Distinction between essential and physiological tremor using Hilbert-Huang transform.

Neurophysiological investigation, based on accelerometric (ACC) and electromyographic (EMG) recordings, is an essential step in the diagnosis of tremor. Among various signal analysis methods, fast Fourier transform (FFT) is the most frequently used in this domain. However, FFT has several limitations: first, it assumes that tremor is a periodic and linear signal, which is not true; second, it cannot distinguish between different types of tremor, when their frequency overlap in similar range, such as essential tremor (ET) and physiological tremor (PT). Therefore, we decided to apply a non-linear method of signal analysis based on empirical mode decomposition (EMD) and Hilbert Huang transform (HHT), according to various procedures and compared to a more classical FFT approach. A first group of 8 healthy subjects with PT and a second group of 8 patients with ET were included in this study. At individual level, FFT was effective to highlight ET in the 8 patients, but PT in only 2 subjects. The EMD-HHT procedures performed better than FFT, revealing a common peak of PT in all subjects. Moreover, at group level, our EMD-HHT method allowed to clearly differentiate the two groups, especially by giving evidence for the existence of low frequency oscillations (around 4Hz) in subjects with PT. Although their physiological origin remains largely unknown, such slow oscillations seem to be of great importance to highlight PT and they have been much underestimated in the literature. Our original EMD-HHT approach is able to provide substantial improvement in the neurophysiological characterisation of the different types of tremor, especially for diagnostic application.