A Review of Atrial Fibrillation Detection Methods as a Service

Atrial Fibrillation (AF) is a common heart arrhythmia that often goes undetected, and even if it is detected, managing the condition may be challenging. In this paper, we review how the RR interval and Electrocardiogram (ECG) signals, incorporated into a monitoring system, can be useful to track AF events. Were such an automated system to be implemented, it could be used to help manage AF and thereby reduce patient morbidity and mortality. The main impetus behind the idea of developing a service is that a greater data volume analyzed can lead to better patient outcomes. Based on the literature review, which we present herein, we introduce the methods that can be used to detect AF efficiently and automatically via the RR interval and ECG signals. A cardiovascular disease monitoring service that incorporates one or multiple of these detection methods could extend event observation to all times, and could therefore become useful to establish any AF occurrence. The development of an automated and efficient method that monitors AF in real time would likely become a key component for meeting public health goals regarding the reduction of fatalities caused by the disease. Yet, at present, significant technological and regulatory obstacles remain, which prevent the development of any proposed system. Establishment of the scientific foundation for monitoring is important to provide effective service to patients and healthcare professionals

Site Characterization Index for Continuous Power Quality Monitoring Based on Higher-order Statistics

The high penetration of distributed generation (DG) has set up a challenge for energy management and consequently for the monitoring and assessment of power quality (PQ). Besides, there are new types of disturbances owing to the uncontrolled connections of non-linear loads. The stochastic behaviour triggers the need for new holistic indicators which also deal with big data of PQ in terms of compression and scalability so as to extract the useful information regarding different network states and the prevailing PQ disturbances for future risk assessment and energy management systems. Permanent and continuous monitoring would guarantee the report to claim for damages and to assess the risk of PQ distortions. In this context, we propose a measurement method that postulates the use of two-dimensional (2D) diagrams based on higher-order statistics (HOSs) and a previous voltage quality index that assesses the voltage supply waveform in a continous monitoring campaign. Being suitable for both PQ and reliability applications, the results conclude that the inclusion of HOS measurements in the industrial metrological reports helps characterize the deviations of the voltage supply waveform, extracting the individual customers' pattern fingerprint, and compressing the data from both time and spatial aspects. The method allows a continuous and robust performance needed in the SG framework. Consequently, the method can be used by an average consumer as a probabilistic method to assess the risk of PQ deviations in site characterization.This work was supported by the Spanish Ministry of Science and Innovation (Statal Agency for Research), and the EU (AEI/FEDER/UE) via project PID2019-108953RB-C21 Strategies for Aggregated Generation of Photovoltaic Plants: Energy and Meteorological Operational Data (SAGPVEMOD), and the precedent TEC2016-77632-C3-3-R

A survey of machine learning techniques applied to self organizing cellular networks

In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future

Current Status and Future Trends of Power Quality Analysis

In this article, a systematic literature review of 153 articles on power quality analysis in PV systems published in the last 20 years is presented. This provides readers with an overview on PQ trends in several fields related to instrumental techniques that are being used in the smart grid to visualize the quality of the energy, establishing a solid literature base from which to start future research. A preliminary appreciation allows us to intuit that higher-order statistics are not implemented in measurement equipment and that traditional instrumentation is still used for the performance of measurement campaigns, not yielding the expected results since the information processed does not come from an electrical network from 20 years ago. Instead, current networks contain numerous coupled load effects; thus, new disturbances are not simple; they are usually complex events, the sum of several types of disturbances. Likewise, depending on the type of installation, the objective of the PQ analysis changes, either by detecting certain events or simply focusing on seeing the state of the network

Intelligent Methods for Characterization of Electrical Power Quality Signals using Higher Order Statistical Features

This paper considers a few important techniques classification for to identify several power quality disturbances. For this purpose, a process based in HOS has been realized to extract features that help in classification. In this stage the geometrical pattern established via higher-order statistical measurements is obtained, and this pattern is function of the amplitudes and frequencies of the power quality disturbances associated to the 50-Hz power-line. Once the features are managed will be segmented to form training and test sets and them will be applied in the statistical methods used to perform automatic classification of PQ disturbances. The best technique of those compared is selected according to correlation and mistake rates

Piercing Through Highly Obscured and Compton-thick AGNs in the Chandra Deep Fields: I. X-ray Spectral and Long-term Variability Analyses

We present a detailed X-ray spectral analysis of 1152 AGNs selected in the Chandra Deep Fields (CDFs), in order to identify highly obscured AGNs ($N_{\rm H} > 10^{23}\ \rm cm^{-2}$). By fitting spectra with physical models, 436 (38%) sources with $L_{\rm X} > 10^{42}\ \rm erg\ s^{-1}$ are confirmed to be highly obscured, including 102 Compton-thick (CT) candidates. We propose a new hardness-ratio measure of the obscuration level which can be used to select highly obscured AGN candidates. The completeness and accuracy of applying this method to our AGNs are 88% and 80%, respectively. The observed logN-logS relation favors cosmic X-ray background models that predict moderate (i.e., between optimistic and pessimistic) CT number counts. 19% (6/31) of our highly obscured AGNs that have optical classifications are labeled as broad-line AGNs, suggesting that, at least for part of the AGN population, the heavy X-ray obscuration is largely a line-of-sight effect, i.e., some high-column-density clouds on various scales (but not necessarily a dust-enshrouded torus) along our sightline may obscure the compact X-ray emitter. After correcting for several observational biases, we obtain the intrinsic NH distribution and its evolution. The CT-to-highly-obscured fraction is roughly 52% and is consistent with no evident redshift evolution. We also perform long-term (~17 years in the observed frame) variability analyses for 31 sources with the largest number of counts available. Among them, 17 sources show flux variabilities: 31% (5/17) are caused by the change of NH, 53% (9/17) are caused by the intrinsic luminosity variability, 6% (1/17) are driven by both effects, and 2 are not classified due to large spectral fitting errors.Comment: 32 pages, 21 figures, 9 tables, accepted for publication in Ap

An Embedded System in Smart Inverters for Power Quality and Safety Functionality

The electricity sector is undergoing an evolution that demands the development of a network model with a high level of intelligence, known as a Smart Grid. One of the factors accelerating these changes is the development and implementation of renewable energy. In particular, increased photovoltaic generation can affect the network’s stability. One line of action is to provide inverters with a management capacity that enables them to act upon the grid in order to compensate for these problems. This paper describes the design and development of a prototype embedded system able to integrate with a photovoltaic inverter and provide it with multifunctional ability in order to analyze power quality and operate with protection. The most important subsystems of this prototype are described, indicating their operating fundamentals. This prototype has been tested with class A protocols according to IEC 61000-4-30 and IEC 62586-2. Tests have also been carried out to validate the response time in generating orders and alarm signals for protections. The highlights of these experimental results are discussed. Some descriptive aspects of the integration of the prototype in an experimental smart inverter are also commented upon