

EMG signals reflect the neuromuscular activation patterns related to the execution of a certain movement or task. In this work, we focus on reaching and grasping (R&G) movements in rats. Our objective is to develop an automatic algorithm to detect the onsets and offsets of muscle activity and use it to study muscle latencies in R&G maneuvers. We had a dataset of intramuscular EMG signals containing 51 R&G attempts from 2 different animals. Simultaneous video recordings were used for segmentation and comparison. We developed an automatic onset/offset detector based on the ratio of local maxima of Teager-Kaiser Energy (TKE). Then, we applied it to compute muscle latencies and other features related to the muscle activation pattern during R&G cycles. The automatic onsets that we found were consistent with visual inspection and video labels. Despite the variability between attempts and animals, the two rats shared a sequential pattern of muscle activations. Statistical tests confirmed the differences between the latencies of the studied muscles during R&G tasks. This work provides an automatic tool to detect EMG onsets and offsets and conducts a preliminary characterization of muscle activation during R&G movements in rats. This kind of approaches and data processing algorithms can facilitate the studies on upper limb motor control and motor impairment after spinal cord injury or stroke.

JTD Keywords: Muscles, Electromyography, Rats, Low pass filters, Microsoft Windows, Band-pass filters