2 citations · 2 across the 4 of their papers we have counts for
4 papers
Epileptic seizure forecasting with long short-term memory (LSTM) neural networks
Daniel E. Payne, Jordan D. Chambers, Anthony Burkitt +4
Objective: Forecasting epileptic seizures can reduce uncertainty for patients and allow preventative actions. While many models can predict the occurrence of seizures from features…
Path Signatures for Seizure Forecasting
Jonas F. Haderlein, Andre D. H. Peterson, Parvin Zarei Eskikand +4
Predicting future system behaviour from past observed behaviour (time series) is fundamental to science and engineering. In computational neuroscience, the prediction of future epi…
Autoregressive models for biomedical signal processing
Jonas F. Haderlein, Andre D. H. Peterson, Anthony N. Burkitt +2
Autoregressive models are ubiquitous tools for the analysis of time series in many domains such as computational neuroscience and biomedical engineering. In these domains, data is,…
On the benefit of overparameterisation in state reconstruction: An empirical study of the nonlinear case
Jonas F. Haderlein, Andre D. H. Peterson, Parvin Zarei Eskikand +3
The empirical success of machine learning models with many more parameters than measurements has generated an interest in the theory of overparameterisation, i.e., underdetermined…