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

Urinary chitinase 3-like protein 1 for early diagnosis of acute kidney injury : a prospective cohort study in adult critically ill patients

Background: Acute kidney injury (AKI) occurs frequently and adversely affects patient and kidney outcomes, especially when its severity increases from stage 1 to stages 2 or 3. Early interventions may counteract such deterioration, but this requires early detection. Our aim was to evaluate whether the novel renal damage biomarker urinary chitinase 3-like protein 1 (UCHI3L1) can detect AKI stage >= 2 more early than serum creatinine and urine output, using the respective Kidney Disease vertical bar Improving Global Outcomes (KDIGO) criteria for definition and classification of AKI, and compare this to urinary neutrophil gelatinase-associated lipocalin (UNGAL). Methods: This was a translational single-center, prospective cohort study at the 22-bed surgical and 14-bed medical intensive care units (ICU) of Ghent University Hospital. We enrolled 181 severely ill adult patients who did not yet have AKI stage >= 2 based on the KDIGO criteria at time of enrollment. The concentration of creatinine (serum, urine) and CHI3L1 (serum, urine) was measured at least daily, and urine output hourly, in the period from enrollment till ICU discharge with a maximum of 7 ICU-days. The concentration of UNGAL was measured at enrollment. The primary endpoint was the development of AKI stage >= 2 within 12 h after enrollment. Results: After enrollment, 21 (12 %) patients developed AKI stage >= 2 within the next 7 days, with 6 (3 %) of them reaching this condition within the first 12 h. The enrollment concentration of UCHI3L1 predicted the occurrence of AKI stage >= 2 within the next 12 h with a good AUC-ROC of 0.792 (95 % CI: 0.726-0.849). This performance was similar to that of UNGAL (AUC-ROC of 0.748 (95 % CI: 0.678-0.810)). Also, the samples collected in the 24-h time frame preceding diagnosis of the 1st episode of AKI stage >= 2 had a 2.0 times higher (95 % CI: 1.3-3.1) estimated marginal mean of UCHI3L1 than controls. We further found that increasing UCHI3L1 concentrations were associated with increasing AKI severity. Conclusions: In this pilot study we found that UCHI3L1 was a good biomarker for prediction of AKI stage >= 2 in adult ICU patients

Forecasting People Trajectories and Head Poses by Jointly Reasoning on Tracklets and Vislets

In this work, we explore the correlation between people trajectories and their head orientations. We argue that people trajectory and head pose forecasting can be modelled as a joint problem. Recent approaches on trajectory forecasting leverage short-term trajectories (aka tracklets) of pedestrians to predict their future paths. In addition, sociological cues, such as expected destination or pedestrian interaction, are often combined with tracklets. In this paper, we propose MiXing-LSTM (MX-LSTM) to capture the interplay between positions and head orientations (vislets) thanks to a joint unconstrained optimization of full covariance matrices during the LSTM backpropagation. We additionally exploit the head orientations as a proxy for the visual attention, when modeling social interactions. MX-LSTM predicts future pedestrians location and head pose, increasing the standard capabilities of the current approaches on long-term trajectory forecasting. Compared to the state-of-the-art, our approach shows better performances on an extensive set of public benchmarks. MX-LSTM is particularly effective when people move slowly, i.e. the most challenging scenario for all other models. The proposed approach also allows for accurate predictions on a longer time horizon.Comment: Accepted at IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2019. arXiv admin note: text overlap with arXiv:1805.0065

Short-term memory for pictures seen once or twice

The present study is concerned with the effects of exposure time, repetition, spacing and lag on old/new recognition memory for generic visual scenes presented in a RSVP paradigm. Early memory studies with verbal material found that knowledge of total exposure time at study is sufficient to accurately predict memory performance at test (the Total Time Hypothesis), irrespective of number of repetitions, spacing or lag. However, other studies have disputed such simple dependence of memory strength on total study time, demonstrating super-additive facilitatory effects of spacing and lag, as well as inhibitory effects, such as the Ranschburg effect, Repetition Blindness and the Attentional Blink. In the experimental conditions of the present study we find no evidence of either facilitatory or inhibitory effects: recognition memory for pictures in RSVP supports the Total Time Hypothesis. The data are consistent with an Unequal-Variance Signal Detection Theory model of memory that assumes the average strength and the variance of the familiarity of pictures both increase with total study time. The main conclusion is that the growth of visual scene familiarity with temporal exposure and repetition is a stochastically independent process

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig