An E-Health Solution for Automatic Sleep Classification according to Rechtschaffen and Kales: Validation Study of the Somnolyzer 24 × 7 Utilizing the Siesta Database

By P. Anderer, G. Gruber, S. Parapatics, Michael Woertz, Tatiana Miazhynskaia, G. Klösch, B. Saletu, J. Zeitlhofer, M. Barbanoj, H. Danker-Hopfe and 8 more. Neuropsychobiology, 2005

303 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Physiology

View Paper: https://www.semanticscholar.org/paper/e6f2970b37a7fe02db90e571cc1d062fbbafcfcb

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

1. Peng (Paul) Wen
2. Yan Li
3. Guohun Zhu

In Physiology

1. Peng (Paul) Wen
2. Yan Li
3. Guohun Zhu

Citations in the corpus

• Analysis and Classification of Sleep Stages Based on Difference Visibility Graphs From a Single-Channel EEG Signal (2014, methodology): “[4] applied EEG, EOG and EMG features to obtain 80% accuracy six-state sleep classification.”
• Automated sleep stage identification system based on time-frequency analysis of a single EEG channel and random forest classifier (2012, background): “Other works were done in the literature with reported accuracies around 80% [37–40].”
• ISRUC-Sleep: A comprehensive public dataset for sleep researchers (2016, background): “[34] P.”
• Automatic Stage Scoring of Single-Channel Sleep EEG by Using Multiscale Entropy and Autoregressive Models (2012, methodology): “In classification, many methods have been proposed, such as linear discriminant analysis (LDA) [15], artificial neural network [7], fuzzy system [10], and decision tree [16].”