Cross-correlation time-frequency analysis for multiple EMG signals in Parkinson’s disease: a wavelet approach

Michele, G. De; Sello, Stefano; Carboncini, Maria Chiara; Rossi, Bruno; Strambi, Soo-Kyung

doi:10.1016/s1350-4533(03)00034-1

Cited by 54 publications

(33 citation statements)

References 15 publications

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“…The methods used previously in the research of EMG signals in PD can be divided into four categories: (1) spectral based methods [3,5,15,19], (2) analysis of EMG burst characteristics during flexion and extension movements [6,9,12,13], (3) morphology-based methods [11], and (4) nonlinear methods [5]. In this study, we did not examine flexion and extension movements but methods from the other three categories were applied for EMG feature extraction.…”

Section: Discussionmentioning

confidence: 99%

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

Rissanen

Kankaanpää

Meigal

et al. 2008

Med Biol Eng Comput

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We present an advanced method for feature extraction and cluster analysis of surface electromyograms (EMG) and acceleration signals in Parkinson's disease (PD). In the method, 12 different EMG and acceleration signal features are extracted and used to form high-dimensional feature vectors. The dimensionality of these vectors is then reduced by using the principal component approach. Finally, the cluster analysis of feature vectors is performed in a low-dimensional eigenspace. The method was tested with EMG and acceleration data of 42 patients with PD and 59 healthy controls. The obtained discrimination between patients and controls was promising. According to clustering results, one cluster contained 90% of the healthy controls and two other clusters 76% of the patients. Seven patients with severe motor dysfunctions were distinguished in one of the patient clusters. In the future, the clinical value of the method should be further evaluated.

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Section: Discussionmentioning

confidence: 99%

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

Rissanen

Kankaanpää

Meigal

et al. 2008

Med Biol Eng Comput

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“…However, instead of the commonly used frequency transform of sEMG, we resorted to time-scale representation [12,26,27,35,36]. Although it has already been applied to various electrophysiological signals for diagnosis of different pathologies, such as Parkinson's disease [5], cerebral palsy [21] and lower back pain [28], it has never been combined with the entropy measures to investigate the amplitude distributions at different dyadic scales.…”

Section: Introductionmentioning

confidence: 99%

Multiscale entropy-based approach to automated surface EMG classification of neuromuscular disorders

Istenič

Kaplanis

Pattichis

et al. 2010

Med Biol Eng Comput

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We introduce a novel method for an automatic classification of subjects to those with or without neuromuscular disorders. This method is based on multiscale entropy of recorded surface electromyograms (sEMGs) and support vector classification. The method was evaluated on a single-channel experimental sEMGs recorded from biceps brachii muscle of nine healthy subjects, nine subjects with muscular and nine subjects with neuronal disorders, at 10%, 30%, 50%, 70% and 100% of maximal voluntary contraction force. Leave-one-out cross-validation was performed, deploying binary (healthy/patient) and three-class classification (healthy/myopathic/neuropathic). In the case of binary classification, subjects were distinguished with 81.5% accuracy (77.8% sensitivity at 83.3% specificity). At three-class classification, the accuracy decreased to 70.4% (myopathies were recognized with a sensitivity of 55.6% at specificity 88.9%, neuropathies with a sensitivity of 66.7% at specificity 83.3%). The proposed method is suitable for fast and non-invasive discrimination of healthy and neuromuscular patient groups, but it fails to recognize the type of pathology.

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“…The UPDRS -motor examination was performed for each patient in each medication condition. The EMG and acceleration signals of one PD patient in each medication condition are presented in Figure 235 Feature 10. It is observed that the number of recurring EMG bursts and the amplitude of tremor decrease with medication and start to increase three hours after taking the medication.…”

Section: Emg and Acceleration Measurements For Quantifying Effects Ofmentioning

confidence: 98%

“…While in Fourier approach the basis functions in the spectral decomposition are global functions, in wavelet approach [1] the functions are local. Therefore, the wavelet-based methods can be more effective than the Fourier-based method in detecting time varying features in the spectrum [10]. The basic idea in the wavelet transform is to decompose the signal into a set of basis functions, which are obtained by scaling and shifting the wavelet function ψ(t).…”

Section: Spectral-based Parametersmentioning

confidence: 99%

See 1 more Smart Citation

Feature Extraction Methods for Studying Surface Electromyography and Kinematic Measurements in Parkinson’s Disease

Rissanen¹,

Kankaanpää²,

Karjalainen³

2012

Computational Intelligence in Electromyography Analysis - A Perspective on Current Applications and Future Challenges

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Cross-correlation time-frequency analysis for multiple EMG signals in Parkinson’s disease: a wavelet approach

Cited by 54 publications

References 15 publications

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

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

Multiscale entropy-based approach to automated surface EMG classification of neuromuscular disorders

Feature Extraction Methods for Studying Surface Electromyography and Kinematic Measurements in Parkinson’s Disease

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