Automatic selection of uncontaminated electromyogram as applied to respiratory muscle fatigue

Abstract

An automatic procedure for detecting artifacts in the electromyogram (EMG) has been developed and applied to a study of respiratory muscle fatigue. Signal segments are characterized by a set of features, the normal variations of which have been estimated in a training session. From the features are calculated a classification variable, which expresses the degree of deviation from normal conditions. A deviation larger than a certain threshold value designates a segment as disturbed. The study deals with the choice of features, the selection of a suitable segment length, and the determination of an optimal classification threshold. The four features chosen include measures of amplitude symmetry, extreme excursions in the signal tracing, the signal-to-noise ratio, and the shape of the EMG power spectrum. Recordings from three subjects were used for the evaluation of the method. The results indicate that a segment length of 250 ms is appropriate. Accepting a 10% rate of false detections, the average rate of missed detections was 2.2%.