Transfer learning in ECG classification from human to horse using a novel parallel neural network architecture

Automatic or semi-automatic analysis of the equine electrocardiogram (eECG) is currently not possible because human or small animal ECG analysis software is unreliable due to a different ECG morphology in horses resulting from a different cardiac innervation. Both filtering, beat detection to classification for eECGs are currently poorly or not described in the literature. There are also no public databases available for eECGs as is the case for human ECGs. In this paper we propose the use of wavelet transforms for both filtering and QRS detection in eECGs. In addition, we propose a novel robust deep neural network using a parallel convolutional neural network architecture for ECG beat classification. The network was trained and tested using both the MIT-BIH arrhythmia and an own made eECG dataset with 26.440 beats on 4 classes: normal, premature ventricular contraction, premature atrial contraction and noise. The network was optimized using a genetic algorithm and an accuracy of 97.7% and 92.6% was achieved for the MIT-BIH and eECG database respectively. Afterwards, transfer learning from the MIT-BIH dataset to the eECG database was applied after which the average accuracy, recall, positive predictive value and F1 score of the network increased with an accuracy of 97.1%

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

Neural Expectation Maximization

Many real world tasks such as reasoning and physical interaction require identification and manipulation of conceptual entities. A first step towards solving these tasks is the automated discovery of distributed symbol-like representations. In this paper, we explicitly formalize this problem as inference in a spatial mixture model where each component is parametrized by a neural network. Based on the Expectation Maximization framework we then derive a differentiable clustering method that simultaneously learns how to group and represent individual entities. We evaluate our method on the (sequential) perceptual grouping task and find that it is able to accurately recover the constituent objects. We demonstrate that the learned representations are useful for next-step prediction.Comment: Accepted to NIPS 201

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

The impact of elevation of total bilirubin level and etiology of the liver disease on serum N-glycosylation patterns in mice and men

The GlycoFibroTest and GlycoCirrhoTest are noninvasive alternatives for liver biopsy that can be used as a follow-up tool for fibrosis patients and to diagnose cirrhotic patients, respectively. These tests are based on the altered N-glycosylation of total serum protein. Our aim was to investigate the impact of etiology on the alteration of N-glycosylation and whether other characteristics of liver patients could have an influence on N-glycosylation. In human liver patients, no specific alteration could be found to make a distinction according to etiological factor, although alcoholic patients had a significant higher mean value for the GlycoCirrhoTest. Undergalactosylation did not show a significantly different quantitative alteration in the cirrhotic and non-cirrhotic population of all etiologies. Importantly, patients with an elevation of total bilirubin level (>2 mg/dl) had a strong increase of glycans modified with alpha 1-6 fucose. The fucosylation index was therefore significantly higher in fibrosis/cirrhosis and hepatocellular carcinoma patients with elevated total bilirubin levels irrespective of etiology. Furthermore, in a multiple linear regression analysis, only markers for cholestasis significantly correlated with the fucosylation index. In mouse models of chronic liver disease, the fucosylation index was uniquely significantly increased in mice that were induced with a common bile duct ligation. Mice that were chronically injected with CCl4 did not show this increase. Apart from this difference, common changes characteristic to fibrosis development in mice were observed. Finally, mice induced with a partial portal vein ligation did not show biological relevant changes indicating that portal hypertension does not contribute to the alteration of N-glycosylation