A Phase Space Box-counting based Method for Arrhythmia Prediction from Electrocardiogram Time Series

Arrhythmia is one kind of cardiovascular diseases that give rise to the number of deaths and potentially yields immedicable danger. Arrhythmia is a life threatening condition originating from disorganized propagation of electrical signals in heart resulting in desynchronization among different chambers of the heart. Fundamentally, the synchronization process means that the phase relationship of electrical activities between the chambers remains coherent, maintaining a constant phase difference over time. If desynchronization occurs due to arrhythmia, the coherent phase relationship breaks down resulting in chaotic rhythm affecting the regular pumping mechanism of heart. This phenomenon was explored by using the phase space reconstruction technique which is a standard analysis technique of time series data generated from nonlinear dynamical system. In this project a novel index is presented for predicting the onset of ventricular arrhythmias. Analysis of continuously captured long-term ECG data recordings was conducted up to the onset of arrhythmia by the phase space reconstruction method, obtaining 2-dimensional images, analysed by the box counting method. The method was tested using the ECG data set of three different kinds including normal (NR), Ventricular Tachycardia (VT), Ventricular Fibrillation (VF), extracted from the Physionet ECG database. Statistical measures like mean (μ), standard deviation (σ) and coefficient of variation (σ/μ) for the box-counting in phase space diagrams are derived for a sliding window of 10 beats of ECG signal. From the results of these statistical analyses, a threshold was derived as an upper bound of Coefficient of Variation (CV) for box-counting of ECG phase portraits which is capable of reliably predicting the impeding arrhythmia long before its actual occurrence. As future work of research, it was planned to validate this prediction tool over a wider population of patients affected by different kind of arrhythmia, like atrial fibrillation, bundle and brunch block, and set different thresholds for them, in order to confirm its clinical applicability.

Translating food on the Internet: from American TV series dubbed for the Italian audience to subtitles on YouTube.

The aim of this dissertation is to analyze the adaptation of food realia and food-related terms from English into Italian in the world of screen translation. Food has always been at the heart of every human being’s life and is an essential lens of analysis when trying to understand culture. With the awareness of what has been done in the past in American TV series dubbed into Italian and looking at how translations have been changing in the last decade, my thesis demonstrates how strategies are more inclined to an overt approach, which leaves the audiovisual text deeply tied to its source language and culture. The work will also include a practical subtitling exercise of three episodes of the currently most famous YouTube cooking show: Nerdy Nummies by Rosanna Pansino. The fact that the show is published weekly on a YouTube channel gives me the opportunity to comment on the evolution of media in the third millennium and how this implies an improvement as far as the knowledge, the skills and the possibilities for audiovisual translators are concerned.

Time Series Database: progettazione di un’applicazione sw basata su piattaforma InfluxDB

La tesi tratta una panoramica generale sui Time Series database e relativi gestori. Successivamente l'attenzione è focalizzata sul DBMS InfluxDB. Infine viene mostrato un progetto che implementa InfluxDB