Interpretable ECG beat embedding using disentangled variational auto-encoders

Electrocardiogram signals are often used in medicine. An important aspect of analyzing this data is identifying and classifying the type of beat. This classification is often done through an automated algorithm. Recent advancements in neural networks and deep learning have led to high classification accuracy. However, adoption of neural network models into clinical practice is limited due to the black-box nature of the classification method. In this work, the use of variational auto encoders to learn human-interpretable encodings for the beat types is analyzed. It is demonstrated that using this method, an interpretable and explainable representation of normal and paced beats can be achieved with neural networks

Sensitivity analysis of expensive black-box systems using metamodeling

Simulations are becoming ever more common as a tool for designing complex products. Sensitivity analysis techniques can be applied to these simulations to gain insight, or to reduce the complexity of the problem at hand. However, these simulators are often expensive to evaluate and sensitivity analysis typically requires a large amount of evaluations. Metamodeling has been successfully applied in the past to reduce the amount of required evaluations for design tasks such as optimization and design space exploration. In this paper, we propose a novel sensitivity analysis algorithm for variance and derivative based indices using sequential sampling and metamodeling. Several stopping criteria are proposed and investigated to keep the total number of evaluations minimal. The results show that both variance and derivative based techniques can be accurately computed with a minimal amount of evaluations using fast metamodels and FLOLA-Voronoi or density sequential sampling algorithms.Comment: proceedings of winter simulation conference 201

Sensor Fusion using Backward Shortcut Connections for Sleep Apnea Detection in Multi-Modal Data

Sleep apnea is a common respiratory disorder characterized by breathing pauses during the night. Consequences of untreated sleep apnea can be severe. Still, many people remain undiagnosed due to shortages of hospital beds and trained sleep technicians. To assist in the diagnosis process, automated detection methods are being developed. Recent works have demonstrated that deep learning models can extract useful information from raw respiratory data and that such models can be used as a robust sleep apnea detector. However, trained sleep technicians take into account multiple sensor signals when annotating sleep recordings instead of relying on a single respiratory estimate. To improve the predictive performance and reliability of the models, early and late sensor fusion methods are explored in this work. In addition, a novel late sensor fusion method is proposed which uses backward shortcut connections to improve the learning of the first stages of the models. The performance of these fusion methods is analyzed using CNN as well as LSTM deep learning base-models. The results demonstrate a significant and consistent improvement in predictive performance over the single sensor methods and over the other explored sensor fusion methods, by using the proposed sensor fusion method with backward shortcut connections.Comment: Paper presented at ML4H (Machine Learning for Health) workshop at NeurIPS 2019. https://ml4health.github.io/2019

Automated sleep apnea detection in raw respiratory signals using long short-term memory neural networks

Sleep apnea is one of the most common sleep disorders and the consequences of undiagnosed sleep apnea can be very severe, ranging from increased blood pressure to heart failure. However, many people are often unaware of their condition. The gold standard for diagnosing sleep apnea is an overnight polysomnography in a dedicated sleep laboratory. Yet, these tests are expensive and beds are limited as trained staff needs to analyze the entire recording. An automated detection method would allow a faster diagnosis and more patients to be analyzed. Most algorithms for automated sleep apnea detection use a set of human-engineered features, potentially missing important sleep apnea markers. In this paper, we present an algorithm based on state-of-the-art deep learning models for automatically extracting features and detecting sleep apnea events in respiratory signals. The algorithm is evaluated on the Sleep-Heart-Health-Study-1 dataset and provides per-epoch sensitivity and specificity scores comparable to the state of the art. Furthermore, when these predictions are mapped to the apnea-hypopnea index, a considerable improvement in per-patient scoring is achieved over conventional methods. This paper presents a powerful aid for trained staff to quickly diagnose sleep apnea

Sequential sensitivity analysis of expensive black-box simulators with metamodelling

Simulators are an essential tool in the design and analysis of complex systems. For many tasks in engineering, highly accurate simulators are used instead of expensive real-life experiments. As accuracy increases, these simulators are becoming computationally more expensive to evaluate. Sensitivity analysis provides information on the importance of the inputs with regard to the outputs. This does not only provide critical information about the workings of the system but also allows to discard inputs with low impact on the outputs. However, sensitivity analysis is an expensive process in terms of number of evaluations. Hence, metamodelling techniques are used to reduce the computational burden. In this paper we present, discuss and evaluate a novel algorithm for sequential variance-based and derivative-based sensitivity analysis of expensive black-box simulators using metamodelling. Two new stopping criteria are proposed on top of the traditional model error based stopping criteria. Extensive testing on benchmark problems and engineering use cases shows the sensitivity indices can be efficiently and accurately computed with a limited number of simulator evaluations. (C) 2018 Elsevier Inc. All rights reserved