Deep time-recurrence features

Barts and The London School of Medicine and Dentistry, Queen Mary University of London Turner Street, London E1 2AD, UK

^(a) E-mail: tdpham123@gmail.com (corresponding author)

Received: 22 March 2023
Accepted: 19 May 2023

Abstract

In this study, the notion of recurrence in chaos and nonlinear dynamics is formulated as a new method for deep feature extraction from complex data. This feature type can enhance the learning power of a state-of-the-art approach in artificial intelligence using time series or sequential data. The deep time-recurrence features are derived using the fuzzy recurrence algorithm and the iterative procedure of convolution, non-linear transformation, and down-sampling. Experimental results obtained from using a public database of healthy and pathological voice signals show that the proposed features can significantly improve the classification performance of long short-term memory networks.