New Perspectives for Computer-Aided Discrimination of Parkinson’s Disease and Essential Tremor

Abstract

Pathological tremor is a common but highly complex movement disorder, affecting ∼5% of population older than 65 years. Different methodologies have been proposed for its quantification. Nevertheless, the discrimination between Parkinson's disease tremor and essential tremor remains a daunting clinical challenge, greatly impacting patient treatment and basic research. Here, we propose and compare several movement-based and electromyography-based tremor quantification metrics. For the latter, we identified individual motor unit discharge patterns from high-density surface electromyograms and characterized the neural drive to a single muscle and how it relates to other affected muscles in 27 Parkinson's disease and 27 essential tremor patients. We also computed several metrics from the literature. The most discriminative metrics were the symmetry of the neural drive to muscles, motor unit synchronization, and the mean log power of the tremor harmonics in movement recordings. Noteworthily, the first two most discriminative metrics were proposed in this study. We then used decision tree modelling to find the most discriminative combinations of individual metrics, which increased the accuracy of tremor type discrimination to 94%. In summary, the proposed neural drive-based metrics were the most accurate at discriminating and characterizing the two most common pathological tremor types.

A Novel Posture for Better Differentiation Between Parkinson's Tremor and Essential Tremor

“…Ai et al ( 2011 ) applied the empirical mode decomposition in separating ET and PD and obtained some remarkable results (Ai et al, 2011 ). In recent years, the approach of data mining based on a huge amount of data (Palmes et al, 2010 ) or of features (Povalej Bržan et al, 2017 ) has been used to investigate this issue.…”

A deep explainable artificial intelligent framework for neurological disorders discrimination

“…It also offers a novel machine intelligence pipeline which can be interpreted from the clinical point of view. Considering the predecessors of NeurDNet with the highest classification accuracies (before the invention of NeurDNET in this paper), i.e., References 15,49,[52][53][54]63 , it is understood that NeurDNet not only excels the classification accuracy of the research that is based on accelerometer data but also outperforms the one based on Electromyogram (EMG) signals recorded from a tremorous hand (which was supposed to have richer neurophysiological content in the signal). To be more specific, here we provide an itemized comparison with recent research publications, leading the state-of-the-art classification accuracy for discriminating PD from ET.…”

“…Ghassemi et al 31 ET/PD classification Electromyogram and accelerometer data, [13 PD,11 ET] for training and testing Classification of Wavelet features with Support Vector Machines (SVM) Accuracy = 83% Brzan et al 49 ET/PD Classificclassificationation Electromyogram data [27 PD,27 ET] for training and testing A set of statistical and physiological features classified with decision tree Accuracy = 94% DiBiase et al 15 ET/PD classification Accelerometer data, [16 PD,20 ET] for training and [55] for testing Analysis in spectral domain Accuracy = 92%, Sensitivity = 95%, Specificity = 95% Barrantes et al 50 ET/PD/Healthy classification Accelerometer data, [17 PD,16 ET, 12 healthy, 7 unknown] Spectral analysis of the signals Accuracy=84.38%…”

Identification of the laterality of motor unit behavior in female patients with parkinson's disease using high‐density surface electromyography

“…A better understanding of the asymmetries in whole muscle activation in the lower extremities may lead to the development of novel therapies. However, to the best of our knowledge, few reports have examined the symmetry of MU behavior (e.g., neural drive to the skeletal muscles) in patients with PD using the high‐density surface electromyography (HD‐SEMG) method (Holobar et al., 2018; Povalej Bržan et al., 2017). In particular, no report has identified the laterality of MU activation properties of lower extremity muscles in patients with PD and the relationship to clinical measures of function.…”