Automatic Assessment of Electromyogram Quality

Power spectrum analysis of the diaphragm electromyogram (EMGdi) is time consuming, and no criteria have been developed to objectively quantify contamination of the signal. The present work describes a set of computer algorithms that automatically select EMGdi free of the electrocardiogram and numerically quantify the common artifacts that affect the EMGdi. The algorithms were tested 1) on human EMGdi (n = 5) obtained with esophageal electrodes positioned at the level of the gastroesophageal junction, 2) on EMGdi obtained in mongrel dogs (n = 5) with intramuscular electrodes in the costal diaphragm, and 3) on computer-simulated power spectra. For authentic and simulated power spectra, indexes were obtained by the algorithms and were able to quantify signal disturbances induced by noise, electrode motion, esophageal peristalsis (in humans), and non-QRS complex-related electrocardiogram activity. With the index inclusion thresholds set to levels that allowed for a high signal acceptance rate with relatively small artifact-induced fluctuations (10-15%) of the EMGdi center frequency, the computer algorithms were found to be as reliable as or more reliable than other methods, including careful visual selection of the time domain signals by experienced analysts. In conclusion, the frequency domain application of computer algorithms offers a reliable and reproducible means to objectively quantify the sources that contaminate the interference pattern EMG.