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Related Experiment Video

Updated: Apr 28, 2026

MRI-guided Focused Ultrasound Thalamotomy for Patients with Medically-refractory Essential Tremor
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Distinction between essential and physiological tremor using Hilbert-Huang transform.

S S Ayache1, T Al-Ani2, J-P Lefaucheur1

  • 1Service de physiologie, hôpital Henri-Mondor, AP-HP, 51, avenue de Maréchal-de-Lattre-de-Tassigny, 94010 Créteil, France; EA 4391, faculté de médecine, université Paris Est Créteil, 8, avenue du Général-Sarrail, 94010 Créteil, France.

Neurophysiologie Clinique = Clinical Neurophysiology
|June 17, 2014
PubMed
Summary

A new non-linear signal analysis method using empirical mode decomposition (EMD) and Hilbert Huang transform (HHT) effectively differentiates essential tremor (ET) from physiological tremor (PT), outperforming traditional fast Fourier transform (FFT). This approach reveals distinct low-frequency oscillations in PT, aiding diagnosis.

Keywords:
AccelerometerAccéléromètreAnalyse du signalDécomposition en mode empiriqueElectromyographyEmpirical mode decompositionEssential tremorFast Fourier transformHilbert-Huang transformPhysiological tremorSignal analysisTransformée de Hilbert HuangTransformée rapide de FourierTremblement essentielTremblement physiologiqueÉlectromyographie

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Neurophysiological investigation using accelerometric (ACC) and electromyographic (EMG) recordings is crucial for tremor diagnosis.
  • Fast Fourier Transform (FFT) is commonly used but has limitations, including assuming linearity and difficulty distinguishing overlapping tremor frequencies like essential tremor (ET) and physiological tremor (PT).

Purpose of the Study:

  • To apply and compare a non-linear signal analysis method, empirical mode decomposition (EMD) combined with Hilbert Huang Transform (HHT), against the classical FFT approach for tremor diagnosis.
  • To investigate the effectiveness of EMD-HHT in differentiating between ET and PT.

Main Methods:

  • Collected ACC and EMG data from 8 healthy subjects with PT and 8 patients with ET.
  • Applied both FFT and various EMD-HHT procedures for signal analysis.
  • Compared the performance of FFT and EMD-HHT at individual and group levels.

Main Results:

  • FFT identified ET in all patients but PT in only two healthy subjects.
  • EMD-HHT procedures outperformed FFT, revealing a common PT peak in all subjects and clearly differentiating ET and PT groups.
  • The EMD-HHT method highlighted unique low-frequency oscillations (around 4Hz) in PT subjects, previously underestimated in literature.

Conclusions:

  • The novel EMD-HHT approach offers substantial improvement in neurophysiological characterization of tremors compared to FFT.
  • This non-linear method shows significant potential for improving the diagnostic accuracy of different tremor types, particularly for distinguishing PT and ET.
  • The identified slow oscillations in PT are important diagnostic markers.