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

• Published in: Medical engineering & physics Vol. 25; no. 5; pp. 361 - 369
• Main Authors: De Michele, Gennaro, Sello, Stefano, Carboncini, Maria Chiara, Rossi, Bruno, Strambi, Soo-Kyung
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.06.2003; Elsevier Science
• Subjects: Adult; Aged; Biological and medical sciences; Electromyography; Electromyography - methods; Humans; Male; Medical sciences; Middle Aged; Movement; Muscle, Skeletal - physiopathology; Parkinson Disease - diagnosis; Parkinson Disease - physiopathology; Parkinson’s disease; Postural Balance; Predictive Value of Tests; Shoulder - physiopathology; Signal Processing, Computer-Assisted - instrumentation; Time-series analysis; Wavelets; Wavelets; Electromyography; Time-series analysis; Parkinson’s disease; Parkinson's disease
• ISSN: 1350-4533; 1873-4030
• DOI: 10.1016/S1350-4533(03)00034-1

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.