Drift removal in plant electrical signals via IIR filtering using wavelet energy

• Drifts or trends in plant electrical signals are removed using IIR filters.
• Energy of wavelet packet nodes are used as selection criteria of the filters.
• Optimum filters enforce minimum discrimination of pre-stimulus plant signals.
• Optimization based tuning of filter parameters using wavelet energy feature.
• Variability with different wavelet basis is explored to test the robustness.

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.

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