Non-invasive characterization of motor unit behaviour in pathological tremor

Abstract

This paper presents the fully automatic identification of motor unit spike trains from high-density surface electromyograms (EMG) in pathological tremor. First, a mathematical derivation is provided to theoretically prove the possibility of decomposing noise-free high-density surface EMG signals into motor unit spike trains with high correlation, which are typical of tremor contractions. Further, the proposed decomposition method is tested on simulated signals with different levels of noise and on experimental signals from 14 tremor-affected patients. In the case of simulated tremor with central frequency ranging from 5 Hz to 11 Hz and signal-to-noise ratio of 20 dB, the method identified ∼8 motor units per contraction with sensitivity in spike timing identification ≥ 95% and false alarm and miss rates ≤ 5%. In experimental signals, the number of identified motor units varied substantially (range 0-21) across patients and contraction types, as expected. The behaviour of the identified motor units was consistent with previous data obtained by intramuscular EMG decomposition. These results demonstrate for the first time the possibility of a fully non-invasive investigation of motor unit behaviour in tremor-affected patients. The method provides a new means for physiological investigations of pathological tremor.

Age-related changes in motor unit firing pattern of vastus lateralis muscle during low-moderate contraction

“…1). This decomposition technique has been validated extensively with both simulated and experimental signals, and the detailed analytical procedure was described (Farina et al 2010;Gallego et al 2015a, b;Holobar et al 2009;Yavuz et al 2015) and applied in many previous studies (Holobar et al 2012;Minetto et al 2009;Minetto et al 2011;Watanabe et al 2013). The pulse-to-noise ratio (PNR), introduced by , was used as a reliable indicator of motor unit identification accuracy .…”

“…This causes considerable discomfort to the investigated subjects. In recent years, there have been several attempts to identify detailed motor unit firing characteristics noninvasively using multichannel surface EMG (SEMG) (Farina and Enoka 2011;Farina et al 2004;Holobar et al 2009Holobar et al , 2012Zazula 2004, 2008;Merletti et al 2008;Watanabe et al 2013). This technique is useful when the insertion of needle or wire electrodes is not desirable or p o s s i b l e .…”

One central oscillatory drive is compatible with experimental motor unit behaviour in essential and Parkinsonian tremor

“…The convolution kernel compensation decomposition algorithm [19,30,31] was used to extract single motor unit spike trains from the multi-channel surface EMG. As in previous studies of motor unit activity in tremor [32], only identified spike trains with pulse-to-noise ratio ^26 dB (metric describing the decomposition accuracy; see [33] for details) was included in the analysis.…”

“…Furthermore, numerical simulations will show how the superimposition of a secondary oscillator (at 7/2) changes this relative power depending on the relative amplitude and phase between the two oscillators. The analytical results were compared to recordings of wrist angular velocity and high-density surface EMG from the wrist muscles exhibiting tremor in 22 patients with ET or PD, as well as to the neural drive to the muscles, estimated from discharge patterns of multiple motor units from each muscle [19].…”

A holistic approach to assess the feasability of telehealth for Parkinson's disease management

“…This system is based on the decomposition of surface electromyogram (sEMG) into contributions of individual motor units allowing the decoding of neural drive to muscles [457]. Holobar et al (2012) introduced a signal-based metric for assessment of accuracy of motor unit identification from high-density sEMG. This metric, called pulseto-noise-ratio (PNR), correlated significantly with both sensitivity and false alarm rate in identification of motor units discharges.…”