Cross-correlation time-frequency analysis for multiple EMG signals in Parkinson's disease: a wavelet approach.
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.
De Michele
Gennaro
G
Enel Research, Via Andrea Pisano 120, Pisa, Italy.
Sello
Stefano
S
Carboncini
Maria Chiara
MC
Rossi
Bruno
B
Strambi
Soo-Kyung
SK
D000328:Adult; D000368:Aged; D004576:Electromyography / Q000379:methods*; D006801:Humans; D008297:Male; D008875:Middle Aged; D009068:Movement; D018482:Muscle, Skeletal / Q000503:physiopathology*; D010300:Parkinson Disease / Q000175:diagnosis* / Q000503:physiopathology*; D004856:Postural Balance; D011237:Predictive Value of Tests; D012782:Shoulder / Q000503:physiopathology; D012815:Signal Processing, Computer-Assisted / Q000295:instrumentation*