Detection of Obstructive Sleep Apnoea Using Features Extracted from Segmented Time-Series ECG Signals Using a One Dimensional Convolutional Neural Network

no code implementations • 3 Feb 2020 • Steven Thompson, Paul Fergus, Carl Chalmers, Denis Reilly

Evaluation of the model is performed using a set of standard metrics which show the proposed model achieves high classification results in both training and validation using our windowing strategy, particularly W=500 (Sensitivity 0. 9705, Specificity 0. 9725, F1 Score 0. 9717, Kappa Score 0. 9430, Log Loss 0. 0836, ROCAUC 0. 9945).

Specificity Time Series +1

SAERMA: Stacked Autoencoder Rule Mining Algorithm for the Interpretation of Epistatic Interactions in GWAS for Extreme Obesity

no code implementations • 27 Aug 2019 • Casimiro Aday Curbelo Montañez, Paul Fergus, Carl Chalmers, Nurul Ahamed Hassain Malim, Basma Abdulaimma, Denis Reilly, Francesco Falciani

One of the most important challenges in the analysis of high-throughput genetic data is the development of efficient computational methods to identify statistically significant Single Nucleotide Polymorphisms (SNPs).

General Classification

Modelling Segmented Cardiotocography Time-Series Signals Using One-Dimensional Convolutional Neural Networks for the Early Detection of Abnormal Birth Outcomes

no code implementations • 6 Aug 2019 • Paul Fergus, Carl Chalmers, Casimiro Curbelo Montanez, Denis Reilly, Paulo Lisboa, Beth Pineles

Machine learning models, trained with FIGO and other user derived features extracted from CTG traces, have been shown to increase positive predictive capacity and minimise variability.

BIG-bench Machine Learning Time Series +1