The differential diagnosis between Ischemic Heart Disease (IHD) and Dilated Cardiomyopathy (DCM), particularly in the early stages of the diseases, can often be difficult. Left ventricular ejection fraction (LVEF) and heart rate variability (HRV) analysis are shown to be helpful tools for diagnosing several cardiac diseases. There is a growing interest in application of machine learning techniques to guide the diagnosis. However, often black-box machine learning models create dissatisfaction among clinicians due to the lack of a model interpretability. The aim of our study was to compare the classification performance of interpretable and clinically plausible models applied for early differential diagnosis between DCM and IHD (NYHA ≤ 1) based on LVEF and HRV features. The study encompassed 196 IHD and 117 DCM subjects. The models were produced by classification tree, logistic regression and naïve Bayes algorithms considering the set of selected HRV and LVEF features, chosen with the information gain method. The results showed that the most informative features for classification between IHD and DCM were LVEF, LF, NN50, pNN50, and meanRR. The naive Bayes model with classification accuracy of 73.5% outperformed classification tree and logistic regression models with 67.4% and 67.1% accuracies, respectively. We also demonstrated that the produced models together with nomograms allow probabilistic interpretation of the classification output between IHD and DCM, which is an important factor to guide the clinical decision making in differential diagnosis.

Dilated cardiomyopathy (DCM) is a heart muscle disease characterized by left ventricular (LV) or biventricular dilatation and systolic dysfunction in the absence of either pressure or volume overload or coronary artery disease sufficient to explain the dysfunction. The use of heart rate variability (HRV) analysis as well as of some machine learning algorithms, proved to be a valuable support in the diagnosis of cardiovascular disease. However, till now, only single beats or electrocardiogram segments of subjects affected by DCM were identified using machine learning techniques applied to HRV parameters. In this study, we used linear and non-linear HRV parameters and some clinical parameters (age, sex and left ventricular ejection fraction) evaluated on a large cohort of 972 subjects to early identify subjects suffered from DCM and to find which features could be selected as predictors for a correct diagnosis. By using principal component analysis and stepwise regression, we reduced the original parameters used as inputs for a series of classification and regression trees (CART). The highest accuracy of 97% and Area Under the Curve (AUC) of 95% were achieved using the ratio between low frequency and high frequency (LF/HF), sex and left ventricular ejection fraction (LVEF) parameters as inputs of the classifier.

Shortages and poor diversification of generating capacity, high environmental impacts, and low qualities of supply and affordability are common characteristics of the electricity sector in most developing countries (DevCs). Recent cost reductions in the telecommunication sector and some generation technologies, especially renewable-based, with improved economic feasibility have paved the way for new electric infrastructures. This is especially true for emerging economies, as locally dispatched hybrid microgrids introduce flexibility and show the promise of technical and economic advantages. The design and integration of microgrids require detailed assessments to ensure the effective deployment of capital to minimize the risk of stranded assets and capital waste. Microgrids can be considered an affordable option for a rapid response to the electrification challenge, recognizing that, in the longer horizon, a sharing of resources with the bulk electrical power system will remain, technically and economically, advantageous. This article presents a comprehensive review of the approaches commonly adopted for microgrid electrification. A “real-life” study case is reported to highlight the operational challenges of a stand-alone microgrid versus a grid-connected system. The need for an improved regulatory framework is presented as the cornerstone problem to be solved to allow an effective integration of microgrids in the national grid.

Energy demand management for residential users is a promising research area within the Smart Grid revolution. The whole energy generation and distribution system performance can indeed be improved by optimizing the house energy management while still meeting the energy needs of customers. In this paper we propose Non-Cooperative models for the optimal planning of energy loads of a single house, with the final goal of minimizing the energy bill. A Pre-emptive variant of the model is also presented where some appliances activities can be interrupted in some phases. Finally, Cooperative models are defined for jointly managing the energy consumption of a group of users in order to easily decrease the aggregated maximum power demand, thus reducing the peak absorption. Numerical results, obtained applying models on realistic data, are presented and discussed.