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An approach for automatic sleep stage scoring and apnea-hypopnea detection

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Abstract

In this article we present an application of data mining to the medical domain sleep research, an approach for automatic sleep stage scoring and apnea-hypopnea detection. By several combined techniques (Fourier and wavelet transform, derivative dynamic time warping, and waveform recognition), our approach extracts meaningful features (frequencies and special patterns like k-complexes and sleep spindles) from physiological recordings containing EEG, ECG, EOG and EMG data. Based on these pieces of information, an ensemble of decision trees is constructed using the principle of bagging, which classifies sleep epochs in their sleep stages according to the rules by Rechtschaffen and Kales and annotates occurrences of apnea-hypopnea (total or partial cessation of respiration). After that, casebased reasoning is applied in order to improve quality. We tested and evaluated our approach on several large public databases from PhysioBank, which showed an overall accuracy of 95.2% for sleep stage scoring and 94.5% for classifying minutes as apneic or non-apneic.

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Authors and Affiliations

  1. Institute of Computer Science, Heinrich Heine University, 40225, Düsseldorf, Germany

    Tim Schlüter & Stefan Conrad

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  1. Tim Schlüter
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  2. Stefan Conrad
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Correspondence to Tim Schlüter.

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Tim Schlüter is a research associate at the Heinrich Heine University in Düsseldorf, Germany. Currently he is nearing the completion of his doctoral thesis for his PhD in Computer Science. Previously, he studied mathematics specializing in theoretical computer science at the Heinrich Heine University, where he also received his diploma. His research interests are knowledge discovery in databases, and especially temporal data mining and time series analysis. Tim Schlüter is member of ACM and the IEEE Computer Society.

Stefan Conrad is a professor of databases and information systems at Heinrich Heine University in Düsseldorf, Germany. He studied computer science at the Technical University of Braunschweig where he also received his PhD. After that, he was first assistant professor at the University of Magdeburg and then associate professor at Ludwig-Maximilians-University Munich. In 2002 he moved to Düsseldorf. His major research topics are the integration of heterogeneous databases and information systems, multimedia databases, and knowledge discovery in databases. Prof. Conrad is a member of several national and international organizations including ACM, IEEE Computer Society, and GI-German Society for Computer Science.

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Schlüter, T., Conrad, S. An approach for automatic sleep stage scoring and apnea-hypopnea detection. Front. Comput. Sci. 6, 230–241 (2012). https://doi.org/10.1007/s11704-012-2872-6

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