1,721,046 research outputs found
Feasibility 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
Valutazione della riproducibilità a breve termine di indici della variabilità della frequenza cardiaca in pazienti con scompenso cardiaco
No full textFractal behaviour of pathological heart rate variability dynamics
No full textHeart rate variability analysis (HRV) is a well recognized tool in the autonomic control assessment. It has been suggested that nonlinear analysis of HRV might provide more valuable information than traditional linear methods. Several non linear fractal techniques recently gained wide interest: that based on indirect fractal dimension (FD) estimation from the 1/f spectral power relationship, and that based on a direct FD estimation from HRV time sequences. Aim of the study was to assess whether FD discriminates pathological HRV dynamics, comparing results with normal subjects and traditional linear indexes. We studied 7 groups of 10 ECG 24h-Holter recordings in normal and different pathologies: obstructive pulmonary disease, stroke, hypertension, post myocardial infarction, heart failure, heart transplanted. HRV was assessed by spectral power in very low, low and high frequency bands and standard deviation between normal beats. FD was estimated directly from the HRV sequences by Higuchi method (HM) and from the 1/f slope of spectral power relationship (beta). Results showed differences in the autonomic control impairments better described by FD than by traditional linear methods. Although HM and beta tried to measure the same FD property, the latter seemed to be rather insensitive to changes in autonomic control. These preliminary results clearly suggest that FD, estimated by HM, contains relevant information related to different HRV pathological dynamics. © 2009 WIT Press
Fractal analysis contains relevant information related to heart rate variability dynamics of normal and pathological subject
No full text
A telemedicine home care based activity monitor device
No full textA minimally invasive low-cost actigraph, with simple on-line movements features extraction, but with performance enhanced by off-line post-processing, is proposed. Despite its cost significantly lower, the proposed actigraph has performance similar to commercially available solutions. Moreover, its open downloading system and wireless integration capabilities make it apposite for telemedicine home care settings. The proposed device has been characterized experimentally by comparing its performance with an emerging gold standard in clinical monitoring physical activity assessment. © 2011 IEEE
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