IntroductionA novel system that combines a compact mobile instrument and Internet communications is presented in this paper for remote evaluation of tremors. The system presents a high potential application in Parkinson's disease and connects to the Internet through a TCP/IP protocol. Tremor transduction is carried out by accelerometers, and the data processing, presentation and storage were obtained by a virtual instrument. The system supplies the peak frequency (fp), the amplitude (Afp) and power in this frequency (Pfp), the total power (Ptot), and the power in low (1-4 Hz) and high (4-7 Hz) frequencies (Plf and Phf, respectively).MethodsThe ability of the proposed system to detect abnormal tremors was initially demonstrated by a fatigue study in normal subjects. In close agreement with physiological fundamentals, the presence of fatigue increased fp, Afp, Pfp and Pt (p < 0.05), while the removal of fatigue reduced all the mentioned parameters (p < 0.05). The system was also evaluated in a preliminary in vivo test in parkinsonian patients. Afp, Pfp, Ptot, Plf and Phf were the most accurate parameters in the detection of the adverse effects of this disease (Se = 100%, Sp = 100%), followed by fp (Se = 100%, Sp = 80%). Tests for Internet transmission that realistically simulated clinical conditions revealed adequate acquisition and analysis of tremor signals and also revealed that the user could adequately receive medical recommendations.ConclusionsThe proposed system can be used in a wide spectrum of telemedicine scenarios, enabling the home evaluation of tremor occurrence under specific medical treatments and contributing to reduce the costs of the assistance offered to these patients.

Tremor in Parkinson's disease (PD) is a fundamental feature used in the determination of disease onset and progression. Traditionally, tremor has been evaluated using frequency domain analysis. However, in many cases, this analysis did not show significant differences comparing healthy elders and individuals with PD. Given its complex nature, recently the interest in nonlinear dynamical analysis for better understanding of tremor has grown. In this paper, we examine the effect of PD on the complexity of the tremor time series of PD patients using the approximate entropy method (ApEn). Tremor was also evaluated in the frequency domain. This study involved 11 healthy and 11 PD patients. The peak frequency was similar in both groups, while the amplitude and power in the peak frequency and the total power were significantly higher in PD patients (p < 0.0001). A significant reduction (p < 0.001) in ApEn was observed in PD. ROC analysis showed that ApEn differentiated physiological tremor from tremor in PD patients with high accuracy. These results are in close agreement with pathophysiological fundamentals, and provide evidence that in PD patients the tremor pattern becomes less complex. Furthermore, our findings also suggest that ApEn has a high clinical potential in assessing PD patients.