Visualisation of Parkinsonian, essential and physiological tremor planes in 3Dspace

The Decomposition Method of Surface Electromyographic Signals: A Novel Approach for Motor Unit Activity and Recruitment Description

This review aims to describe a novel method in the field of electromyography (EMG), established and improved upon in the last three decades that is able to observe specific parameters of muscle units (MUs). This concept is called the decomposition method, based on its ability to decompose a surface EMG signal to describe muscle activity on the level of individual muscle units in contrast to the level of the whole muscle, as is customary for regular surface electromyography. We provide a brief overview of its history, constituent parts regarding both hardware and software and possible applications. We also acknowledge the state of the research, regarding the background of the decomposition algorithm, the main software component responsible for identifying individual motor units and their parameters. As a result of the ability to describe the behavior of individual motor units during muscle contractions, key concepts in neuromuscular physiology have been put forward, pertaining to the hierarchy of MUs during their recruitment. Together with the recent application for cyclic contractions and gait, the decomposition method is beginning to open up wider possibilities of enquiry.

Imaging Tremor Quantification for Neurological Disease Diagnosis

In this paper, we introduce a simple method based on image analysis and deep learning that can be used in the objective assessment and measurement of tremors. A tremor is a neurological disorder that causes involuntary and rhythmic movements in a human body part or parts. There are many types of tremors, depending on their amplitude and frequency type. Appropriate treatment is only possible when there is an accurate diagnosis. Thus, a need exists for a technique to analyze tremors. In this paper, we propose a hybrid approach using imaging technology and machine learning techniques for quantification and extraction of the parameters associated with tremors. These extracted parameters are used to classify the tremor for subsequent identification of the disease. In particular, we focus on essential tremor and cerebellar disorders by monitoring the finger–nose–finger test. First of all, test results obtained from both patients and healthy individuals are analyzed using image processing techniques. Next, data were grouped in order to determine classes of typical responses. A machine learning method using a support vector machine is used to perform an unsupervised clustering. Experimental results showed the highest internal evaluation for distribution into three clusters, which could be used to differentiate the responses of healthy subjects, patients with essential tremor and patients with cerebellar disorders.