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Detection of Obstructive Sleep Apnoea Using Features Extracted from Segmented Time-Series ECG Signals Using a One Dimensional Convolutional Neural Network
The study in this paper presents a one-dimensional convolutional neural ...
02/03/2020 ∙ by Steven Thompson, et al. ∙
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SAERMA: Stacked Autoencoder Rule Mining Algorithm for the Interpretation of Epistatic Interactions in GWAS for Extreme Obesity
One of the most important challenges in the analysis of high-throughput ...
08/27/2019 ∙ by Casimiro Aday Curbelo Montañez, et al. ∙
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Modelling Segmented Cardiotocography Time-Series Signals Using One-Dimensional Convolutional Neural Networks for the Early Detection of Abnormal Birth Outcomes
Gynaecologists and obstetricians visually interpret cardiotocography (CT...
08/06/2019 ∙ by Paul Fergus, et al. ∙
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Extracting Epistatic Interactions in Type 2 Diabetes Genome-Wide Data Using Stacked Autoencoder
2 Diabetes is a leading worldwide public health concern, and its increas...
08/28/2018 ∙ by Basma Abdulaimma, et al. ∙
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Collaborative Pressure Ulcer Prevention: An Automated Skin Damage and Pressure Ulcer Assessment Tool for Nursing Professionals, Patients, Family Members and Carers
This paper describes the Pressure Ulcers Online Website, which is a firs...
08/17/2018 ∙ by Paul Fergus, et al. ∙
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Deep Learning Classification of Polygenic Obesity using Genome Wide Association Study SNPs
In this paper, association results from genome-wide association studies ...
04/09/2018 ∙ by Casimiro Adays Curbelo Montañez, et al. ∙
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Utilising Deep Learning and Genome Wide Association Studies for Epistatic-Driven Preterm Birth Classification in African-American Women
Genome Wide Association Studies (GWAS) are used to identify statisticall...
01/06/2018 ∙ by Paul Fergus, et al. ∙
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