Meet Spinky: An Open-Source Spindle and K-Complex Detection Toolbox Validated on the Open-Access Montreal Archive of Sleep Studies (MASS).

Sleep spindles and K-complexes are among the most prominent micro-events observed in electroencephalographic (EEG) recordings during sleep. These EEG microstructures are thought to be hallmarks of sleep-related cognitive processes. Although tedious and time-consuming, their identification and quantification is important for sleep studies in both healthy subjects and patients with sleep disorders.

A Robustness Comparison of Two Algorithms Used for EEG Spike Detection.

Spikes and sharp waves recorded on scalp EEG may play an important role in identifying the epileptogenic network as well as in understanding the central nervous system. Therefore, several automatic and semi-automatic methods have been implemented to detect these two neural transients. A consistent gold standard associated with a high degree of agreement among neuroscientists is required to measure relevant performance of different methods.

Learning machines and sleeping brains: Automatic sleep stage classification using decision-tree multi-class support vector machines.

Background: Sleep staging is a critical step in a range of electrophysiological signal processing pipelines used in clinical routine as well as in sleep research. Although the results currently achievable with automatic sleep staging methods are promising, there is need for improvement, especially given the time-consuming and tedious nature of visual sleep scoring.

New Method: Here we propose a sleep staging framework that consists of a multi-class support vector machine (SVM) classification based on a decision tree approach.

A reliable approach to distinguish between transient with and without HFOs using TQWT and MCA.

Recent studies have reported that discrete high frequency oscillations (HFOs) in the range of 80-500Hz may serve as promising biomarkers of the seizure focus in humans. Visual scoring of HFOs is tiring, time consuming, highly subjective and requires a great deal of mental concentration. Due to the recent explosion of HFOs research, development of a robust automated detector is expected to play a vital role in studying HFOs and their relationship to epileptogenesis.

Wavelet filtering based on Mellin transform dedicated to cochlear prostheses.

Current speech processing strategies for cochlear prosthesis system use filter bank structure in order to extract speech signal's energies relatively to the multiple frequency bands which are associated to the dedicated stimulation electrodes. This research concerned wavelet filtering module based on Mellin transform and dedicated to one cochlear speech processing strategy. Our bank filtering module was composed of twenty one filters designed according to Morlet mother wavelet and distributed along the Munich critical bands.