ABSTRACT
Patients who have mild to severe chronic obstructive pulmonary disease may obtain improvement in dyspnea, exercise capacity, and health-related quality of life as a result of exercise training. The type and intensity of training is of key importance in determining outcomes. High-intensity aerobic training leads to physiologic gains in aerobic fitness. Nevertheless, extreme breathlessness or peripheral muscle fatigue may prevent some patients from performing high-intensity exercise; therefore, new tools are needed to improve the effectiveness of pulmonary rehabilitation.
Subject(s)
Biofeedback, Psychology/methods , Exercise Therapy/methods , Oxygen Inhalation Therapy/methods , Pulmonary Disease, Chronic Obstructive/rehabilitation , Humans , PrognosisABSTRACT
The improvements in the motor ability in patients with Parkinson's disease due to antiparkinsonian medication is well-known and widely documented. Recent results, based both on kinematic parameters and standard electromyographic (EMG) signal analysis, clearly indicated that the medication reduced, as expected, the clinical signs of Parkinson's disease, but did not restore agonist burst duration modulation with distance in elbow flexion movements. The main aim of the present work is to shed more light on this medication effect using a wavelet analysis approach on multiple EMG signals recorded both on shoulder and elbow muscles in ballistic or rapid movements. The wavelet cross-correlation information allows us to evidence some important quantitative features of the EMG signals due to medication.
Subject(s)
Algorithms , Antiparkinson Agents/therapeutic use , Diagnosis, Computer-Assisted/methods , Electromyography/methods , Muscle Contraction , Parkinson Disease/diagnosis , Parkinson Disease/drug therapy , Aged , Arm/physiopathology , Female , Humans , Male , Middle Aged , Movement , Muscle, Skeletal/physiopathology , Parkinson Disease/physiopathology , Recovery of Function/physiology , Signal Processing, Computer-Assisted , Treatment OutcomeABSTRACT
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.
