access icon free Automated system for weak periodic signal detection based on Duffing oscillator

© The Institution of Engineering and Technology
Received 29/04/2020, Accepted 08/12/2020, Revised 07/12/2020, Published 05/01/2021

The periodic signals that have predictable and deterministic characteristics are used in the analysis and modelling of dynamical systems in diverse fields. These signals can be detected as the weak signals within the time series obtained from the measurable processes of dynamical systems. The Duffing oscillator is effective in detecting weak periodic signals with a very low signal-to-noise ratio. In this study, the authors present a method to automate the weak periodic signal detection of the Duffing oscillator using a quantitative index for the classification of the periodic and non-periodic signals. In this method, the authors use the wavelet scale index as the quantitative index in the classification of signals. Thus, they are able to plot the wavelet scale index spectrum of the Duffing oscillator where the frequency values of the weak periodic signals correspond to near-zero wavelet scale index parameters. First, the authors perform simulations using the method and detect weak periodic signals embedded in noise. Then, they employ two electroencephalogram signals to demonstrate the feasibility of the proposed method in the empirical data. Lastly, they compare the method to the periodogram power spectral density estimate based on fast Fourier transform.

Inspec keywords: signal classification; spectral analysis; medical signal detection; electroencephalography; fast Fourier transforms; medical signal processing; oscillators; wavelet transforms

Other keywords: nonperiodic signal classification; electroencephalogram signals; near-zero wavelet scale index parameters; Duffing oscillator; wavelet scale index spectrum; quantitative index; weak periodic signal detection; periodogram power spectral density; fast Fourier transform; periodic signal classification

Subjects: Bioelectric signals; Digital signal processing; Biology and medical computing; Signal detection; Electrical activity in neurophysiological processes; Electrodiagnostics and other electrical measurement techniques

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