Using time causal quantifiers to characterize sleep stages

Automated Recognition of Sleep Stages Using Electroencephalograms

The assessment of different sleep stages and their disorders in diseases is an important part of telematic medicine. With an electroencephalogram, the different stages of sleep can be monitored and classified with respect to brain activity. By means of modern data management such as the patient monitor ixTrend, for example, the data can be recorded for long sleep phases and evaluated by a compu...

Classification and Verification of Handwritten Signatures with Time Causal Information Theory Quantifiers

We present a new approach for handwritten signature classification and verification based on descriptors stemming from time causal information theory. The proposal uses the Shannon entropy, the statistical complexity, and the Fisher information evaluated over the Bandt and Pompe symbolization of the horizontal and vertical coordinates of signatures. These six features are easy and fast to compu...

The Stages of Sleep

Using a quadratic parameter sinusoid model to characterize the structure of EEG sleep spindles

Sleep spindles are essentially non-stationary signals that display time and frequency-varying characteristics within their envelope, which makes it difficult to accurately identify its instantaneous frequency and amplitude. To allow a better parameterization of the structure of spindle, we propose modeling spindles using a Quadratic Parameter Sinusoid (QPS). The QPS is well suited to model spin...

The effects of sleep stages and time of night on NREM sleep ERPs.

Event-related potential (ERP) is one of the best techniques for studying information processing during sleep because it does not require behavioral responses or consciousness awareness. Several ERP components have been identified during non-rapid eye movement (NREM) sleep, but the associated underlying processes of these waveforms remain unclear. The present study examines the effect of sleep s...