102,173 research outputs found
Agreement between Opal and G-Walk Wearable Inertial Systems in Gait Analysis on Normal and Pathological Subjects
No full textDespite the growing use of different wearable inertial systems, increasingly diffused in clinical practice, there is still a lack of knowledge about the agreement between systems based also on different sensor configuration. Aim of the study has been the investigation of the agreement between Opal and G-Walk wearable inertial systems in gait analysis on normal and post stroke subjects. Although both systems are able to describe significant gait differences in the two populations, study results suggest that gait analysis evaluations carried out by different inertial systems does not give completely overlapping estimation about the different parameters and that this must be taken in correct account especially comparing results of clinical trials obtained by different systems and sensor's placements
Agreement between Opal and G-Walk Wearable Inertial Systems in Gait Analysis on Normal and Pathological Subjects
No full textDespite the growing use of different wearable inertial systems, increasingly diffused in clinical practice, there is still a lack of knowledge about the agreement between systems based also on different sensor configuration. Aim of the study has been the investigation of the agreement between Opal and G-Walk wearable inertial systems in gait analysis on normal and post stroke subjects. Although both systems are able to describe significant gait differences in the two populations, study results suggest that gait analysis evaluations carried out by different inertial systems does not give completely overlapping estimation about the different parameters and that this must be taken in correct account especially comparing results of clinical trials obtained by different systems and sensor's placements
Relationship between fractal dimension and power-law exponent of heart rate variability in normal and heart failure subjects
No full textAmong the plethora of indices that can describe the fractal-like behaviour of heart rate variability (HRV), the fractal dimension (FD) and the power-law exponent (β) have gained wide acceptance. Since HRV is generally modelled with fractional Brownian motion (fBm), the linear scaling relationship between β and FD, valid for fBm, is often applied to HRV series to derive one index from the other. In this paper the relationship between β and FD is calculated in normal (NR) and heart failure (HF) HRV series. Results revealed that a linear dependence between β and FD can be found only when the slope of the spectral density is calculated over the whole spectrum instead of considering more widespread very low frequency ranges. Moreover, the relationship is slightly different from that characterizing fBm and is not unique for the two categories of subjects. The common practice of estimating β from FD for HRV applying the theoretical relationship should be reconsidered
Valutazione delle dimensioni del bulbo aortico, dell'atrio e del ventricolo sinistro mediante ecocardiografia M-mode
No full textEvaluation of ventricular septal defect shunt size by color flow Doppler echocardiography.
No full textFeasibility of Machine Learning in Predicting Features Related to Congenital Nystagmus
No full textCongenital nystagmus is an ocular-motor disease affecting people’s visual acuity since their first years of life. Electrooculography is used to perform eye tracking in these patients, giving the possibility to extract a wide variety of parameters. The relationships among all these variables were analysed in the past and the aim of this paper is to perform a new analysis employing more recent techniques, those of machine learning. The electrooculography of 20 patients was recorded, signals were pre-processed, and some parameters were extracted through a custom-made software. Knime analytics platform was chosen in order to build predictive models using Random Forests and Logistic Regression Tree algorithms and some evaluation metrics were computed. The visual acuity and the variability of eye positioning were predicted employing five and six variables, respectively. In terms of coefficient of determination, visual acuity had values over 0.72 and variability of eye positioning over 0.70. Compared to the results obtained without machine learning algorithms during the past years, these values become more valuable. In conclusion, this approach showed its feasibility in detecting relationships among variables related to congenital nystagmus; it could be tested in order to find new and stronger relationships among these variables and be of support for clinicians
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