Statistical Analysis and Kinematic Assessment of Upper Limb Reaching Task in Parkinson’s Disease

The impact of neurodegenerative disorders is twofold; they affect both quality of life and healthcare expenditure. In the case of Parkinson’s disease, several strategies have been attempted to support the pharmacological treatment with rehabilitation protocols aimed at restoring motor function. In this scenario, the study of upper limb control mechanisms is particularly relevant due to the complexity of the joints involved in the movement of the arm. For these reasons, it is difficult to define proper indicators of the rehabilitation outcome. In this work, we propose a methodology to analyze and extract an ensemble of kinematic parameters from signals acquired during a complex upper limb reaching task. The methodology is tested in both healthy subjects and Parkinson’s disease patients (N = 12), and a statistical analysis is carried out to establish the value of the extracted kinematic features in distinguishing between the two groups under study. The parameters with the greatest number of significances across the submovements are duration, mean velocity, maximum velocity, maximum acceleration, and smoothness. Results allowed the identification of a subset of significant kinematic parameters that could serve as a proof-of-concept for a future definition of potential indicators of the rehabilitation outcome in Parkinson’s disease

Heart Rate Variability Dynamics for the Prognosis of Cardiovascular Risk

Statistical, spectral, multi-resolution and non-linear methods were applied to heart rate variability (HRV) series linked with classification schemes for the prognosis of cardiovascular risk. A total of 90 HRV records were analyzed: 45 from healthy subjects and 45 from cardiovascular risk patients. A total of 52 features from all the analysis methods were evaluated using standard two-sample Kolmogorov-Smirnov test (KS-test). The results of the statistical procedure provided input to multi-layer perceptron (MLP) neural networks, radial basis function (RBF) neural networks and support vector machines (SVM) for data classification. These schemes showed high performances with both training and test sets and many combinations of features (with a maximum accuracy of 96.67%). Additionally, there was a strong consideration for breathing frequency as a relevant feature in the HRV analysis

Heart Rate Variability Dynamics for the Prognosis of Cardiovascular Risk

Statistical, spectral, multi-resolution and non-linear methods were applied to heart rate variability (HRV) series linked with classification schemes for the prognosis of cardiovascular risk. A total of 90 HRV records were analyzed: 45 from healthy subjects and 45 from cardiovascular risk patients. A total of 52 features from all the analysis methods were evaluated using standard two-sample Kolmogorov-Smirnov test (KS-test). The results of the statistical procedure provided input to multi-layer perceptron (MLP) neural networks, radial basis function (RBF) neural networks and support vector machines (SVM) for data classification. These schemes showed high performances with both training and test sets and many combinations of features (with a maximum accuracy of 96.67%). Additionally, there was a strong consideration for breathing frequency as a relevant feature in the HRV analysis

EXPERIMENTAL ANALYSIS OF WET ELECTROSTATIC SCRUBBING FOR THE ABATEMENT OF SUBMICRON PARTICULATE MATTER

Experimental results on wet electrostatic scrubbing of submicronic particles, produced by a model combustion process, are reported. This process should be used to develop new technologies to reduce environmental footprint of industry and transportation, but also for the cleaning of indoor air or virus abatement, and may have application to the recovery of precious by-products in particulate forms (e.g. entrained catalysts in process gas streams). Experiments were carried out in a lab-scale facility that was purposely designed and optimized to perform tests with a train of falling droplets of identical size and charge, which scrubs a closed volume of gas containing pre-charged submicronic particles at opposite polarity. The experimental campaign aimed to assess the effects of droplet and particle charge and number of scrubbing droplets on the efficiency of wet electrostatic scrubbing. Results were interpreted with a model derived from the theory on wet scavenging of aerosol particles in atmosphere showing a good matching between experimental and theoretical data. Experimental results and modeling demonstrated that the process was dominated by electrostatic interactions among droplets and particles. Particle removal efficiency, in the submicronic range, was favored by higher particles and droplet charges, while image charge seem to have a negligible effect on particle abatement when only the droplets are charged. Results allowed assessment of guidelines for the design of wet electrostatics scrubbers for particle abatement, which are discussed in the paper with reference to a specific case study

Feasibility of Machine Learning applied to Poincaré Plot Analysis on Patients with CHF

As an alternative to the traditional methods of analysis in the time and frequency domains regarding heart rate variability, new interest has been concentrated in using a non-linear analysis technique of the beat-beat time series, known as the Poincaré Plot Analysis. The parameters provided by the analysis can be used as input for machine learning algorithms in order to distinguish patients in three classes of congestive heart failure, according to the New York Heart Association. Tree-based algorithms for classification and synthetic minority oversampling technique (SMOTE) for balancing the dataset with artificial data were implemented in Knime analytics platform, reaching an overall accuracy between 75% and 80%, specificity and sensitivity greater than 90% in some classes and F-measures ranging from 68% to 92%. Further investigations could be pursued with bigger datasets and avoiding the use of artificial data to balance the classes