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EMG-based characterization of pathological tremor using the iterated Hilbert transform.

Jakob Lund Dideriksen¹, Francesco Gianfelici, Lana Z Popovic Maneski

¹Department of Health and Science Technology, Faculty of Engineering, Medicine, and Sport Science, Aalborg University, DK-9220 Aalborg, Denmark. jldi@hst.aau.dk

IEEE Transactions on Bio-Medical Engineering

|August 2, 2011

Summary

This study introduces an iterated Hilbert transform (IHT) method to accurately estimate pathological tremor characteristics from surface electromyogram (EMG) signals. The IHT effectively models tremor and voluntary activity for improved tremor suppression strategies.

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Area of Science:

Biomedical Engineering
Neuroscience
Signal Processing

Background:

Pathological tremor characterization is crucial for developing effective tremor suppression techniques, such as functional electrical stimulation.
Accurate estimation of tremor's amplitude, phase, and frequency from muscle activity (EMG) or movement (kinematics) is essential.
Existing methods may lack accuracy or require prior knowledge of tremor characteristics.

Purpose of the Study:

To present and validate a novel approach for estimating pathological tremor characteristics from surface electromyogram (EMG) signals.
To demonstrate the efficacy of the iterated Hilbert transform (IHT) in modeling tremor and voluntary activity components.
To assess the performance of the IHT method against simulated and experimental pathological tremor data.

Main Methods:

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Utilized the iterated Hilbert transform (IHT) for signal processing of surface electromyogram (EMG) data.
Tested the IHT method on simulated tremor signals from a tremor generation model and experimentally recorded patient data.
Compared the IHT method's performance with empirical mode decomposition (EMD) for tremor characterization.

Main Results:

The IHT method effectively demodulated tremor amplitude (R²=0.52), frequency (RMSE=2.6 Hz), and phase.
Accurate estimation of voluntary activity was achieved (R²=0.62 with simulated inertial load).
Estimated tremor components from experimental data showed high correlation with inertial measurements of limb movement (0.62±0.15).

Conclusions:

The iterated Hilbert transform (IHT) provides an accurate and effective method for characterizing pathological tremor from EMG signals.
The IHT method outperforms empirical mode decomposition in tremor analysis without requiring a priori knowledge.
This approach holds potential for integration into control systems for tremor suppression strategies.