Key result
Wavelet cross-correlation analysis of multiple EMG signals provided a local intermittency measure that distinguished low-frequency behavior in Parkinsonian patients from normal subjects.
Why the study?
Does wavelet cross-correlation analysis of EMG signals accurately classify Parkinsonian patients compared to normal subjects?
Does wavelet cross-correlation analysis of EMG signals accurately classify Parkinsonian patients compared to normal subjects?
Wavelet cross-correlation analysis of EMG signals provides a promising method to classify Parkinson's disease severity and distinguish patients from healthy controls.
May aid EMG-based Parkinson's distinction; hypothesis-generating and requires prospective validation before clinical adoption.
Using a wavelet analysis approach, it is possible to investigate better the transient and intermittent behavior of multiple electromyographic (EMG) signals during ballistic movements in Parkinsonian patients. In particular, a wavelet cross-correlation analysis on surface signals of two different shoulder muscles allows us to evidence the related unsteady and synchronization characteristics. With a suitable global parameter extracted from local wavelet power spectra, it is possible to accurately classify the subjects in terms of a reliable statistic and to study the temporal evolution of the Parkinson's disease level. Moreover, a local intermittency measure appears as a new promising index to distinguish the low-frequency behavior from normal subjects to Parkinsonian patients.
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Michele et al. (2003) studied Parkinson's disease. Wavelet cross-correlation analysis of EMG signals vs. Normal subjects was evaluated on Classification of subjects and temporal evolution of Parkinson's disease level. Wavelet cross-correlation analysis of multiple EMG signals provided a local intermittency measure that distinguished low-frequency behavior in Parkinsonian patients from normal subjects.
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