An optimal Bayesian threshold method for onset detection in electric biosignals

In this work, we consider the problem of identifying activity phases in electromyography signals, and various other potential types of electrical and non-electrical biological signals such as electroneurograms, electroencephalograms, voice and ultrasounds. The solution to this problem has been provided under relatively limited scenarios. The purpose of the present work is to propose an optimal Bayesian classifier to solve the problem of detecting bursts on biological signals. To that end, a parametrization of the distribution of samples in signals is presented. We propose a model based on a linear combination of normal distributions with mean equal to zero and different variances. The threshold criterion is expressed in a closed-form, and the use of morphology operators in the post-processing treatment leads to accurate results. Various comparisons are provided against other techniques available in the literature. In all of our experiments, we show that our present approach yields superior results.