4 citations · 5 across the 5 of their papers we have counts for
6 papers
Enabling Unsupervised Training of Deep EEG Denoisers With Intelligent Partitioning
Qiyu Rao, Haozhe Tian, Homayoun Hamedmoghadam +1
Denoising electroencephalogram (EEG) is an inherently challenging task, since neural activity is not only subtle but also inseparable from spectrally overlapping noise artifacts. T…
Synthesizing Epileptic Seizures: Gaussian Processes for EEG Generation
Nina Moutonnet, Joshua Corneck, Felipe Tobar +1
Reliable seizure detection from electroencephalography (EEG) time series is a high-priority clinical goal, yet the acquisition cost and scarcity of labeled EEG data limit the perfo…
Machine Intelligence on the Edge: Interpretable Cardiac Pattern Localisation Using Reinforcement Learning
Haozhe Tian, Qiyu Rao, Nina Moutonnet +2
Matched filters are widely used to localise signal patterns due to their high efficiency and interpretability. However, their effectiveness deteriorates for low signal-to-noise rat…
Augmentation of EEG and ECG Time Series for Deep Learning Applications: Integrating Changepoint Detection into the iAAFT Surrogates
Nina Moutonnet, Gregory Scott, Danilo P. Mandic
The performance of deep learning methods critically depends on the quality and quantity of the available training data. This is especially the case for physiological time series, w…
A Systematic Review of EEG-based Machine Intelligence Algorithms for Depression Diagnosis, and Monitoring
Amir Nassibi, Christos Papavassiliou, Ildar Rakhmatulin +2
Depression disorder is a serious health condition that has affected the lives of millions of people around the world. Diagnosis of depression is a challenging practice that relies…
Clinical translation of machine learning algorithms for seizure detection in scalp electroencephalography: systematic review
Nina Moutonnet, Steven White, Benjamin P Campbell +5
Machine learning algorithms for seizure detection have shown considerable diagnostic potential, with recent reported accuracies reaching 100%. Yet, only few published algorithms ha…