Aphid EPG waveforms classification based on wavelet kernel extreme learning machine

Abstract Insect electrical penetration graph (EPG) instrument is a powerful tool to study the feeding behavior of piercing-sucking insect and insect transmission virus mechanism. However, the classification of EPG waveforms has been carried out manually. It is urgent to identify EPG waveforms automatically to improve the analysis efficiency. Machine learning methods are widely used in classifying human bioelectrical signals, but rarely in EPG signals. In this study, the kernel extreme learning machine (ELM) was applied to the classification of EPG waveforms. Morlet wavelet function was used as the kernel function of the ELM, and the number of neurons was selected adaptively by incremental algorithm to solve the classification problem of EPG waveforms quickly and accurately. In the experiment, the 6-dimensional feature vector was used to enter the wavelet kernel extreme learning machine (WKELM) which was composed of low frequency wavelet energy in the 2nd and 3rd layers, fractal box dimension, Hurst exponent and spectral centroid in the first two layers of HHT. The average classification accuracy was 94.47%, which was 3.04% higher than the previous study. The experimental results showed that the proposed classification method of EPG waveforms based on WKELM has high identification performance, which laid a theoretical foundation for the research and development of EPG waveforms automatic identification and analysis system.