Analysis of Tremor Activity of Antagonist Muscles in Essential Tremor and Parkinson’s Diseases

Ivanova, Ekaterina; Федин, П.А.; Брутян, А.Г.; Иванова-Смоленская, И.А.; Иллариошкин, С. Н.

doi:10.18821/1560-9545-2014-19-4-11-18

Cited by 5 publications

(5 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Historically, EMG analysis methods evolved from spectral analysis [ 1 , 2 , 3 , 4 , 5 , 6 , 7 ] and time-domain signal analysis methods such as morphological analysis [ 8 ], amplitude analysis [ 9 ], and autoregressive analysis [ 10 , 11 ] towards time–frequency domain analysis [ 12 , 13 , 14 , 15 , 16 ]. The state-of-the-art of EMG analysis methods is characterized by the active use of nonlinear data analysis methods [ 17 ], such as fractal analysis [ 18 ], phase analysis [ 19 ], recurrent quantification analysis [ 4 , 20 , 21 ], and the deep learning of neural networks [ 12 , 22 , 23 , 24 , 25 ]. According to the authors, the existing methods for analyzing EEG, EMG, and tremor signals, such as wavelet analysis [ 26 , 27 , 28 ], focus on local time–frequency changes in the signal and, therefore, do not reveal the generalized properties of the signal.…”

Section: Introductionmentioning

confidence: 99%

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

Sushkova

Morozov

Gabova

et al. 2021

Sensors

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

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

Sushkova

Morozov

Gabova

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Meta-analysis for PD risk factors found that the total RR was 1.70 [34], and there was a significant coupling between the intensity of tremor and the severity of hypertension [35]. ET patients had significantly reduced catecholamine metabolism and excretion [36], and some case reports indicated adverse effects of severe tremor in patients when treated with beta-blockers [37]. (2) Alteration cerebral stiffness due to hypertension may lead to decreased energy convergence of transcranial ultrasound.…”

Section: Discussionmentioning

confidence: 99%

Fibrinogen is the key factor associated with tremor relieve and recurrence in the ET patients subjected with unilateral MRgFUS thalamotomy

Lin,

Lu,

Zhang

et al. 2024

Preprint

View full text Add to dashboard Cite

Background There were variable differences and declines in efficacy of magnetic resonance guided focused ultrasound (MRgFUS) thalamotomy, but whether the clinical status of these patients was involved was still unknown. Methods We retrospectively reviewed the tremor symptoms and clinical variables (at hospitalization) of 59 essential tremor (ET) patients subjected with MRgFUS thalamotomy. Patients were categorized by the short-term tremor relieve, tremor recurrence within 12 months and long-term maintenance outcome. Multivariate logistic regression was used to screen independent factors and construct the nomogram. Additional harmaline-induced ET model in male ICR mice were employed, we evaluated the possible anti-tremor effects of fibrinogen knock-out. Results MRgFUS thalamotomy resulted in significant tremor relieve, multivariate analysis suggested fibrinogen (OR = 0.182, 95% CI = 0.042-0.796,) and monocyte count (OR = 0, 95% CI = 0-0.001) were independently associated with better short-term tremor relieve (>75%) at 1-month postoperatively. A total of nine ET patients (15.254%) experienced significant tremor recurrence. Admission systolic blood pressure (OR = 1.013, 95% CI = 1.010-1.062), hypertension comorbidity (OR = 2.163, 95% CI = 1.412-53.565) and fibrinogen (OR = 1.620, 95% CI = 1.047-24.376) independently contributed to tremor recurrence. There were 15 ET patients (25.424%) sustained long-term and effective tremor control. Only admission systolic blood pressure was the independent prognostic factor (OR = 0.880, 95% CI = 0.785-0.987) for this favorable outcome. The nomograms were established for better tremor relieve and tremor recurrence and have excellent performance as the AUCs were 0.829 and 0.853 (bootstrap repetition = 1000). Finally, FGB knock-out significantly suppressed harmaline-induced tremor in vivo (P < 0.001). Conclusion Fibrinogen and hypertension are key factors associated with tremor relieve and recurrence in the ET patients subjected with MRgFUS thalamotomy.

show abstract

“…Researchers have expressed different and sometimes directly opposite opinions on this issue. In particular, several publications indicate the possibility of the differential diagnosis of PD and ET by analyzing the phase difference in antagonist muscle EMG signals with high accuracy [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 ]. However, other authors have discussed about the inapplicability of this approach to the differential diagnosis of PD and ET because it cannot provide acceptable accuracy [ 13 , 14 , 15 , 16 , 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

Investigation of Phase Shifts Using AUC Diagrams: Application to Differential Diagnosis of Parkinson’s Disease and Essential Tremor

Sushkova

Morozov

Kershner

et al. 2023

Sensors

View full text Add to dashboard Cite

This study was motivated by the well-known problem of the differential diagnosis of Parkinson’s disease and essential tremor using the phase shift between the tremor signals in the antagonist muscles of patients. Different phase shifts are typical for different diseases; however, it remains unclear how this parameter can be used for clinical diagnosis. Neurophysiological papers have reported different estimations of the accuracy of this parameter, which varies from insufficient to 100%. To address this issue, we developed special types of area under the ROC curve (AUC) diagrams and used them to analyze the phase shift. Different phase estimations, including the Hilbert instantaneous phase and the cross-wavelet spectrum mean phase, were applied. The results of the investigation of the clinical data revealed several regularities with opposite directions in the phase shift of the electromyographic signals in patients with Parkinson’s disease and essential tremor. The detected regularities provide insights into the contradictory results reported in the literature. Moreover, the developed AUC diagrams show the potential for the investigation of neurodegenerative diseases related to the hyperkinetic movements of the extremities and the creation of high-accuracy methods of clinical diagnosis.

show abstract

Analysis of Tremor Activity of Antagonist Muscles in Essential Tremor and Parkinson’s Diseases

Cited by 5 publications

References 0 publications

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

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

Fibrinogen is the key factor associated with tremor relieve and recurrence in the ET patients subjected with unilateral MRgFUS thalamotomy

Investigation of Phase Shifts Using AUC Diagrams: Application to Differential Diagnosis of Parkinson’s Disease and Essential Tremor

Contact Info

Product

Resources

About