Motor Nerve Conduction Block Estimation in Demyelinating Neuropathies by Deconvolution

Abstract

A deconvolution method is proposed for conduction block (CB) estimation based on two compound muscle action potentials (CMAPs) elicited by stimulating a nerve proximal and distal to the region in which the block is suspected. It estimates the time delay distributions by CMAPs deconvolution, from which CB is computed. The slow afterwave (SAW) is included to describe the motor unit potential, as it gives an important contribution in case of the large temporal dispersion (TD) often found in patients. The method is tested on experimental signals obtained from both healthy subjects and pathological patients, with either Chronic Inflammatory Demyelinating Polyneuropathy (CIDP) or Multifocal Motor Neuropathy (MMN). The new technique outperforms the clinical methods (based on amplitude and area of CMAPs) and a previous state-of-the-art deconvolution approach. It compensates phase cancellations, allowing to discriminate among CB and TD: estimated by the methods of amplitude, area and deconvolution, CB showed a correlation with TD equal to 39.3%, 29.5% and 8.2%, respectively. Moreover, a significant decrease of percentage reconstruction errors of the CMAPs with respect to the previous deconvolution approach is obtained (from a mean/median of 19.1%/16.7% to 11.7%/11.2%). Therefore, the new method is able to discriminate between CB and TD (overcoming the important limitation of clinical approaches) and can approximate patients’ CMAPs better than the previous deconvolution algorithm. Then, it appears to be promising for the diagnosis of demyelinating polyneuropathies, to be further tested in the future in a prospective clinical trial.

Investigation of Motor Units Activity: Comparison of Single Channel Surface EMG Deconvolution and Blind Source Separation of Multichannel Data

“…where α is the regularization parameter. The problem was discretized writing convolution as the multiplication of the unknown sampled cumulative firing with a matrix as in [40] [49] [50] AX ≈ b (7) where A is a lower triangular Toeplitz matrix collecting the samples of the kernel, b = {b i } = {s(t i )} is the vector of recorded data samples and X the unknown time distribution of firings.…”

EMG und Neurografie – ein Update

“…Moderne Simulationen (▶ Abb. 5), die dieser Tatsache Rechnung tragen, zeigen, dass temporale Dispersion weit größere Amplitudenunterschiede als 50 % verursachen kann [29,30]. Dies gilt übrigens auch für Flächenunterschiede.…”

“…Der Vergleich der Form der Reizantworten ist zwar ohne Computer nicht quantitativ möglich, ist aber aufgrund theoretischer Erwägungen besonders empfindlich für temporale Dispersion. Die Überlegung hat schon zu praktisch brauchbaren Ergebnissen beim Nachweis temporaler Dispersion geführt und ist Gegenstand aktueller weiterer Untersuchungen 26 29 30 .…”