Investigation of Surface EMG and Acceleration Signals of Limbs’ Tremor in Parkinson’s Disease Patients Using the Method of Electrical Activity Analysis Based on Wave Trains

2018 ◽  
pp. 253-264 ◽  
Author(s):  
Olga S. Sushkova ◽  
Alexei A. Morozov ◽  
Alexandra V. Gabova ◽  
Alexei V. Karabanov
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Electrical Activity ◽  
Surface Emg ◽  
Activity Analysis ◽  
Wave Trains ◽  
Acceleration Signals

Surface EMG and acceleration signals in Parkinson’s disease: feature extraction and cluster analysis

2008 ◽  
Vol 46 (9) ◽  
pp. 849-858 ◽  
Author(s):  
Saara M. Rissanen ◽  
Markku Kankaanpää ◽  
Alexander Meigal ◽  
Mika P. Tarvainen ◽  
Juho Nuutinen ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Cluster Analysis ◽  
Parkinson's Disease ◽  
Feature Extraction ◽  
Surface Emg ◽  
And Cluster Analysis ◽  
Acceleration Signals

scholarly journals A Statistical Method for Exploratory Data Analysis Based on 2D and 3D Area under Curve Diagrams: Parkinson’s Disease Investigation

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4700
Author(s):  
Olga Sergeevna Sushkova ◽  
Alexei Alexandrovich Morozov ◽  
Alexandra Vasilievna Gabova ◽  
Alexei Vyacheslavovich Karabanov ◽  
Sergey Nikolaevich Illarioshkin
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Electrical Activity ◽  
Local Time ◽  
Wave Train ◽  
Time Frequency ◽  
Power Spectral ◽  
Wave Trains ◽  
Frequency Changes ◽  
2D And 3D

A statistical method for exploratory data analysis based on 2D and 3D area under curve (AUC) diagrams was developed. The method was designed to analyze electroencephalogram (EEG), electromyogram (EMG), and tremorogram data collected from patients with Parkinson’s disease. The idea of the method of wave train electrical activity analysis is that we consider the biomedical signal as a combination of the wave trains. The wave train is the increase in the power spectral density of the signal localized in time, frequency, and space. We detect the wave trains as the local maxima in the wavelet spectrograms. We do not consider wave trains as a special kind of signal. The wave train analysis method is different from standard signal analysis methods such as Fourier analysis and wavelet analysis in the following way. Existing methods for analyzing EEG, EMG, and tremor signals, such as wavelet analysis, focus on local time–frequency changes in the signal and therefore do not reveal the generalized properties of the signal. Other methods such as standard Fourier analysis ignore the local time–frequency changes in the characteristics of the signal and, consequently, lose a large amount of information that existed in the signal. The method of wave train electrical activity analysis resolves the contradiction between these two approaches because it addresses the generalized characteristics of the biomedical signal based on local time–frequency changes in the signal. We investigate the following wave train parameters: wave train central frequency, wave train maximal power spectral density, wave train duration in periods, and wave train bandwidth. We have developed special graphical diagrams, named AUC diagrams, to determine what wave trains are characteristic of neurodegenerative diseases. In this paper, we consider the following types of AUC diagrams: 2D and 3D diagrams. The technique of working with AUC diagrams is illustrated by examples of analysis of EMG in patients with Parkinson’s disease and healthy volunteers. It is demonstrated that new regularities useful for the high-accuracy diagnosis of Parkinson’s disease can be revealed using the method of analyzing the wave train electrical activity and AUC diagrams.

Comprehensive analysis of resting tremor based on acceleration signals of patients with Parkinson’s disease

2021 ◽  
pp. 1-13
Author(s):  
Sen Liu ◽  
Han Yuan ◽  
Jiali Liu ◽  
Hai Lin ◽  
Cuiwei Yang ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Rating Scale ◽  
Statistical Tests ◽  
Real Life ◽  
Feature Analysis ◽  
Support Vector ◽  
Monitoring Methods ◽  
Resting Tremor ◽  
Acceleration Signals

