Abstract:
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Recently developed techniques allow the analysis
of surface EMG in multiple locations over the skin
surface (high-density surface electromyography,
HDsEMG). The detected signal includes information from
a greater proportion of the muscle of interest than conventional
clinical EMG. However, recording with many
electrodes simultaneously often implies bad-contacts,
which introduce large power-line interference in the corresponding
channels, and short-circuits that cause nearzero
single differential signals when using gel. Such signals
are called ‘outliers’ in data mining. In this work, outlier
detection (focusing on bad contacts) is discussed for
monopolar HDsEMG signals and a new method is proposed
to identify ‘bad’ channels. The overall performance
of this method was tested using the agreement rate against
three experts’ opinions. Three other outlier detection
methods were used for comparison. The training and test
sets for such methods were selected from HDsEMG signals
recorded in Triceps and Biceps Brachii in the upper arm
and Brachioradialis, Anconeus, and Pronator Teres in the
forearm. The sensitivity and specificity of this algorithm
were, respectively, 96.9 ± 6.2 and 96.4 ± 2.5 in percent in the test set (signals registered with twenty 2D electrode arrays corresponding to a total of 2322 channels), showing that this method is promising. |