A Novel Combination Method of a Convolutional Neural Network and Energy Operators for the Detection of Change-Points in Electromyographic Signals

Currently, neural network algorithms based on time-domain features are used for change-point detection problems, and they have proven to be effective. However, due to the instability of human biosignals, establishing a training dataset with labels is difficult. For supervised learning methods, wherein parameters are updated on a small sample set through a feed-forward mechanism, it is difficult to ascertain the degree to which the performance of the trained neural network corresponds to the overfitting of the dataset upon which the network was trained. To this end, this paper attempted to directly replace the parameters in the convolutional neural network that need to be updated by training. A method based on the combination of the Teager–Kaiser energy operator (TKEO) and the convolutional neural network is proposed. We tested the proposed method on simulated EMG data with different signal-to-noise ratios and real data with labels, respectively. Compared with multiple detection methods, the proposed method had significant advantages in terms of reliability, accuracy, and computational speed. Furthermore, the proposed method does not require any prior knowledge about the signal, lending itself to be flexible and adaptable to any application. It may be a promising alternative to solving change-point detection problems.