BACKGROUND: Resting tremor is an essential characteristic in patients suffering from Parkinson’s disease (PD). OBJECTIVE: Quantification and monitoring of tremor severity is clinically important to help achieve medication or rehabilitation guidance in daily monitoring. METHODS: Wrist-worn tri-axial accelerometers were utilized to record the long-term acceleration signals of PD patients with different tremor severities rated by Unified Parkinson’s Disease Rating Scale (UPDRS). Based on the extracted features, three kinds of classifiers were used to identify different tremor severities. Statistical tests were further designed for the feature analysis. RESULTS: The support vector machine (SVM) achieved the best performance with an overall accuracy of 94.84%. Additional feature analysis indicated the validity of the proposed feature combination and revealed the importance of different features in differentiating tremor severities. CONCLUSION: The present work obtains a high-accuracy classification in tremor severity, which is expected to play a crucial role in PD treatment and symptom monitoring in real life.

scholarly journals Analysis of EMG and Acceleration Signals for Quantifying the Effects of Deep Brain Stimulation in Parkinson’s Disease

2011 ◽  
Vol 58 (9) ◽  
pp. 2545-2553 ◽  
Author(s):  
S. M. Rissanen ◽  
M. Kankaanpaä ◽  
M. P. Tarvainen ◽  
V. Novak ◽  
P. Novak ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Deep Brain Stimulation ◽  
Brain Stimulation ◽  
Acceleration Signals ◽  
Deep Brain

scholarly journals Diagnosis of Parkinson’s Disease Type 1 and 2 on the Electrical Activity of Hand Muscle During Isometric Contraction With and Without Intellectual Activity

2019 ◽  
Vol 7 (1) ◽  
pp. 26-30
Author(s):  
Roya Sotoodeh ◽  
Fereidoun Nowshiravan Rahatabad ◽  
Nader Jafarnia Dabanloo
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Electrical Activity ◽  
Isometric Contraction ◽  
Type I ◽  
Intellectual Activity ◽  
Invasive Approach ◽  
Statistical Population ◽  
Shoulder Muscle

Background: Parkinson’s disease (PD) is the third most common neurodegenerative disease in the central nervous system. It is a degenerative and slowly progressive disease. PD patients have difficulty at the beginning of the movement, at the path of movement, and at the end of the movement. PD consists of 5 main stages. Methods: The purpose of this study was to diagnose PD type 1 and 2 on the electrical activity of shoulder muscle during isometric contraction with and without intellectual activity. In the same way, the electrical activity of the 2 right anterior deltoid muscles and the right upper trapezium by electromyographic apparatus in 4 conditions: (1) Resting, (2) Resting with intellectual activity, (3) performing an isometric movement in the hands, and (4) performing a movement Isometric in hand with an intellectual activity evaluated. The statistical population of the study consisted of 20 patients as experimental group (10 patients in the first stage and 10 patients in the second stage of PD) and ten healthy subjects as the control group as the statistical population. Shapiro-Wilk test was used to determine the normal distribution of data. Averages of variables were compared using the ANOVA test at a significant level of 5%. All statistical methods were performed using SPSS software. Results: The results of this study showed that the electrical activity of the shoulder belt muscles is significantly different during and after the isometric contraction with and without mental activity in PD type I and II. Conclusion: As a result, a non-invasive approach to assessing the electrical activity of shoulder muscle can be used to determine the first and second stages of PD. Research shows that PD is a chronic and progressive complication that most affects older people. Also, according to the results, Parkinson’s patients have weaker performance in cognitive functions than healthy people.

Surface EMG is able to predict L-DOPA effect on gait in patients with Parkinson’s disease

2009 ◽  
Vol 29 ◽  
pp. e23-e24
Author(s):  
P. Caliandro ◽  
I. Minciotti ◽  
G. Vergili ◽  
F. Fusco ◽  
C. Pazzaglia ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Surface Emg

Analysis of surface EMG signal morphology in Parkinson's disease

2007 ◽  
Vol 28 (12) ◽  
pp. 1507-1521 ◽  
Author(s):  
Saara Rissanen ◽  
Markku Kankaanpää ◽  
Mika P Tarvainen ◽  
Juho Nuutinen ◽  
Ina M Tarkka ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Surface Emg ◽  
Emg Signal ◽  
Surface Emg Signal
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