Human sleep is cyclical with a period of approximately 90 minutes, implying long temporal dependency in the sleep data. Yet, exploring this long-term dependency when developing sleep staging models has remained untouched. In this work, we show that while encoding the logic of a whole sleep cycle is crucial to improve sleep staging performance, the sequential modelling approach in existing state-of-the-art deep learning models are inefficient for that purpose. We thus introduce a method for efficient long sequence modelling and propose a new deep learning model, L-SeqSleepNet, which takes into account whole-cycle sleep information for sleep staging. Evaluating L-SeqSleepNet on four distinct databases of various sizes, we demonstrate state-of-the-art performance obtained by the model over three different EEG setups, including scalp EEG in conventional Polysomnography (PSG), in-ear EEG, and around-the-ear EEG (cEEGrid), even with a single EEG channel input. Our analyses also show that L-SeqSleepNet is able to alleviate the predominance of N2 sleep (the major class in terms of classification) to bring down errors in other sleep stages. Moreover the network becomes much more robust, meaning that for all subjects where the baseline method had exceptionally poor performance, their performance are improved significantly. Finally, the computation time only grows at a sub-linear rate when the sequence length increases.

This special issue covers relevant contemporary challenges in the field of biomedical signal processing and possibilities for future technological development.

Atrial fibrillation (AF) is the most common cardiac arrhythmia in humans, with an estimated lifetime prevalence of 25%. It is characterized by irregular and disorganized electrical activation of the atria. In the past decade, catheter ablation, i.e., focally burning targeted areas of cardiac muscle, has emerged as a potentially curative therapy for AF. Accompanying this development there has been an increasing interest in quantitative intraprocedural signal analysis to guide the selection of ablation targets. In this review, we provide an overview of quantitative signal processing approaches for mapping and ablation of AF.

This article tells the story of Edward Thomas (Ted) Both, one of Australia's lesser known inventors, who made pioneering contributions to biomedical engineering.