Drift Removal in Plant Electrical Signals via IIR Filtering Using Wavelet Energy

TL;DR

This paper presents an optimized IIR filtering method using wavelet energy to remove low frequency drifts from plant electrical signals, enhancing the analysis of stimulus-specific responses.

Contribution

It introduces a novel wavelet energy-based optimization approach for tuning IIR filters to effectively remove drifts while preserving relevant frequency components in plant signals.

Findings

The optimized filter aligns pre-stimulus energy distributions across experiments.

It effectively distinguishes stimulus-induced changes in plant electrical signals.

The method enhances plant signal processing by reducing artifacts and drifts.

Abstract

Plant electrical signals often contains low frequency drifts with or without the application of external stimuli. Quantification of the randomness in plant signals in a stimulus-specific way is hindered because the knowledge of vital frequency information in the actual biological response is not known yet. Here we design an optimum Infinite Impulse Response (IIR) filter which removes the low frequency drifts and preserves the frequency spectrum corresponding to the random component of the unstimulated plant signals by bringing the bias due to unknown artifacts and drifts to a minimum. We use energy criteria of wavelet packet transform (WPT) for optimization based tuning of the IIR filter parameters. Such an optimum filter enforces that the energy distribution of the pre-stimulus parts in different experiments are almost overlapped but under different stimuli the distributions of the energy get changed. The reported research may popularize plant signal processing, as a separate field, besides other conventional bioelectrical signal processing paradigms.